Product Description
This text provides an introductory-level overview of artificial intelligence (AI). It features clear presentation of principles integrated with short, workable programs which are designed to help students to learn by experimentation and to develop an intuitive understanding of the subject. The book features: expression of AI theory in programs written with common LISP, the established standard for the field; new chapters on common sense reasoning and neural networks; new sections on global variables, LISP structures, LISP association lists, iterative deepening, constructing decision trees, genetic algorithms and embedded AI; expanded coverage of expert systems; updated programming style in example programs, plus many new example programs; and coverage of additional Common LISP features. ... Read more

Customer Reviews (3)

A must
The 2nd edition is a real complete book on AI elements.
The book is for undergraduate or first year graduate, and
it is not required a full background in calculs or algebra.
All chapters require a pratice works on lisp example, in order
to be most effective.
Tanimoto written lisp examples prior to the language standardization,
so source codes could be a little re-worked.
Although all text example are based on lisp, it would be easy
to applay theory to other programming language as C/C++, tcl/tk, etc.
Finnaly, the book is "a must" for people real interested on AI.

VERY GOOD BOOK, except...
I learned to programming Common Lisp using Tanimoto's book (in conjunction with Winston Horn's "Lisp"). The book's writing is superb, and the examples are very well thought out and implemented.

One word of caution: the book was written before the complete standardization of Common Lisp. So some of the functions, such as those specific to I/O and FEXPR will not work on current Common Lisp implementations (such as GCL). But all of these functions can be worked around easily.

I'll still give this book a five star. The book is particularly good for self-study. So I recommend it to any AI enthusiast.

A very broad treatment of AI Fundamentals
Tanimoto provides a very broad treatment of AI techniques.As such dicussions are often brief.There is an outstanding section on Computer vision.Knowledge representation is also well covered.The author presents highly idiomatic examples of Lisp code.This book would be ideal for anyone not familiar with AI techniques who wants to do AI research and/or development. ... Read more

Product Description
Nearly all AI books are scholarly and theoretical. Most concentrate on basic research questions and issues. This book avoids those issues and controversies and instead deals with where we are now in terms of practical applications and real applied potential. The book represents the first collection of applications selected on the basis of how successful they have been. Areas include, manufacturing and design, computer assisted instruction, national defense, robotics, airline industry, software engineers, etc. ... Read more

Product Description
This is an eagerly awaited revision of the single bestselling introduction to Artificial Intelligence ever published. It retains the best features of the earlier works including superior readability, currency, and excellence in the selection of the examples.Amazon.com Review
This book is one of the oldest and most popular introductionsto artificial intelligence.An accomplished artificial intelligence(AI) scientist, Winston heads MIT's Artificial IntelligenceLaboratory, and his hands-on AI research experience lends authority towhat he writes. Winston provides detailed pseudo-code for most of thealgorithms discussed, so you will be able to implement and test thealgorithms immediately. The book contains exercises to test yourknowledge of the subject and helpful introductions and summaries toguide you through the material. ... Read more

Customer Reviews (10)

Rethinking Fraud Aalysis
My business used this book to help us rethink our analysis of credit and prepaid card fraud. While the material itself has been around for some time, it helped us develop new methods of detecting potential fraud.

Nauseating
In a phrase: as nauseating as the "artwork" which besmirches its cover.This book is definitely not worth the price.Donate the money instead to your city's homeless instead!You will learn as much about AI by doing so and will actually contribute something to the world.Of course, the cover makes a great prank at cocktail parties.Place it under someone's drink and it will look like the beverage has been spilled.

Winston's book is not only disorganized, but pretentious.He writes about the mind as if he has the authority of a philosopher of mind, when, in fact, he's just a programmer.Winston and his books will go down in history with the works of others, such as Doug Lenat, who made their fame primarily by doing something very easy before anyone else got around to doing it.

Real AI is yet to come.

Can't get worse
This book is bad (period). It is very incoherent and ill-organized. The examples are vague and serve anything but support the material. Very theoritical with hardly any real life applications. Lacking in modern AI topics/game design.

Miserable AI book - avoid at all costs
Winston's book is really terrible.I mean truly repellently, malignantly bad."Can it really be as bad as all that?" you wonder.Yes!!It's that bad!!For starters, the book is poorly organized.Topics that logically belong together are often several chapters apart.There is no overall structure to the book.It seems like a collection of topics in AI that were hastily assembled without concern for thematic organization or flow.For example, the forward and backward chaining algorithms are presented in a chapter (Ch. 7) on rule-based systems, but are not even mentioned in the chapter (Ch. 13) on logic!Perceptron training is presented AFTER backpropagation!Contrast this with the much better book by Russell and Norvig, which uses the theme of intelligent agents as a continuing motivation throughout, and which groups related topics into logically arranged chapters.

The examples in Winston are atrocious.The main example in the backpropagation chapter is some kind of classification network with a bizarre topography.This example is so trivial and weird that it totally fails to illustrate the strengths of backpropagation.The explanations of generalization and overfitting in backprop training are awful.

The only chapter of this book that is not an unmitigated pedagogical disaster is the chapter on genetic algorithms, although better introductions exist (e.g. Melanie Mitchell).

A further annoyance is the placement of all the exercises at the end of the book instead of the end of the chapters to which they correspond.

Avoid this book.It is truly horrible, and vastly superior books on AI are readily available at comparable prices.

Very useful and well written; an industry perspective:
Suppose you are, like me, a software engineer who never actually studied CS beyond junior level undergraduate 'data structures'... and now you have to work on something involving complicated pattern matching... this is howto do it: buy this book and Sipser's on the Theory of Computation.Afterdigesting them (which is easy if you're as good with logical mathematics asthe typical software engineer), you should be able to read currentliterature in either field, and will have a deep, fundamental understandingof how to best solve whatever problem you're working on.That's whatworked for me, anyway.An excellent book, as is Sipser's. ... Read more

Product Description
"Ming Design II is a welcome update of its predecessor, itself auseful compendium on the philosophy of cognitive science. This newvolume retains the intellectual foundations, and some discussions ofclassical AI built on them, while adding connectionism, situated AI, anddynamic systems theory as extra storeys. Which of these is the moststable, and whether the foundations need to be re-worked, are questionsreaders will be eager to explore." -- Margaret A. Boden, Professor ofPhilosophy and Psychology, University of Sussex, UK "Haugeland's MindDesign II brings together nearly all the essential philosophicalperspectives in Cognitive Science. If you want to understand currentopinion on the philosophy of mind, you should make sure you are familiarwith the contents of this book." -- James L. McClelland, Carnegie MellonUniversity and the Center for the Neural Basis of Cognition

Mind design is the endeavor to understand mind (thinking, intellect) interms of its design (how it is built, how it works). Unlike traditionalempirical psychology, it is more oriented toward the "how" than the"what." An experiment in mind design is more likely to be an attempt tobuild something and make it work--as in artificial intelligence--than toobserve or analyze what already exists. Mind design is psychology byreverse engineering. When Mind Design was first published in1981, it became a classic in the then-nascent fields of cognitivescience and AI. This second edition retains four landmark essays fromthe first, adding to them one earlier milestone (Turing's "ComputingMachinery and Intelligence") and eleven more recent articles aboutconnectionism, dynamical systems, and symbolic versus nonsymbolicmodels. The contributors are divided about evenly between philosophersand scientists. Yet all are "philosophical" in that they addressfundamental issues and concepts; and all are "scientific" in that theyare technically sophisticated and concerned with concrete empiricalresearch. Contributors: Rodney A. Brooks, Paul M. Churchland, AndyClark, Daniel C. Dennett, Hubert L. Dreyfus, Jerry A. Fodor, JosephGaron, John Haugeland, Marvin Minsky, Allen Newell, Zenon W. Pylyshyn,William Ramsey, Jay F. Rosenberg, David E. Rumelhart, John R. Searle,Herbert A. Simon, Paul Smolensky, Stephen Stich, A. M. Turing, Timothyvan Gelder ... Read more

Customer Reviews (2)

Great Essays on A.I.
Mind Design II was my first serious introduction to artificial intelligence and the issues surrounding work in this multi-disciplinary area.I found it both accessible and enlightening.That being said, it is by no means a completely light read for newcomers, and it is important to invest time into thinking about the key discussion points of the book (connectionism (NFAI) vs. GOFAI, symbolism, representation, etc.).My only complaint with the book is that it is hard to tell the difference between what is current and what isn't (Turing's essay, for instance), and the fact that it was published in 1997 doesn't make it any easier.Nevertheless, I highly recommend this book to anyone interested in learning more about the philosophy and science of "mind design."

The best compendium of papers on artificial intelligence
This is the best compendium of papers in artificial intelligence that I've seen (at least on the same level of "the artificial intelligence debate" -- which is also excellent).

However, some of these ideasare getting outdated. If you want to see some true innovation in AI youshould check out Douglas Hofstadter's Fluid Concepts and CreativeAnalogies. ... Read more

Product Description
In 1950, Alan Turing, the British mathematician, cryptographer, and computer pioneer, looked to the future: now that the conceptual and technical parameters for electronic brains had been established, what kind of intelligence could be built? Should machine intelligence mimic the abstract thinking of a chess player or should it be more like the developing mind of a child? Should an intelligent agent only think, or should it also learn, feel, and grow?

Affect and Artificial Intelligence is the first in-depth analysis of affect and intersubjectivity in the computational sciences. Elizabeth Wilson makes use of archival and unpublished material from the early years of AI (1945-70) until the present to show that early researchers were more engaged with questions of emotion than many commentators have assumed. She documents how affectivity was managed in the canonical works of Walter Pitts in the 1940s and Turing in the 1950s, in projects from the 1960s that injected artificial agents into psychotherapeutic encounters, in chess-playing machines from the 1940s to the present, and in the Kismet (sociable robotics) project at MIT in the 1990s.

Elizabeth A. Wilson is a professor in the Department of Women's Studies at Emory University. She is the author of Neural Geographies: Feminism and the Microstructure of Cognition and Psychosomatic: Feminism and the Neurological Body.

"Original and beautifully written." -Lucy Suchman, Lancaster University

"An elegantly written, thoroughly engaging, and absolutely compelling history of the role of emotions and affect in thought about, and design of, 'artificial intelligence.'" -Robert Mitchell, Duke University ... Read more

Product Description

In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence–solving the complex problems that arise wherever computer technology is applied. Key representation techniques including logic, semantic and connectionist networks, graphical models, and many more are introduced. Presentation of agent technology and the use of ontologies are added. A new machine-learning chapter is based on stochastic methods, including first-order Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation. A new presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling. Use of Markov decision processes in reinforcement learning. Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi, are added. A new supplemental programming book is available online and in print: AI Algorithms in Prolog, Lisp and Java ™. References and citations are updated throughout the Sixth Edition. For all readers interested in artificial intelligence.

Customer Reviews (9)

good mention of Hidden Markov Models
One distinguishing feature of the 6th edition is the prominent place given to Hidden Markov Models. Indeed, one might have asked for these to have been equally prominent in earlier editions. For several (>10) years, HMMs have been successfully used in various practical applications. Above all, in Automatic Speech Recognition. To often correctly infer the word or phrase that was uttered. The models have made ASRs prominent and for the most part, practical in being used in mass consumer applications. But HMMs in those contexts were not often considered AI per se. Here, the text moves HMM squarely into view, as a valid and vital technique for AI.

Not that the text is restricted to this, of course. It still has a broad introductory coverage of major AI topics. Consider the predicate calculus. Or stochastic methods to infer meaning. [You might consider HMM to be a special type of stochastic method.]

Perhaps the best summary of the book is that it seems attuned to practical applications of AI. The algorithm descriptions and suggested usages aid the porting to contexts where you do not necessarily need the full panoply of AI. The hard AI problems you might leave to others. You can treat this entire text as a good summation of powerful computational algorithms.

Superficial and unclear
Trying to gather the greatest audience possible, this book is superficial, completly unclear and boring. Why? Topics are quickly introduced, concepts are rarely analized deeply, it's more discorsive than formal. With so many subjects of AI in the same book not enough space can be given to all of them, so most of the chapters are lists of important algorithms or concepts, barely explained. Do you want to verify it? See the table of contents andthe number of pages, and try to see how much space can be given toevery point... not enough.

Fantastic Introduction to AI
This book really stands out among the AI texts (I've read 4 others). First, the language is clear and simple enough for undergrads to grasp. Second, there are consistent examples that pervade the text to help the reader apply each method to an established problem. Third, the explanations of algorithms/structures are crafted and phrased to TEACH, not merely to summarize a bunch of material for reference purposes. Finally, the programming chapters allow the student to realize the material, and really think about the problems by implementing them and hashing out the details.

I cannot complain about any lack of depth - the length already exceeds 900 pages. To those that desire more, look into academic journals - this is an intro. Moreover, robotics, vision, neural nets, and other topics already have their own "forked" research fields, with textbooks of comparable length focusing on those topics alone!

Enjoy! This text is sure to get you started!

this book not cover much
I bought this book for my introduction course in AI. I feel that this book has lack of somethings which are very important, neural networks, and Ai and robotics to name a few. I found that the text is very hard to understand. Again he didn't use enough example to explain some of the topics. I am lost reading this book. The book is not well structured and turned me bored after 30 minutes reading it. The reason are, AI term definations are not included as other book do, few visual diagrams, objective is not well defined. Once again, he didn't include introduction/review of what we acpect to learn of each of every chapters. Reading it is like reading a "white bible". Only plain text and unprofessional layout. This book discorage me reading it. I think i should buy other book that have a wider coverage topics in AI and yet easy to understand, consistent with my AI course syllibus and yet easy for my eyes.

Good For Beginners in AI
This is a very good book for anyone wanting to get an insight. Good for the first college course in AI too. It introduces the different areas of AI quite well, and develops logic before doing that. Prolog and LISP are also introduced.

The only reason I wouldn't give this book 5 stars is because
1) The Prolog and LISP features aren't all that great. They could have done better than just explaining what they did.

2) There was very little or almost no depth in the material covered. I wanted to go on reading more about the advanced features, but that never happened. So, I had to go to the library and look for something there.

But a great book for a college course. I wouldn't recommend this for a Grad course in CS...A grad student should be knowing beyond what this book covers. ... Read more

Product Description

The book’s contributing authors are among the top researchers in swarm intelligence. The book is intended to provide an overview of the subject to novices, and to offer researchers an update on interesting recent developments. Introductory chapters deal with the biological foundations, optimization, swarm robotics, and applications in new-generation telecommunication networks, while the second part contains chapters on more specific topics of swarm intelligence research.

Customer Reviews (1)

better routing protocols ?!
You might perhaps regard this as another in Springer's extensive list of texts on robotics. Some of the book describes the biological underpinnings; the swarms that exist in nature. But the bulk relates to various implementations in robotics.

There is modelling and analysis of different swarm robotic systems. Where there is often custom hardware.

In a different light, one chapter looks at not robots, but purely computers; ie. the computers are without custom mechanical fittings that are typical of robots. Instead, routing protocols are conjectured, inspired by group behaviours found in some social insect societies like ants and bees. This is perhaps [at least to me] the most imaginative of the chapters. Those authors really did a marked conceptual shift from one context to another. Good for them! ... Read more

Product Description

Artificial intelligence—broadly defined as the study of making computers perform tasks that require human intelligence—has grown rapidly as a field of research and industrial application in recent years. Whereas traditionally, AI used techniques drawn from symbolic models such as knowledge-based and logic programming systems, interest has grown in newer paradigms, notably neural networks, genetic algorithms, and fuzzy logic.

The significantly updated second edition of Fundamentals of the New Artificial Intelligence thoroughly covers the most essential and widely employed material pertaining to neural networks, genetic algorithms, fuzzy systems, rough sets, and chaos. In particular, this unique textbook explores the importance of this content for real-world applications. The exposition reveals the core principles, concepts, and technologies in a concise and accessible, easy-to-understand manner, and as a result, prerequisites are minimal: A basic understanding of computer programming and mathematics makes the book suitable for readers coming to this subject for the first time.

Topics and features:

• Retains the well-received features of the first edition, yet clarifies and expands on the topic

• Features completely new material on simulated annealing, Boltzmann machines, and extended fuzzy if-then rules tables [NEW]

• Emphasizes the real-world applications derived from this important area of computer science

• Provides easy-to-comprehend descriptions and algorithms

• Updates all references, for maximum usefulness to professors, students, and other readers [NEW]

• Integrates all material, yet allows each chapter to be used or studied independently

This invaluable text and reference is an authoritative introduction to the subject and is therefore ideal for upper-level undergraduates and graduates studying intelligent computing, soft computing, neural networks, evolutionary computing, and fuzzy systems. In addition, the material is self-contained and therefore valuable to researchers in many related disciplines. Professor Munakata is a leading figure in this field and has given courses on this topic extensively.

Product Description
"This book is ... not on philosophy, science or reality, but why and how minds might invent such things. The laws of mind, not the laws of gravity or electricity, but the methods of the mind that made these tools. It is not on brains of neurons, controllers or computers, but the logical possibilities of mind, from minimal axioms deducing all the kinds of mind that are and can ever be."Mind Making applies original artificial intelligence research to precisely define "mind". On that foundation, author Patrick Roberts advances ancient questions of free-will, reality and ethics. Deeper, the book offers a scientific method for resolving philosophical questions."How to prove a model of mind? Only by testing an analogous combination of entirely mindless parts. Otherwise, you remain trapped in endless debates, never reaching certainties because you can't suspend your own mind. Twenty-five hundred years of futile verbal philosophical debate ends. Philosophy becomes an engineering problem: Machine mind m outperformed mind n in a statistically significant set of tests. n's assumptions about reality are wrong. m's are right and are complete because m contains no minds but those we made."To the psychologist, Mind Making offers a model of the human mind unburdened by the technicalities of neurons and chemistry. To the engineer, designs for more reliable, powerful machines. To the philosopher, proven ultimate reality. To the lay reader, better knowledge of his mind, and of his world as an effect of that mind."These laws of mind are all that can be true for everyone, everywhere, forever. They can't be false because they made truth. Always true, you need never doubt them. In your mind, they are the last possessions you can lose. By comparison, all other knowledge is trivia." ... Read more

Customer Reviews (1)

Simply. Brilliant!
Mind Making is a one-of-a-kind piece of work that avoids the usual traps of other philosophy and artificial intelligence books. Patrick Roberts' Mind Making is a dense read with many, many superb ideas. You don't have to be a philosopher or computer programmer to appreciate it. And, at just over 150 pages, it makes for a quick, yet dense but satisfying read. This is the kind of book that will hold people's interest for many years - make that decades - to come. My only gripe: the book feels somehow incomplete. The book's ambition doesn't fit its length. It's as if the author left out some of the pieces of the puzzle. Though I'm sure we can look forward to a revised edition in the future which will contain solutions to the unanswered questions. Overall, Mind Making is a definite must-read! ... Read more

Product Description

This book presents sequential decision theory from a novel algorithmic information theory perspective. While the former is suited for active agents in known environments, the latter is suited for passive prediction in unknown environments. The book introduces these two different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an unknown environment. Most AI problems can easily be formulated within this theory, reducing the conceptual problems to pure computational ones. Considered problem classes include sequence prediction, strategic games, function minimization, reinforcement and supervised learning. The discussion includes formal definitions of intelligence order relations, the horizon problem and relations to other approaches. One intention of this book is to excite a broader AI audience about abstract algorithmic information theory concepts, and conversely to inform theorists about exciting applications to AI.

Customer Reviews (6)

A gem under a pile of unnecessary mathematical obfuscation
This is probably the most rigorous attempt to formalize AI. The book
succeeds in presenting the state-of-the-art AI theory from a technical
point of view, but neglects intuition, and is difficult to read for the novice and thus inaccessible to a wider audience. I will try to be explain the ideas in a less technical manner in this review.

The main idea of the book in combining classical control theory
concepts with Bayesian inference and algorithmic information theory.
The author avoids to struggle with anthropocentric aspects of
intelligence (which are subject to a fierce debate that is sometimes
of metaphysical nature) by defining intelligent agents as utility
maximizing-systems. The core ideas are, in a nutshell:

1) Goal: Build a system with an I/O stream interfaced with an
environment, where inputs are observations and outputs are actions,
that optimizes some cumulative reward function over the observations.
Two ingredients are necessary: model the a priori unknown environment
and solve for the reward-maximizing actions.

2) Model: This is a probability distribution over future observations
conditioned on the past (actions and observations). Instead of using
any particular domain-specific model, he uses a weighted average of
"all" models. By "all models", the set of all mechanically calculable
models is meant, i.e. the set of all algorithmically approximable
probability models.

3) Policy: Given the model, all possible futures can be simulated (up
to a predefined horizon) by trying out all possible interaction paths.
Essentially, a huge decision tree is constructed. Having this
information, it is easy to solve for the best policy. Just pick at
each step the action that promises the highest expected future reward.
This is done using Bellman's optimality equations.

Why does this theoretically work? If the environment is equal to one
of the sub-models in the mixture, then the combined model's posterior
estimation converges to the environment. The model is updated step by
step using Bayes' rule. Since the model becomes more accurate, the
policy based on it converges to the optimum. This is certainly very
impressive. Algorithmic information theory is the main tool to derive
the theory. Most convergence results depend on the complexities of the
models.

Does it work in practice? Unfortunately, the presented solution cannot
be implemented in practice since it is incomputable. Even worse, there
is at the moment no principled way to downscale his approach (and make
it practical), since we don't know how to simplify (a) model the
mixture and (b) the computation of the policy. The author makes these
points very clear in his book. I believe that these are the main
challenges for future AI research.

The PROs: This is the first time I see a unified, formal and
mathematically sound presentation of artificial intelligence. The
proposed theoretical solution provides invaluable insight about the
nature of learning and acting-hidden even in very subtle details in
his approach and in his equations. Whereas you might feel that
classical AI or commonplace Machine Learning theory looks like a
patchwork of interesting concepts and methods, here (almost)
everything fits nicely together into a coherent and elegant solution.
Once you have studied and understood this book (which is taking years
in my case), it is very difficult to go back to the traditional
approaches of AI.

The CONTRAs: I have however some critiques against this book. Hutter
is a brilliant mathematician and sharp thinker. Unfortunately his
writing style is very formal and he sometimes neglects intuition. The book introduces difficult notation (although some of it pays off in the
long run) that ends up obfuscating simple ideas. The overly
mathematical style has certainly not helped to the spread of
the proposal.

To summarize, this books represents a giant leap in the theory of AI.
If you have advanced mathematical training and enough patience to
study it, then this book is for you. For the more practically-oriented
researcher in AI, I recommend waiting more time until a more user-friendly version of this book is published.

Axiomatic Artificial Intelligence Theories
Hutter's book is the most recent attempt to put artificial intelligence on a firm mathematical footing. (For an earlier effort see, for instance,
Theory of Problem Solving: An Approach to Artificial Intelligence, Ranan Banerji, Elsevier, 1969) If successful, such a foundation would permit us to elaborate and explore intelligence by applying the formal methods of mathematics (e.g., theorem proving).

Hutter starts from Werbos' definition of intelligence: "a system to handle all of the calculations from crude inputs through to overt actions in an adaptive way so as to maximize some measure of performance over time" which seems reasonable. (P. J. Werbos, IEEE Trans. Systems, Man, and Cybernetics, 1987, pg 7)

Finding all of the proper axioms for such a mathematical theory of intelligence is still an open and difficult problem, however. Hutter places great stock in Occam's razor.But there is experimental evidence that Occam's razor is incorrect. (The Myth of Simplicity, M. Bunge, Prentice-Hall, 1963) See also, Machine Learning, Tom Mitchell, McGraw-Hill, 1997, pg 65-66.Rather than saying that nature IS simple I believe that it is more correct to say that we are forced to approximate nature with simple models because "our" (both human and AI) memory and processing power is limited.

I am also unsure that we should assume a scalar utility. In Theory of Games and Economic Behavior (Princeton U. Press, 1944, pg 19-20) von Neumann and Morgenstern said: "We have conceded that one may doubt whether a person can always decide which of two alternatives...he prefers...It leads to what may be described as a many-dimensional vector concept of utility."Vector utility (value pluralism) has been employed in AI in my Asa H system (Trans. Kansas Academy of Science, vol. 109, # 3/4, pg 159, 2006)

I suppose, then, that I object to the word "Universal" in Hutter's title.
I think that he is exploring only one kind of intelligence and that there are others.

Very ambitious project.
This book differs from most books on the theoretical formulations of artificial intelligence in that it attempts to give a more rigorous accounting of machine learning and to rank machines according to their intelligence. To accomplish this ranking, the author introduces a concept called `universal artificial intelligence,' which is constructed in the context of algorithmic information theory. In fact, the book could be considered to be a formulation of artificial intelligence from the standpoint of algorithmic information theory, and is strongly dependent on such notions as Kolmogorov complexity, the Solomonoff universal prior, Martin-Lof random sequences and Occam's razor. These are all straightforward mathematical concepts with which to work with, the only issue for researchers being their efficacy in giving a useful notion of machine intelligence.

The author begins the book with a "short tour" of what will be discussed in the book, and this serves as helpful motivation for the reader. The reader is expected to have a background in algorithmic information theory, but the author does give a brief review of it in chapter two. In addition, a background in sequential decision theory and control theory would allow a deeper appreciation of the author's approach. In chapter four, he even gives a dictionary that maps concepts in artificial intelligence to those in control theory. For example, an `agent' in AI is a `controller' in control theory, a `belief state' in AI is an `information state' in control theory, and `temporal difference learning' in AI is `dynamic programming' or `value/policy iteration' in control theory. Most interestingly, this mapping illustrates the idea that notions of learning, exploration, adaptation, that one views as "intelligent" can be given interpretations that one does not normally view as intelligent. The re-interpretation of `intelligent' concepts as `unintelligent' ones is typical in the history of AI and is no doubt responsible for the belief that machine intelligence has not yet been achieved.

The author's formulations are very dependent on the notion of Occam's razor with its emphasis on simple explanations. The measurement of complexity that is used in algorithmic information theory is that of Kolmogorov complexity, which one can use to measure the a prior plausibility of a particular string of symbols. The author though wants to use the `Solomonoff universal prior', which is defined as the probability that the output of a universal Turing machine starts with the string when presented with fair coin tosses on the input tape. As the author points out, this quantity is however not a probability measure, but only a `semimeasure', since it is not normalized to 1, but he shows how to bound it by expressions involving the Kolmogorov complexity.

The author also makes use of the agent model, but where now the agent is assumed to be acting in a probabilistic environment, with which it is undergoing a series of cycles. In the k-th cycle, the agent performs an action, which then results in a perception, and the (k+1)-th cycle then begins. The goal of the agent is to maximize future rewards, which are provided by the environment. The author then studies the case where the probability distribution of the environment is known, in order to motivate the notion of a `universal algorithmic agent (AIXI).' This type of agent does not attempt to learn the true probability distribution of the environment, but instead replaces it by a generalized universal prior that converges to it. This prior is a generalization of the Solomonoff universal prior and involves taking a weighted sum over all environments (programs) that give a certain output given the history of a particular sequence presented to it. The AIXI system is uniquely defined by the universal prior and the relation specifying its outputs. The author is careful to point out that the output relation is dependent on the lifespan or initial horizon of the agent. Other than this dependence the AIXI machine is a system that does not have any adjustable parameters.

The author's approach is very ambitious, for he attempts to define when an agent or machine could be considered to be `universally optimal.' Such a machine would be able to find the solution to any problem (with the assumption that it is indeed solvable) and be able to learn any task (with the assumption that it is learnable). The process or program by which the machine does this is `optimal' in the sense that no other program can solve or learn significantly faster than it can. The machine is `universal' in that it is independent of the true environment, and thus can function in any domain. This means that a universal optimal machine could perform financial time series prediction as well as discover and prove new results in mathematics, and do so better than any other machine. The notion of a universally optimal machine is useful in the author's view since it allows the construction of an `intelligence order relation' on the "policies" of a machine. A policy is thought of as a program that takes information and delivers it to the environment. A policy p is `more intelligent' than a policy p' if p delivers a higher expected reward than p'.

The author is aware that his constructions need justification from current practices in AI if they are to be useful. He therefore gives several examples dealing with game playing, sequence prediction, function minimization, and reinforcement and supervised learning as evidence of the power of his approach. These examples are all interesting in the abstract, but if his approach is to be fruitful in practice it is imperative that he give explicit recommendations on how to construct a policy that would allow a machine to be as universal and optimal (realistically) as he defines it (formally) in the book. Even more problematic though would be the awesome task of checking (proving) whether a policy is indeed universally optimal. This might be even more difficult than the actual construction of the policy itself.

The State of the Art as it Exists Today
Artificial Intelligence has proven to be one of those elusive holy grails of computing. Playing chess (very, very well) has proven possible, while driving a car or surviving in the wilderness is a long, long way from possible. Even the definition of these problems has proven impossible.

This book first makes the assumption that unlimited computational resources are available, and then proceeds to develop a universal theory of decision making to derive a rational reinforcement learning agent.

Even this approach is incomputable and impossible to implement. Chapter 7 presents a modified approach that will reduce the computational requirements, although they remain huge.

Chapter 8 summarizes the assumptions, problems, limitations, performance of this approach, and concludes with some less technical remarks on various philosophical issues.

This is a highly theoretical book that describes the state of the art in AI approaches. Each chapter concludes with a series of problems which vary from "Very Easy, solvable from the top of your head" to "If you can solve this you should publish it in the professional literature."

This is the state of the art as it exists today.

Theoretical universal AI
Solomonoff's famous inference model solves the inductive learning problem in a universal and provably very powerful way.Many methods from statistics (maximum likelihood, maximum entropy, minimum description length...) can be shown to be special cases of the model described by Solomonoff.However Solomonoff Induction has two significant shortcomings: Firstly it is not computable, and secondly it only deals with passive environments.Although many problems can be formulated in terms of sequence prediction (for example categorisation), in AI in general an agent must be able to deal with an active environment where the agent's decisions affect the future state of the environment.

In essence, the AIXI model, the main topic of this book, is an extension of Solomonoff Induction to this much more general space of active environments.Although the model itself is very simple (it is really just Solomonoff's model with an expectimax tree added to examine the potential consequences of the agent's actions) the resulting analysis is now more difficult than in the passive case. While optimality can be show in certain senses, the powerful convergence bounds that Solomonoff induction has now appear to be difficult to establish.

Like Solomonoff induction, AIXI also suffers from computability problems.In the one of the final sections a modified version of AIXI is presented which is shown to be computable and optimal in some sense.Practically this algorithm would be much too slow, but this is a clear step away from abstract models which can in theory be implemented.

For anybody interested in universal theories of artificial intelligence this book is a must.The presentation is quite technical in places and thus the reader should have some understanding of theoretical computer science, statistics and Kolmogorov complexity. ... Read more

Product Description
This book provides a comprehensive introduction to the use of artificial intelligence (AI) in computer games. The author concentrates on the techniques and strategies for developing efficient AI engines for gaming applications. Building on fundamental principles of artificial intelligence, the author explains how to create nonplayer characters (NPCs) with progressively more sophisticated capabilities.

Starting with the basic capability of acting in the game world, the book explains how to develop NPCs who can perceive, remember what they perceive, and then to continue in the game play to think about the effects of possible actions and finally to learn from their experience.

The author considers the system architecture and explains how to implement potential behaviors (both reactive and deliberate) for intelligent and responsive NPCs allowing for games that are more fun and engaging. ... Read more

Customer Reviews (6)

A joy to read
I had a lot of fun reading this book - it's short ( compared to those giant 700-800 page books ) but covers a lot of very interesting concepts with clear and simple examples. The concepts presented include the most recent developments in Game AI and academic AI which is nice. The author describes the algorithms and ideas used in various aspects of Game AI design by guiding the reader through a simple game. The writing is clear and concise. Overall, a joy to read.

The real title should include "intro"
The first thing I noticed when I got it up was how thin it was. It reminded me of the small reference O'reilly books. Props for having a hardcover though. I think that it is really called "Artificial Intelligence for Computer Game An Introduction", but you would only know that by seeing it on the first page as that isn't on the cover, side or back.

Before getting into the book I have to mention the code. You get your first glimpse of code on page seventeen where a class header is shown. The class name is tgGameState. Any guess what "tg" stands for? Neither do I. He tries to save on space by having functions with partial words like "inline getNumCharacters()", but the follows it with a pointless comment // Get the number of characters. In appendix B (Programming) it says that code is written to be as easy to understand as possible and is therefore not that efficient. If he had wanted to go for readability he would have expanded the function names, removed the pointless comment, and ditched all the inlines and not of even mentioned the constructor, deconstructor (which aren't defined in the book anyway) etc.It would have been much better to use sudo code.

Onto the actual book. My mention of the reference O'reilly books wasn't just to point out the size. This book really does feel like a jumping off point for AI in computer games. topics are briefly mentioned, but never really gone into depth and to make it sound complicated greek symbols are used when showing a formula. I would have appreciated five or six footnotes per pages telling where to get more information, but most of the time there wasn't (but there was a lot in the back). The first two chapters where more of a crash course in game design. So by the time I was on chapter three and on page 33 you can tell that was nervous that i was 1/3 through the book and really hadn't gotten into any sort of real AI stuff. but it picks up from there. There are a lot of hints for how to integrate AI into games. For example a Non-player controller (NPC) could have an arrow drawn on its chest (where it thinks the player is) and other visuals indicating its internal state.One neat idea was that your NPC could have several decision making units that could be swapped out. When really close to the player the most CPU intensive one would be used and when far away in the locked room the "stand still" one could be used. Perception, Mood, Remembering, Searching, some basic physics were all touched upon. In chapter 7 it gets very close to mentioning/talking about genetic algorithms, but alas it was not to be.

The title really should have had "an introduction" in it. I expected it to be bigger with more in-depth explanations that didn't leave me hanging. On the plus side I found out the name of the orc on the cover is named "Fluffy".For an easy read that is fairly high level on this topic this book isn't that bad, but you probably want to compliment it will others.

Great book for beginners
Well the reviewer above who said the book was worth 5 bucks totally missed the point of the book.This was not the typical 400-plus-page book chock full of code examples that could be dropped into an app and used as code modules.This book is specifically for those who would like a relatively quick, comprehensive overview of a lot of the main areas that computer game AI involves.None are gone into extremely deeply, but they don't need to be - that's not the point of the book.

The book did seem short when I first saw it, but there's a surprising amount of content here.For me it was a perfect intro to game AI and a great book to start with for anyone who would like to learn more about the subject.

Worth about 5 bucks
A total rip off! This skimpy booklet reads like a thesis and has only scarce and laughable code snippets. 35 dollars for this? You must be kidding! If interested in a decent, hands on game AI programming book try "Programming Game AI by Example" by Mat Buckland.

A manual of basic techniques
Artificial Intelligence For Computer Games by John David Funge is a solid, straightforward instructional text of basic artificial intelligence theory, the principles from which it derives, and how it is practically applied to program challenging and creative NPC behavior in popular computer games. Black-and-white diagrams and boolean logic symbols help drive the precepts home, though Artificial Intelligence For Computer Games does not contain any computer code per se - this is a manual of basic techniques that can generalize to any programming system. An absolute must-read for anyone striving to program or refine their own games. ... Read more

Product Description
Examines both theory and its practical applications. Includes discussions of the practical problems involved in their implementation. Textbook. ... Read more

Customer Reviews (2)

Serious errors
I used to use this text for AI undergraduate courses, and have given up.It has grievous errors in it, including in the on-line errata.Two quick examples: the information theoretic equations for decision trees is dead wrong.And the alpha-beta algorithm is completely incorrect.The text also has packed large amounts of information into very tight spaces, leading to poor explanations in important sections: the backpropagation section in particular consistently leads to serious confusion among students.And attempts to reduce algorithms into a procedural rather than functional/recursive format only result in excess complexity.

This is particularly frustrating because there really is no good undergraduate text for AI.This one comes close, packing in lots of stuff into an inexpensive volume.But the errors are serious enough, and in such high-utility sections, that this book cannot be recommended.

This is a useful book by eminent authors.
This is a good book for the beginners in the subject. I liked the book for its beautiful writing style. The most useful chapters of this book are chapter 5 on learning, chapter 7 on planning and chapter 9 on imageunderstanding.The concepts on the situation calculus discussed in chapter 6are also presented very interestingly. The examples used to illustratedifferent issues are realistic. The book must be on the desk of anyoneinterested in the domain of Artificial Intelligence. ... Read more

Product Description
Automated planning technology now plays a significant role in a variety of demanding applications, ranging from controlling space vehicles and robots to playing the game of bridge. These real-world applications create new opportunities for synergy between theory and practice: observing what works well in practice leads to better theories of planning, and better theories lead to better performance of practical applications.

Automated Planning mirrors this dialogue by offering a comprehensive, up-to-date resource on both the theory and practice of automated planning. The book goes well beyond classical planning, to include temporal planning, resource scheduling, planning under uncertainty, and modern techniques for plan generation, such as task decomposition, propositional satisfiability, constraint satisfaction, and model checking.

The authors combine over 30 years experience in planning research and development to offer an invaluable text to researchers, professionals, and graduate students.

*Comprehensively explains paradigms for automated planning.
*Provides a thorough understanding of theory and planning practice, and how they relate to each other.
*Presents case studies of applications in space, robotics, CAD/CAM, process control, emergency operations, and games.

*Provides a thorough understanding of AI planning theory and practice, and how they relate to each other.
*Covers all the contemporary topics of planning, as well as important practical applications of planning, such as model checking and game playing.
*Presents case studies and applications in planning engineering, space, robotics, CAD/CAM, process control, emergency operations, and games.
*Provides lecture notes, examples of programming assignments, pointers to downloadable planning systems and related information online. ... Read more

Customer Reviews (3)

Excellent presentation that fills a void
Until this book, possibly the only comprehensive treatment of planning has been a paper collection: Readings in Planning (Morgan Kaufmann Series in Representation and Reasoning). What these authors have done is phenominal - they've marshalled a bibliography of 565 publications into a comprehensive treatment from a common point of view. That makes it much easier to analyze different approaches to planning, as well as to see how various application domains have applied these approaches to solve real problems.

The first 448 pages of the book discusses various planning approaches, from classical state-space planning including recent improvements in the STRIPS model (GraphPlan), to dealing with temporal operations and resource scheduling. They then use the readers understanding of these deterministic approaches to bridge to planning under uncertainty, which is where planning meets the "real world" of imperfect knowledge, observability or even actions having unintended effects. The next roughly 100 pages goes into application domains discussing how space applications, robotics, manufacturing, emergency evacuation and even the game of bridge has used these planning methods to give the reader better intuitions on their own domain.

Finally some minority approaches such as case-based planning and plan related areas such as plan recognition are introduced briefly, leading to tutorial appendices on search (and complexity), first order logic, and model checking.

I have been working on the periphery of planning research for over 25 years, including (currently) directing advanced research in adversarial planning (a topic not addressed by this book, but that's hardly surprising given the novelty of the approach ;-). This is the best overview and reference I've seen to date for this very important area.

Good book, but could be better
The book is good, covers a lot and is very clear.

The downside: there are some small errors and mistakes.For example, the authors define gamma: SxAxE -> 2^Sas the transition function, where S is the state space, A is the set of actions, E is the set of events. Later they say that if there are no events to be considered from the outside world, then you could use E={} (empty set) -- Assumption A3, page 10. Although this is intuitively OK, it is mathematically flawed, because the cartesian product of anything with {} is{}.
Planning with MDPs and specially with POMDPs deserves more attention. In particular, the very short commentary on planning with POMDPs mentions that it is not possible to solve big POMDPs. This is not true anymore; there are very good heuristics for POMDP solving currently.
I think more theorems could have been presented and proved, and some advanced sections could be added to each chapter (some authors include a section with a star, for example)
I also don't like the way pseudo-code is presented, but that is a matter of taste.

It would also be nice if the examples in chapter 2 were fully specified. That helps a lot to understand how problems are represented.

On the good side, there are LOTS of examples for each definition, and there are exercises at the end of each chapter (more exercises would be nice, actually). I also like the discussion and historical remarks at the end of chapters.

This is certainly a very good book. Anyone interested in planning ought to have it (and people interested in AI will certainly benefit from it).

Great Introductory Book.
Automated Planning is a good book for those who get started out in the field of search and planning. It's a good overview of the topics that abound within the planning community.

The only downside of the book is its dealing with important topics like planning graphs and markov description process is cursory, and more detail would have been nice. ... Read more

Product Description
Accessible to all readers, including students of secondary school and amateur technology enthusiasts, Robotics, Mechatronics, and Artificial Intelligence simplifies the process of finding basic circuits to perform simple tasks, such as how to control a DC or step motor, and provides instruction on creating moving robotic parts, such as an "eye" or an "ear." Though many companies offer kits for project construction, most experimenters want to design and build their own robots and other creatures specific to their needs and goals. With this new book by Newton Braga, hobbyists and experimenters around the world will be able to decide what skills they want to feature in a project and then choose the right "building blocks" to create the ideal results.

In the past few years the technology of robotics, mechatronics, and artificial intelligence has exploded, leaving many people with the desire but not the means to build their own projects. The author's fascination with and expertise in the exciting field of robotics is demonstrated by the range of simple to complex project blocks he provides, which are designed to benefit both novice and experienced robotics enthusiasts. The common components and technology featured in the project blocks are especially beneficial to readers who need practical solutions that can be implemented easily by their own hands, without incorporating expensive, complicated technology.

Accessible to technicians and hobbyists with many levels of experience, and written to provide inexpensive and creative fun with robotics

Appeals to all sorts of technology enthusiasts, including those involved with electronics, computers, home automation, mechanics, and other areas. ... Read more

Customer Reviews (3)

Excellent Entry Level Text to Robotics
For anyone who is new to robotics or interfacing motors in general, this book will be well received.

What I liked about it is the way it encourages the use of electrical principles to achieve common robotics goals over microcontroller based control.(If you want your robot to come on at night and stay off during the day for example, just use a switching circuit with an LDR or similar device.Why program something that's not necessary).

There is a section on micro processors but it's rather rudimentary and to be honest could probably be skipped over.

The true value of this book comes from the hardware circuits which are instantly accessible out of the box.

I found it a handy reference with great simple circuits that would certainly save a student or casual experimenter a lot of time and hence the title of the book.

This book would be useful on the bookshelf of most robotics enthusiasts and for the price is highly recommended(there were great circuits in this book that could take 4 chapters in some books to get to the point.This book illustrates them in a page, simple straight forward and ready to go which is what this book is about).

If your after the finer points on robotics and prefer a more in depth break down of electrical cicuits (right down to transistor biasing calculations and designing your own switching circuits for complex feedback hardware then this book might not be for you), however even for the advanced robotics enthusiast I'm sure this would be a handy collection of circuits for quick easy reference.

I recommend it and if later on the author was to add a few chapters on programming interfaces for embedded controllers, this book would certainly get a 5 star rating.

Enjoy,
Sean A. Curtin

Don't believe the title
Robotics, Mechatronics and Artificial Intelligence may be an odd title for this book, as, while most of the circuit blocks are certainly usable in robotics, there are very few where a robot would be the primary use.There are circuits for anything from Touch Lamps and Light Dimmers through to the more robotic circuits like the particularly good chapter on H-Bridges.There are blocks which cover a good number of uses in robotics and mechatronics, but I don't know exactly where he gets the AI bit of his title.

One thing the title got exactly right though was 'Experimental Ciruit Blocks'.There were a number of errors in type but, more importantly if you're using this book as a reference, the schematics.Things like in the "how to use transistors" chapter he more than once mixes his PNP and NPN transistors.Nearly all of the blocks are fine, just keep on your toes.

Very useful ref of practical examples, but with errors
This is a good ref. for, in particular hobbyists and students, where a collection of useful practical examples can be found.However, I spotted quite a number of errors (both typing errors and errors in circuit diagrams such as wrong pin no., incorrect connection point or polarity).For inexperience users, following exactly some of the circuits may result in producing smoke factory.I would like to recommend to recheck and correct some of these errors.I gave it 4 stars instead of 5 stars partly because of these errors. ... Read more

Product Description

Customer Reviews (21)

Good introductory book
This book is a great introduction to CI for people who are starting out. The coverage is broad rather than deep - along with the expected theoretical background on Term-Document Vectors, similarity computations using Cosine similarity and Pearson correlation, etc, it also introduces software that CI programmers are likely to find useful, for example, Nutch for web-crawling, Lucene for text tokenization and search/indexing, Weka for data mining, etc, although you would have to spend some time with these tools by yourself if you don't already know them.

For people who have been working on CI for a while, this book provides great insights on how to use the various concepts to areas such as clustering, classification and building predictive models, and recipes to translate item and user data into meaningful information. Depending on your previous experience though, you may find certain sections of the book redundant.

If I have a complaint, it would be the rather verbose Java code that accompanies the various recipes. The code is written with best practices in mind, so while it is probably directly copy-pasteable into your own code, it is harder to read and takes a bit more time to understand than similar pseudo-code (or code written with readability as its primary objective).

Overall, a very practical and informative book that I think would be useful to both new and experienced CI programmers alike.

A lot of ideas, but neither theoretical enough nor pratical enough
This book contains a lot of ideas and as such is a good starting point for further reading.But it's not a one-stop resource for actually implementing the algorithms it mentions, as a lot of them are described only in a very high level and incomplete way.For example, in the discussion of model-based recommendation engines in sections 12.3.3-12.3.5, the author gives a very short description of latent semantic indexing (LSI) and some Java code that shows how to use the Weka implementation.But firstly, the description is too short to give the user a real understanding of what is going on theoretically.And secondly, the implementation description doesn't go nearly far enough:it shows that reconstructing the original matrix from the top N dimensions of the singular value decomposition gives a close approximation to the original, but then it just stops there; it doesn't explain how to actually use the decomposition in a recommendation engine.And the section on LSI is verbose compared to the "section" on Bayesian belief networks, which at a single paragraph of text is completely inadequate for either practical or theoretical purposes.And so on throughout the book.

It's a must read..
I'm a start-up CEO, who's had 3 of my engineers review this book. Unanimously, they came back raving about how much they picked up from the book and hence how much time they saved. If you manage any technical resources and are interested in this area, buy a copy for each of your developers, it will save you and your team a lot of time and effort.

Fascinating book about how Web 2.0 sites work.
To really understand this book one would probably have to be a Java programmer, which I'm not, but I was able to follow the argumentation. I do have some background with data mining using SAS and SQL and the mathematics described are fairly easy to understand for someone with even a 1st year engineering or applied math background.I also have an interest in linguistics which kept me going.

The basic idea is that one can catalog documents by removing irrelevant words (adjectives, abstract pronouns, conjunctives) and "stemming" the remaining words (ie: reducing "sews", "sewing", "resew", "sewer" to a root "sew") and creating a vector containing each root word and the word frequency and then normalizing it.One simple result is the ability to produce "word clouds".Similarity between documents is measured by taking the dot product of the two vectors. Any document compared to itself would have a dot product of 1. Two documents with no common stem words would have a dot product of zero.Similar docs would have a high value close to 1, say .8. Dissimilar docs would have a low coefficient, say .15. Even mistaking "sewer" (a conduit for waste) and sewer (one who uses a needle and thread) is taken into account because both docs would only be similar on a couple of keywords, and dissimilar on most others.

What's really neat is how this information gets collected and can be applied. Social networking sites, including the one you are reading right now, Amazon.com, collect data on us through our choices. Browse for a book while logged on then that's something you are interested in. Approve a review the words in the review, summary of the book and the title counts towards your interests.Disapprove and that counts against your interests.Write a review and the words you write become part of your cumulative profile as well, reduced to a vector or vectors of keywords and frequencies.

Here's how it gets applied:One of Amazon's marketing tools is it's "recommendation engine". (The book talks about Netflix recommendation engine and business model).By matching your vector against other people who have bought/viewed what you have bought a prediction can be made as to the likelihood of you being interested in the something that they have bought, or not interested in items that they rejected or disliked.The more Amazon caters to what you are interested in, and doesn't bother you with irrelevancies, the happier you may be.

Other applications discussed include the automatic creation of folksonomies (taxonomies based on popular usage) using cluster analysis and categorization using Bayes theorem.

In addition to recommendation engines Alag points out the usefulness of these techniques to Search and points out several search engines that apply this approach (as does Google),tools that search out and provide news based on your preferences, or suggest "friends" (ie: Facebook or eHarmony might use these ideas), search for similar material to identify copyright infringement, email filters that keep out spam for rolex watches or viagra (unless you are interested in rolex watches or viagra), construct a virus detection engine based on code phrases or early detection of epidemics or adverse reactions to medication through similarities in medical reports.Alag himself appears to be working at a biotech firm NextBio that matches public medical and genome related data to data held by private companies.

Some of the basic tools discussed are Lucene, a free version of what Google will sell you for a search engine, Nutch, a free web crawler, both of which require coding and WEKA, a free open source data mining package that looks usable by the rest of us.

Loved the book and the author's organization of the material.Some of the social implications are scary, especially for privacy concerns, but so is the implication of not leveraging the information that one holds within your organization to provide the best possible service. For example the World Bank has the capability (not necessarily using these methods) to match similar projects around the world so that experience gained in one area can be found and applied elsewhere. This is a key fast moving tech that one needs to understand in order to see where we are going as a society.C.I. in Action is merely the opening salvo - the methods and techniques described are the basics but there is much room for refinement and elaboration and this topic could be the start of a whole new field. The book also recommends and has sparked my interest in the site [...] which is probably more accessible to someone without a math or tech background.

Finally a note to SF fans, esp. of Spider Robinson's Callahan's Crosstime Saloon series, this may be the point at which the Web starts to appear to be intelligent. :-)

Collective Intelligence in Action
This book is more deserving of the "Collective Intelligence" title than O'Reilly's "Programming Collective Intelligence" as it's not just about algorithms, but discusses blogs, wikis etc, and shows how to do basic implementations of features such as tag clouds or finding related content in that context. Instead of explaining specific algorithms in detail, existing Java libraries are used, e.g. WEKA for data mining and Lucene for search.

There are lots of diagrams, and (somewhat verbose) Java code. The examples in this book are good starting points for further exploration; this book is more about showing what can be done and getting you started in the right direction than providing you with an understanding of the algorithms (as does the O'Reilly book) and libraries that are used.
... Read more

Product Description

This is the first book on current research on artificial general intelligence (AGI), work explicitly focused on engineering general intelligence – autonomous, self-reflective, self-improving, commonsensical intelligence. Each author explains a specific aspect of AGI in detail in each chapter, while also investigating the common themes in the work of diverse groups, and posing the big, open questions in this vital area.

This book will be of interest to researchers and students who require a coherent treatment of AGI and the relationships between AI and related fields such as physics, philosophy, neuroscience, linguistics, psychology, biology, sociology, anthropology and engineering.

Customer Reviews (2)

A review of Artificial General Intelligence
If you are interested in human-level artificial intelligence you probably should own this book.I liked reading the book and am glad I own it but there are criticisms.Most of the book is too qualitative.Even where prototype software has been deployed algorithms are not given, even in pseudocode.Too much of the book is speculation.I also think that too little attention has been paid to the control of complexity.

Introduction to the most ambitious projects ever undertaken in the history of technology
In the past five decades, the field of artificial intelligence has made significant progress, some of which can be characterized as radical departures with the past, while some as steady progress built on preconceived ideas. In general, progress in any field of endeavor is recognized by the participants and by the observers thereof, but this has not been the case in artificial intelligence. With few exceptions anytime an advance is made in this field it is at first greeted with a great deal of enthusiasm, and the algorithms it deploys are viewed as "intelligent." After some time however (and this time is relatively short) the algorithms are "understood" and are then designated as mere "programs" that certainly cannot be considered as intelligent. The "advance" finds its place in history as "trivial", and certainly not to be given any further consideration as "intelligent". Consequently, intelligent machines are always considered to be just beyond the horizon, as a goal to be achieved when better technology and algorithms are available.

But again, progress has been made in artificial intelligence: there are intelligent machines and they are used quite extensively in business and industry. But these machines are limited if one judges them from the standpoint of what is possible using human intelligence. The algorithms, or reasoning patterns that they deploy, are limited to working in a specific domain, such as finance, radiology, or network engineering. Human intelligence on the contrary can function in many different domains: a good chess player can also be a good musician or a good architect. Of course one can easily place algorithms in a particular machine each one of which has expertise in a particular domain, but they cannot cross over from one domain to another without considerable alteration from the designer or specialist. And any change in one domain-specific algorithm or reasoning pattern will not effect the efficacy of another algorithm or reasoning pattern with expertise in a different domain. To make an analogy with what is often discussed in the field of cognitive science, the machines of today thus have "modularized" intelligence: the modules or "programs" or "software" are designed to "think" in a certain domain or perform tasks restricted to certain domains.

There are a few in the artificial intelligence community that believe that genuine machine intelligence must at least be domain-independent, along with exhibiting curiosity and an ability to adapt to radically new situations. Such intelligence, in analogy with the human case, must be general enough to deal with situations, challenges, and contexts that are not tied to one domain. This has been called 'artificial general intelligence' (AGI) and is the subject of this book. It is a collection of articles by some of the individuals who have been actively involved in AGI and are working hard to bring it to fruition. The challenges in doing this are enormous, due in part to the paucity in funding for such endeavors, but due mostly to the conceptual difficulties involved in constructing reasoning patterns that can operate in many different domains without the assistance of the human engineer/designer. Suffice it to say that the goals that are discussed in this book represent the most ambitious projects ever attempted in the history of technology.

To assess or monitor the progress in AGI requires that one have at least a working definition of intelligence, and in the article by Pei Wang entitled "The Logic of Intelligence" this requirement is articulated clearly, albeit in a more general context. Wang asks whether there is an "essence of intelligence" that distinguishes intelligent entities from non-intelligent ones. His question is an interesting one since answering it will be necessary if one, again, is to gauge the progress in AGI. If the boundary between non-intelligent and intelligent systems is ill-defined then making claims regarding the status of AGI would be unfounded. But the definition of intelligence must also be one that is fruitful in a practical sense, since if AGI is to be successful it must have wide application in business, industry, and education. Wang settles on a "working" definition of intelligence, which he regards as a definition that is realistic enough to allow researchers to work directly with it. Such a definition will be robust in the sense that it is simple, has a close proximity to the concept to be defined, and allows a certain degree of progress to be made. His working definition of intelligence can be categorized as an adaptive one, in that it asserts that an intelligent machine is one that can adapt to its environment while having only insufficient knowledge and resources. The machine is therefore able to take the initiative to change its knowledge base or reasoning patterns as it confronts novel situations in the environment. He is careful to note what an unintelligent machine would be like, namely one that has been designed with the explicit assumption that the problems it attempts to solve are exclusively those that it has the knowledge and resources for, i.e. such a machine would be "programmed" to tackle certain problems of interest to the user, and would be given only those snippets of knowledge or expertise deemed relevant by this user. If the user were to give an intelligent machine this same collection of problems, it may not be able to find the solution more efficiently than the unintelligent one (or even find the "correct" solution), as the time scales needed for adaptation may be too long relative to the time needed for the unintelligent machine to solve the problems. The author recognizes this possible degradation in performance when using an intelligent machine, and such an issue will be very important when decisions are being made to deploy intelligent machines in time-critical situations or in situations where human or animal health is at stake.

Wang calls his version of AGI the 'Non-Axiomatic Reasoning System' (NARS) which deploys 'experience-grounded semantics', the latter of which is too be distinguished from the 'model-theoretic' semantics that is used in ordinary computing machines and is the foundation of much of theoretical computer science. In NARS, truth is dependent on the amount of evidence that is available, as is the meaning essentially. Wang also discusses in detail the need for `categorical logic' for knowledge representation, again since the machine is expected to operate with insufficient knowledge and resources, where `evidence' plays the key role in deciding the truth of statements (and not mere assignments of `T' or `F'). The NARS system will arrive at a solution that is `reasonable,' i.e. an optimal solution based on the knowledge it has at the time. Mistakes of course can be made, and in fact should be made, since otherwise the machine cannot learn from experience (even though trial and error learning is within the author's boundaries of what he considers intelligent). Therefore, an intelligent machine of the NARS type will not be "fool proof and incapable of error" to quote a line from a popular Hollywood movie. It will however constantly update it its knowledge base, a feature that the author calls `self-revisable'. He does not really say if such a machine could exhibit curiosity, i.e. do the problems it attempts to solve have to be instigated by the user or does it take the initiative to explore new knowledge bases or domains? If so, then such a machine might cause problems in deployment, since it can wander in conceptual space and not focus on the problems it was put in place to solve. However he does allow for autonomous behavior and creativity in the machine, even to such a degree that it completely loses track of the input tasks, i.e. the input tasks become `alienated' to use his words. In this regard, a NARS machine is somewhat like a human philosopher, for it can explore large conceptual spaces on its own and possibly get lost in them. Or more positively, it can find new knowledge that it did not possess before and construct concepts novel to itself (i.e. express `local creativity').

There are many other interesting discussions throughout the book, with each author outlining his/her notion of what it means for a machine to be intelligent and various strategies for constructing intelligent machines. One of these, called the Novamente project has been widely discussed in online messaging and is probably the oldest attempt to bring about AGI of those discussed in the book (at least from the standpoint of its origins). Particularly interesting in the Novamente project is its connection with dynamical systems, specifically in the role of attractors. Even though they do not mention it, the property of `shadowing' in the theory of dynamical systems may be a fruitful one for them to consider, especially in their use of `terminal attractors'. The shadowing property, if possessed by the `mind' of Novamente, would guarantee that an arbitrary dynamic pattern may not be a true `concept map' (as the authors define concept map), but it would be an approximation to some concept map. The shadowing property would guarantee that the reasoning patterns would be domain-independent, since any concept map acting on a particular domain, could be represented or approximated by some reasoning pattern. This reviewer does not know if the shadowing property has been applied to artificial intelligence, or even to neural networks, but if the dynamical systems paradigm holds in the latter, it does seem like an idea that may hold some promise, however small, for the development of domain-independent artificial intelligence. ... Read more

Customer Reviews (3)

Seven Dwarfs
1. Seven Dwarf Robots: The obstacle detection system consists ofthree ultrasonic transducers, forward, front-left, and front-right.An infrared system is used for inter-robot communications.Each robot has four photodiodes, 90 degrees apart and allows transmissions to be received regardless of rotation.Movement is accomplished by two rear wheels and single castor wheel in the front. Rules: 1. If no object is in front, then drive both wheels forward 2. If an object is in front and to the right, then turn left 3. If an object is in front and to the left, then turn right. 4. If an object or objects are detected in front and both to the left and to the right, then spin around 180 degrees.Goal: Learn how to active obstacle avoidance through active learning, by trial and error and to learn general behavioural responses and high quality peformance.
2. Flocking is a behavior found when a predator is near prey.The animals flock for protection.The flocking animals must remain in close proximity to each other whilst changing direction and speed.They must always avoid collision with each other and obstacles within their environment.The four levels of flocking control are: 1. avoid objects 2. if no other robots are detected nearby become the leader 3. if in a flock try to maintain position 4. if a flock can be seen in the distance, speed up and head towards its, with more priory being given to following the closest visible leader. It is possible there might be more than one leader."To ensure that this new leader does not simply turn around and rejoin the main body of the flock there is a short period of time fro which it is not allowed to reliquish leadership to any robots that are followers."A new leader will reliquish leadership to another leader in front of it.Robots can diverge from a leader and follow another leader.This happens when a leader robot moves too strongly/quickly when leading.Other robots that are not the leader will be seen as obstacles to avoid.A higher priority weighting is given to the leader based on number of robots in the cluster and a true flock pattern will emerge."Once the group has become leaderless then they will either become aware of a leader of another group, as a group, follow after it, or conversely, if no other leader is in the vincinity, they will bob and weave until a leader emerges from their ranks."
3. Improved communication and predator/prey algorithms were introduced in the 3rd Generation of seven dwarfs.A reward/penalty system needed to be implement to indicate good or bad behavior.The robots avoid predators and seek a recharge of the battery.The predator responded to prey based on range.Infrared signal could be used to indicate predator or a sudden change in light.The prey flees and the predator chases.
4. "Learning is a like a search process, in which the agent search the world for states that maximise reward and thus minimize punishment."Co-operative mechanism help reduce the size of the search area by distributing work load. Sharing experiences between learning robots does lead to faster and more robust learning."The task of each robots is to learn to associate the best motor action for its current situation so that it moves around whilst avoiding obstacles.The learning algorithm does not build a map of its environment, just detects ranges of : no object, 500 mm right, 500 mm left, 230 mm right, and 230 left.This information is used to do the action of high probability success."Given enough time, the robot should select the optimal actions for each situtation."
5. Robots can be trained onpatterns for of machine failure and it learns the combination of factors leading up to failure.The combination of factors leading to failure are often complex and difficiult for a human operator to recognize.
6. Artificial neural networks can learn the best mode of operation for an individual production line.
7. ANN can be used to recognize certain features on the face, such as an eye.
8. "Intelligent Wheelchair" moves around on the basis of a map of the location and must avoid hitting things, including walls and people. Yoshio Matsumoto intelligent wheelchair moves left and right by watching the face direction and gaze direction.Voice can start or stop the intelligent wheelchair. Humans and machines cooperating, as one whole.
9. A set of cameras fed ANN images of faces and taught the difference between a smiling face and an angry face.If smiling, the cameras would move gently towards the individual, and stop.If angry, they would move away from the person.The robot could be seen to interact with the facial expressions of the individual which it had been taught to recognize.
10. Nigel Archer's leg provided a study of the type of forces and stresses present in human walking."Thereby, finding itself in a role in the analysis and correction of erroneous gaits in humans."
11. Cyberhand is a new kind of hand prosthesis.The robot hand is capable of creating a link between the central nerveous system and the device."The natural hand is controlled by using the neural commands going from the CNS to the peripheral nervous system. "The peripheral system bring information such as hand position,slippage, and force back to the CNS.A telemetric link (receiver and transmitter) for both efferent and afferent signals.
12.Rodney Brooks, behavioural traits of an insect as levels or layers: 1. Avoid contact with objects 2. Wander around, avoiding obstacles 3. Explore the world by setting distance as a main goal 4. Build a map of the environment for use with path planning 5. Notice changes in the static environment 6. Reason about the world in terms of objects and perform task related to the objects 7. Formulate and execute plans that involve changing the state of the world in a desirable way. 8. Reason about the behaviour of objects in the world and modify plans accordingly.
13."Allowing machines the ability to learn to communicate with each other is an interesting step."Robots must be reward when they communicate in a good way. "With each robot learning to communicate with others, it is exciting to look at the variety of ways in which the range of information communicated can be considerably expanded."One idea is to bring pupil robots together to learn from their leader.The machine could communicate in a language unknown to man.
14. "By connecting more neurons together in a higher density, with good connections between neurons and relatively well developed learning techniques, robot machiens are becoming more and more intelligent."The three law of robotics fail in war conflict scenarios.Asimov's rules are fictional."Should robots be allow to determine their own future?""Should robot machines get a vote?"Is there anything we can do to stem the tide of machines?"There appear to be far too many driving forces, both financial and technological, to turn that around."Moravec says the advantage of brain chips is speed.Thought assisted by brain chips executing internal processes.Machines can have their intelligence distributed.What is important is the overall intelligence capabilities and overall self-sufficiency of a machine-controlled network taken together.
15. "It is more likely the fittest machine will be successful in making further machines"Military applications of ANN will proliferate. "We will see intelligent self-controlled aircraft fighting against other intelligent self-controlled aircraft."Machines can be designed to operate and evolve in space.
16. "Once the first powerful machine, with intelligence similar to that of a human, is switched on, we will most liely not get the opportunity to switch it back off."

Very insightful and historically important
From a perusal of the title, it might appear that this book is one of a few that could be classified as "futurism" or "future-projected technology". These books, which have mostly appeared in the last five years or so, have an extremely optimistic view of future developments in artificial intelligence, but most of them do not justify this optimism with rigorous scientific evidence or attempt to quantify what is means for a machine to exhibit intelligence.

This book, first published in 1997, and appearing in paperback last year, is however different in this regard. In the book the author attempts, and in general succeeds, in giving the reader an overview of the status of artificial intelligence as it was in 1997. It does project these developments out to the future, even to the year 2050, but it does so in a way that is free of the overindulgences of media hype and Hollywood exaggerations that frequently accompany "semi-popular" works on artificial intelligence. Even though it is targeted at readers that are not specialists in artificial intelligence, the book does enable readers with a general education to understand just how advanced machine intelligence was during that time. Most importantly, the author strives to identify what it means for a machine to be intelligent, and his proposals for defining and measuring machine intelligence are quite interesting and show keen insight.

Indeed, the author's views on intelligence, machine or otherwise, are quite refreshing, for he does not make them human-centric. Other species exhibit intelligence in ways that are unique to them and highly suitable for their survival. The author emphasizes that life forms or machines have a degree of intelligence that is appropriate to themselves and the contexts and environments in which they are situated. Humans he says, via technological development, are bringing about machines that may very soon exhibit intelligence that is highly competitive to that of human intelligence, but this is to be measured relative to the needs of each, and these needs may conflict. The author is concerned with this potential conflict, and he devotes a sizable portion of the book in elaborating on just how it may come about.

Throughout the book the author endeavors to contrast the differences between human and machine intelligence. The fact that humans behave and perform differently makes any comparison between machines and humans problematic he believes. The absence of a `typical' human as a standard of comparison for machine intelligence implies that other measures must be devised for estimating this intelligence. And, just as there is high variability among human performance and ability, it is to be expected that this would also be the case for machines. The machines will differ in their respective abilities and with respect to humans. In some instances these machines will "outperform" humans on various tasks, as they have done in many cases up to the time of publication of this book and at the present time.

Another interesting difference between human and machine intelligence that the author points out concerns what has been called `domain-specificity' by many researchers. In the author's view, machines that are performing "intelligent acts" do so only in certain domains that are highly specified. A machine adept at chess for example may not be good at doing network management. Humans though can think and accomplish goals in many different domains: they can be good chess players as well as good network managers. However currently there is much debate among cognitive scientists as to whether human expertise is the result of a collection of specialized modules that interact in some way or whether it is the result of a "general" type of module that can think in many different domains. The author does not indulge himself in this debate, but instead emphasizes that machines and humans in general exhibit different types of intelligence. It is only when their performance on specific tasks is compared can one say whether a machine is "smarter" than a human, or vice versa.
In the author's view, both humans and machines can learn both in a "passive" and in an "active" sense. Passive learning is closer to what one would describe as "memorization", whereas active learning involves the deliberate initiation of the learning process. Scientific investigation would be an excellent example of active learning, for it involves setting up equipment, taking measurements, etc, in order to test a particular hypothesis or hunch on the part of the investigator. Clearly some machines can do passive learning, via the addition of extra rules or data, but not all can, says the author. Machines can also perform active learning and the author discusses an example of how this is done. While doing this he diverges into a discussion of the `frame problem' in artificial intelligence, which he dismisses as not being a limitation of machine intelligence, giving examples of just why he takes this viewpoint. The frame problem, he concludes, is just as much a problem for humans as well as other life forms.

Particularly insightful is the author's discussion on the advantages of using neural networks for learning rather than depending on expert systems. He is careful to point out that artificial neurons are not exactly the same as human neurons, and therefore that artificial neural network brains will be different from human brains. The performance of these artificial brains will therefore be different, and thus their intelligence will be. The author then asks the reader to consider what goals are to be accomplished using these artificial brains. Since the construction of these brains is done to get something different from human brains, the advantages in using them must be delineated. In the author's view they must go beyond the limitations imposed by the human brain. The author spends over half of the book describing what has been accomplished in the actual construction of artificial brains, with emphasis on the activities of his laboratory at the University of Reading in the UK. All of this discussion is fascinating reading.

A riveting and thought-provoking discussion
March Of The Machines: The Breakthrough In Artificial Intelligence by Kevin Warwick (Professor of Cybernetics, University of Reading, United Kingdom) is an informed and informative survey into the history, philosophy, and state-of-the-art exposition on the development of artificial intellegiance within the framework of computer science. Drawing upon the author's extensive knowledge of the field, including his personal achievement of building robots that communicate in their own language, teach each other lessons, and behave as they will with regard to human beings, March Of The Machines is part history and part future speculation concerning the science of robots and its current and forthcoming impact on humanity. Claiming that the possibility exists for machine intelligence to surpass human intelligence, and therefore human domination of the planet, all in the reasonably near future, March Of The Machines is a riveting and thought-provoking discussion of what composes intellect and the fallout of man's own scientific achievements. Also very highly recommended is Kevin Warwick's autobiography, I, Cyborg (025207-2154, $19.95) and his experiences as a cybernetic pioneer who used his own body to advancing the science of cybernetics by surgically replacing parts of his own organic system with technological implants.
... Read more

Product Description

While Web 2.0 was about data, Web 3.0 is about knowledge and information. Scripting Intelligence: Web 3.0 Information Gathering and Processing offers the reader Ruby scripts for intelligent information management in a Web 3.0 environment—including information extraction from text, using Semantic Web technologies, information gathering (relational database metadata, web scraping, Wikipedia, Freebase), combining information from multiple sources, and strategies for publishing processed information. This book will be a valuable tool for anyone needing to gather, process, and publish web or database information across the modern web environment.

• Text processing recipes, including speech tagging and automatic summarization
• Gathering, visualizing, and publishing information from the Semantic Web
• Information gathering from traditional sources such as relational databases and web sites

Customer Reviews (2)

Excellent Web 3.0 technology overview
As a software architect building a scalable web platform that will be providing communication services for its users, this book helped me quickly get an understanding of some of the key ideas, tools, and technologies that will go into the system.There may be many different definitions for "Web 3.0" but I agree with the author that no matter on its final form a big part of Web 3.0 will be more interconnectedness and more intelligent data processing and retrieval.Web-related technology is too broad and changing too quickly to be an expert in everything but as an architect I need to know enough about the different options in order to use them effectively.The text and examples from the book are clear and concise and gave me the knowledge I needed to improve the system design and with the new background information I now know what to research further.

Ruby-centric tutorials on Semantic Web, Natural Language Processing, and Large-Scale Data Storage and Processing Technologies
This four-part book is focused on programming techniques and technologies that in the author's opinion can help next generation web applications handle data more "intelligently".The code samples are implemented in Ruby (and a little bit of Java).

Part One (Chapters 1-3) is basically an introduction to text and natural language processing, sampling tools and techniques for extracting raw text from various document types (e.g., pdf to plain text), classifying a document's subject matter (e.g., is this a document on "Health" or "Politics") or overall sentiment direction (degree of positiveness or negativeness), and recognizing entities such as persons and places in text (e.g., is "Florida" in a given sentence referring to a U.S. state, which is a place entity, or a person whose last name is Florida?).

Part Two (Chapters 4-7) provides tutorials on the Semantic Web, explaining what the RDF subject-predicate-object data format is and how a query language like SPARQL supports inferencing.URLs for publicly available RDF data sets, as well as tools and services useful for exploring them are given.

Part Three (Chapters 8-12) covers topics relating to the use of object-relational mapping (e.g., ActiveRecord and DataMapper used in standalone mode) and search (e.g., Lucene and Sphinx) technologies, publishing relational data as RDF data,and strategies for large-scale data storage involving the use of multiple servers, memcached, CouchDB, Amazon S3, or Amazon EC2.

Part Four (Chapters 13-15) includes a really good tutorial on the use of Hadoop-like Map Reduce facilities for large scale data processing, and ties things together by showing how the knowledge learned from previous chapters can be applied to the development of more substantial web applications.

The author uses many open-source gems (Ruby-centric software library) and tools in this book (most will work fine on Linux, Mac, or Windows, and with Ruby 1.8.x or 1.9, but exceptions are reported clearly), so in many cases, you only need to write a limited amount of code to follow along.If you don't want to download and install the gems, Appendix A provides instructions on how to apply for an Amazon Web Services account to access a ready to use Amazon Machine Image put together by the author for use on a rented Amazon EC2 Server Instance.

Because of the breadth of coverage, each technology can only be discussed to a limited depth, which some readers may find adequate and some may not, depending on a reader's interest on a particular topic, but most should still find this book to be a valuable resource, and that the author explains things well and concisely. ... Read more

Product Description
Is human creativity a wall that AI can never scale? Many people are happy to admit that experts in many domains can be matched by either knowledge-based or sub-symbolic systems, but even some AI researchers harbor the hope that when it comes to feats of sheer brilliance, mind over machine is an unalterable fact. In this book, the authors push AI toward a time when machines can autonomously write not just humdrum stories of the sort seen for years in AI, but first-rate fiction thought to be the province of human genius. It reports on five years of effort devoted to building a story generator--the BRUTUS.1 system.$linebreak$This book was written for three general reasons. The first theoretical reason for investing time, money, and talent in the quest for a truly creative machine is to work toward an answer to the question of whether we ourselves are machines. The second theoretical reason is to silence those who believe that logic is forever closed off from the emotional world of creativity. The practical rationale for this endeavor, and the third reason, is that machines able to work alongside humans in arenas calling for creativity will have incalculable worth. ... Read more

Customer Reviews (7)

A few useful ideas, lots of hype
This book discusses the issue of computer programs that can generate stories, with particular emphasis on a program which the authors claim can do so.

The first part of the book discusses philosophical issues regarding artificial intelligence, in attempt to answer the question, Can a computer generate stories which are indistinguishable from human-written stories.

One can see why the authors make modest claims here: if one examines carefully the algorithm presented in the second half of the book, one notices that at certain strategic points the program needs "help," i.e. human intervention.So, humans still have to do the hard part; without this, the program fails.The program can only do the "easy" parts.

Notwithstanding this and the hypey technobabble that permeates the book, this book does present useful research and references on the parts of storytelling that can be automated at the present time, which are significant.

From the back cover: "Computers can play superlative chess, diagnose disease, guide spacecraft, power robots that can deliver mail and (soon) clean hoses, etcetera.But can computers 'originate' anything?Can computers be genuinely creative?This is the toughest question that those sanguine about AI face.This book reports on a multi-year attempt to engineer a blueprint (BRUTUS) for a computer system that can hold its own against literarily creative humans, and on the first incarnation of that blueprint (BRUTUS.1)."

A prelude to automated novel writing.
Machines that can summarize documents are commonplace, as well as machines that can extract words and lines from paragraphs and rearrange them to possibly form something useful or interesting. But can a machine write a short story, or even a full-fledged novel with complex characters and themes? That such ability is not only possible for machines but is actually present in some of them is the subject of this book, and if one ignores the philosophical rhetoric on the "strong AI" problem, the authors give a fine overview of their project to create a "story-telling machine", which they have designated as BRUTUS.

The authors claim that their book "marks the marriage of logic and creativity", a claim that will raise the eyebrows of many a philosopher, literary critic, or novelist. But the intuitive dissonance that many in these professions may have regarding the reduction of the free-play of the imagination to the rigors and organization of logic should not dissuade others from believing that such a reduction is not only possible, but has actually been accomplished. Ironically, the authors early in the book assert that there are no examples of machine creativity in the world. Of course, this assertion depends on one's notion of what creativity is, and to what degree this creativity may have depended on the assistance of machines. Machines that create new mathematics, scientific theories, music, or novels do not yet exist, the authors claim, but they do take pains to express their optimism regarding future developments in "machine creativity".

The authors are incorrect in their belief that there are no machines now that can currently develop new and interesting results in a wide variety of different domains. In addition, their notion of intelligence is too anthropomorphic, too tied to what human intelligence is, or is not (and one could argue that machine intelligence is even better understood than human intelligence). The authors though have written a book that gives the reader much insight into what is involved in building creative, thinking machines. Most refreshingly, the authors do not want to settle the question of machine creativity from the comfort of their armchairs, but instead from the laboratory by actually building artificial authors. Philosophical speculation is for the most part eschewed, and is replaced by the rigors and sometimes frustrations of laboratory experiments.

According to the authors, BRUTUS exhibits "weak" creativity rather than "strong", with the latter being compared to the creation ex nihilo, examples of this being non-Euclidean geometry and the Cantor diagonalization method from mathematics. Weak creativity on the other hand, is a more practical notion, and according to the authors is rooted in the "operational" one developed by psychologists. In the development of BRUTUS, the authors wanted to create an automated story generator that satisfied seven requirements: 1. The machine must be competitive with the requirements of strong creativity. 2. The machine must be able to generate imagery in the mind of the reader. 3. The machine must produce stories in a "landscape of consciousness." 4. The machine must be capable of formalizing the concepts at the core of "belletristic" fiction, with the example of "betrayal" being emphasized the most by the authors. 5. The machine must be able to generate stories that a human would find interesting. 6. The machine must be in command of story structures that will give it "immediate standing" in the human audience. 7. The prose developed by the machine must be rich and compelling, not "mechanical". BRUTUS they say meets all of these requirements, but no doubt some critics will think otherwise. The authors do make a sound case for their assertions that it does, and it is the belief of this reviewer that they have, and that BRUTUS is one of first automated story generators. With optimism toward the future developments of BRUTUS and artificial intelligence in general, they state that "a machine able to write a full, formidable novel, or compose a feature-length film, or create and manage the unfolding story in an online game, would be, we suspect, pure gold. "

They are right.

Selmer Bringsjord tells tall tales in the guise of logic
Unfortunately, Selmer Bringsjord is very able with the form of logic but not with its substance -- he "proves" false statements and "disproves" true ones. He applies his sophistry vigorously in the service of his anti-computational agenda. But it isn't just a matter of bad faith promotion of an ideology -- true incompetence is involved. Bringsjord is famous for denying a statement that followed from a statement he claimed to be agnostic about and yet not abandoning his agnosticism. When the contradiction was pointed out to him, he wrote a paper in which he "argued" that the claim of a contradiction was fallacious by offering a bogus "inference rule" that supposedly was required, and then showing that the "inference rule" that he himself offered was fallacious. Of course, that one should not hold that not Q and at the same time be agnostic about P, when it is known that P implies Q, is not something that any competent thinker would deny, let alone publish such a paper against, a paper that could be considered the defining example of a straw man argument.

cuts across disciplines
Here at Ohio State you just as likely to find this book in hands of a philosopher as a computer scientist.It covers the "big" questions (How smart can computers get?Can they ever be truly creative? etc.), covers the logical and mathematical issues in computational story generation, and also, of course, talks about how the Brutus system was engineered.In sum, I guess the book exemplifies cognitive science.One caveat, though:the authors aggressively take a logic-based approach to AI, and pan non-symbolic (e.g., neural net-based) approaches.If you're not a fan of logic, then you'll probably want to read this book because it's the best challenge going to your point of view.If you're a logic lover, this will be your cup of tea.

I'll still have my job!
I expected to find a book that predicts creative writers will soon be replaced by machines, but what I found was -- thank goodness -- the exact opposite.The authors argue that literary fiction cannot possibly bereduced to computation -- but that formulaic fiction may be another story. Brutus is a machine master of formula.Let's just hope that I'm right thatmy own novels (which are mentioned in this book) *are* belletristic! ... Read more

Product Description
This book presents both theoretical foundations of AI and an indication of the ways that current techniques can be used in application programs. With the revision, most of the content has been preserved as it is, and an effort has been put in on adding new topics that are in sync with the recent developments in this field. ... Read more