e99 Online Shopping Mall
Help | |
Home - Science - Artificial Intelligence (Books) |
  | Back | 61-80 of 100 | Next 20 |
click price to see details click image to enlarge click link to go to the store
61. Logical Foundations of Artificial Intelligence by Michael R. Genesereth, Nils J. Nilsson | |
Hardcover: 406
Pages
(1987-07-15)
list price: US$75.95 -- used & new: US$75.00 (price subject to change: see help) Asin: 0934613311 Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description Intended both as a text for advanced undergraduates and graduate students, and as a key reference work for AI researchers and developers, Logical Foundations of Artificial Intelligence is a lucid, rigorous, and comprehensive account of the fundamentals of artificial intelligence from the standpoint of logic. The first section of the book introduces the logicist approach to AI--discussing the representation of declarative knowledge and featuring an introduction to the process of conceptualization, the syntax and semantics of predicate calculus, and the basics of other declarative representations such as frames and semantic nets.This section also provides a simple but powerful inference procedure, resolution, and shows how it can be used in a reasoning system. The next several chapters discuss nonmonotonic reasoning, induction, and reasoning under uncertainty, broadening the logical approach to deal with the inadequacies of strict logical deduction.The third section introduces modal operators that facilitate representing and reasoning about knowledge.This section also develops the process of writing predicate calculus sentences to the metalevel--to permit sentences about sentences and about reasoning processes.The final three chapters discuss the representation of knowledge about states and actions, planning, and intelligent system architecture. End-of-chapter bibliographic and historical comments provide background and point to other works of interest and research.Each chapter also contains numerous student exercises (with solutions provided in an appendix) to reinforce concepts and challenge the learner.A bibliography and index complete this comprehensive work. |
62. Experiments in artificial intelligence for small computers by John Krutch | |
Paperback: 110
Pages
(1981)
-- used & new: US$6.13 (price subject to change: see help) Asin: 0672217856 Canada | United Kingdom | Germany | France | Japan | |
63. Problem-Solving Methods in Artificial Intelligence by nils nilsson | |
Hardcover: 244
Pages
(1971)
Asin: B000PGHBQ0 Canada | United Kingdom | Germany | France | Japan | |
64. Artificial Intelligence in Finance & Investing: State-of-the-Art Technologies for Securities Selection and Portfolio Management by Robert R. Trippi | |
Hardcover: 272
Pages
(1995-11-19)
list price: US$71.95 -- used & new: US$63.99 (price subject to change: see help) Asin: 1557388687 Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description |
65. Game Development Essentials: Game Artificial Intelligence by Jr., John B. Ahlquist, Jeannie Novak | |
Paperback: 320
Pages
(2007-07-09)
list price: US$68.95 -- used & new: US$37.00 (price subject to change: see help) Asin: 1418038571 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description Customer Reviews (1)
Interesting Read |
66. Computational Intelligence: Concepts to Implementations by Russell C. Eberhart, Yuhui Shi | |
Hardcover: 496
Pages
(2007-08-24)
list price: US$82.95 -- used & new: US$53.43 (price subject to change: see help) Asin: 1558607595 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description Customer Reviews (3)
prominent acknowledgement of Hopfield
Great intro for non mathematicians...
not good enough |
67. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence by John H. Holland | |
Paperback: 228
Pages
(1992-04-29)
list price: US$29.00 -- used & new: US$26.01 (price subject to change: see help) Asin: 0262581116 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description Customer Reviews (4)
Heavily mathematical
The founder's words
Not an Introductory book
Genetic Algorithms Classic for Engineering Topics include: background, aformal framework, illustrations (genetics, economics, game playing,searches, pattern recognition and statistical inference, control andfunction optimization, and central-nervous system), schemata, the optimalallocation of trials, reproductive plans and genetic operators, therobustness of genetic plans, adaptation of coding and representations, andoverview, interim and prospectus. Inclusion of a disk ofspreadsheet-based examples would have increased user-friendliness to thesometimes moderately-complex mathematics. Otherwise, this book is a wellpresented, and useful classic for researchers and software vendors seekingto develop more innovative intelligent products. ... Read more |
68. Computational Intelligence: A Logical Approach by David Poole, Alan Mackworth, Randy Goebel | |
Hardcover: 576
Pages
(1998-01-08)
list price: US$129.00 -- used & new: US$8.00 (price subject to change: see help) Asin: 0195102703 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description Customer Reviews (4)
Buy A Better Book
Cretenous.
shame on the Mackworth and Poole
Serves well as an introduction The emphasis in the book is on intelligent agents, which the authors characterize in chapter one. Agents are viewed as black boxes that take in knowledge, past experiences, goals/values, and observations and output actions. They define what they call a representation and reasoning system consisting of a language to communicate to a computer, a methodology for giving meaning to this language, and a collection of procedures for computation. They also outline the three applications domains they will be developing in the book: an autonomous delivery robot, a diagnostic assistant, and an infobot. The authors expand upon the representation and reasoning system in chapter 2 in terms that are familiar from mathematical logic and computer science. A formal language, a semantics, and a proof procedure are the three essentials of an RRS. All of these elements are discussed in great detail, and concrete examples are given for all the main concepts. Readers without any background in logic may find the reading difficult, but with some effort it could be read profitably. The authors do a good job of presenting material that is usually delegated to texts on formal computer science. In chapter three, the authors show how representational knowledge can be used for domain representation, querying, and problem solving. This is done via an example of electrical house wiring and the PROLOG-astute reader will find the presentation very straightforward. But LISP programmers will also see its influence and the discussion on lists. An application is given in computational linguistics, namely that of definite clauses for context-free grammars. A discussion of searching is given in chapter 4, in the context of potential partial solutions to a problem, with the hope that these will truly be real solutions for the problem at hand. Graph searching, blind search strategies, heuristic searching, and refinements of these are all discussed with great clarity. And, because of their importance in applications, dynamic programming and constraint classification problems are overviewed, albeit very briefly. Chapter 5 turns to the topic of how to choose a representation langauge for knowledge. The authors detail the criteria for comparing different languages or logics in terms of expressiveness, worse-case complexity, and naturalness. Most important in this chapter is the discussion on qualitative versus quantitative representations. This is followed in chapter 6 by a discussion of the user interactions to a knowledge-based system in terms of a meta-interpreter that produces knowledge acquistion, debugging, etc. The next chapter shows how definite clause representation and reasoning systems can be extended to include the relation of equality and negation, and quantification of variables. This sets up naturally a discussion of first-order predicate calculus, but only a brief overview is given. A very short treatment of modal logic is given. Chapter 8 considers agents that act and reason in time, with three representations given for reasoning about time. These are the STRIPS representation (developed at Stanford University), the situation calculus, and the event calculus. It is then shown how these can be used to reason and produce plans to achieve goals. Although brief, the discussion is very interesting, and the authors give good references for further reading. The authors generalize their discussions to assumption-based reasoning in chapter 9, which up until this chapter has been restricted to reasoning from knowledge bases. Nonmonotonic reasoning is defined, along with abduction, which is a form of reasoning different from both deduction and induction, and which emphasizes hypothesis formation. Chapter 10 considers the more realistic situation whre the agents have incomplete or uncertain knowledge. This naturally brings up a discussion of probability, which the authors define as the study of how knowledge affects belief. They distinguish between evidence and background knowledge, the latter which is stated in terms of conditional probabilities, the former characterized by what is true in the situation being studied. Belief networks are introduced as a graphical representation of conditional independence, these graphs being directed and also acyclic (the latter for reasons of causality). An algorithm for determining the posterior distribution of belief networks is given, and is based on the idea that a belief network specifies a factorization of the joint probability distribution. A brief overview of decision networks is also given. The important topic of learning theory is overviewed in chapter 11. And, naturally, neural networks make their appearance here, although the discussion is very brief. PAC learning is also treated, as well as Bayesian learning. Unfortunately, the important field of inductive logic programming is not discussed, but some references are given. The last chapter covers artificial purposive agents, otherwise known as robots. This is a vast subject, and only a general overview is given here, but the authors do a good job of showing how robots can be characterized within the concepts outlined in the book. Dynamical systems are used to represent the agent function for a robot. Readers familiar with the theory of dynamical systems will see the state transition function appear here in a more general context. The states of an agent at time t encode all of the information about its history. The state transition functions acts on the states and percepts, with the percepts playing the role of time in the usual dynamical system. The appendices include a terminology list and a short introduction to PROLOG, along with a few examples of PROLOG code applied to some of the concepts in the book. Although very general, the inclusion of these examples are of further help in understanding the material in the book. ... Read more |
69. Artificial Dreams: The Quest for Non-Biological Intelligence by H. R. Ekbia | |
Hardcover: 416
Pages
(2008-04-28)
list price: US$86.99 -- used & new: US$54.28 (price subject to change: see help) Asin: 0521878675 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description Customer Reviews (2)
AI is Not What it Seems
Falls short in its criticism |
70. Artificial Life: A Report from the Frontier Where Computers Meet Biology by Steven Levy | |
Paperback: 400
Pages
(1993-07-27)
list price: US$21.00 -- used & new: US$5.00 (price subject to change: see help) Asin: 0679743898 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description Customer Reviews (14)
Superb!!
Great Beginners book
My Review of this Book It is about artifical intelligence. If you have a computer you will know exactly what I mean. When you hook up a computer, it acts alive, and you gotta interact with it like it is artifically intelligent. Like when I hook up the voice-recognition thing where you speake into the mikerofone, it acts like it hears you too, and does what it is told to do. Sometimes that is to write a letter, or to tell it to go onto the net. I told my computer to go onto the net once thru the mike, and it did it, as it was spoken and said what to do. So if you read and buy this book you will learn to do this, and hook it up yourself. The book has plans and charts to do all this stuff. When you read it, pass it onto a friend, and they may help you once they read it themselves. I gave this book 5-stars, because it was a very good one, and I will now know how my computer is so smart. I told it what to do, and it help me with this revue to. So buy it but just one time, because a friend and other people will be able to read this for free, once you give it to them. Engines are my hobbie, and so are electronic power supplys, so I plan to use this book for that to. I will design new ones that are faster than sound, and my computer will be smart and help me with that. So buy this book, once, and you will like it along with all the friendly people that you knowe.That's my revuiew, but I will do anew one when a new adition of the book comes out to the press. I do recomend that you buy this one time for the people who wanto know about how artifical intelligent computers get smarter and help you with life-things you need to do, but not all by yourselfe, but with a computer.
An excellent intro to a new science
fascinating It's not a masterpiece of literature, but it was interesting enough to forever change my research career. ... Read more |
71. Swarm Intelligence: From Natural to Artificial Systems by Eric Bonabeau, Marco Dorigo, Guy Theraulaz | |
Kindle Edition: 320
Pages
(1999-08-27)
list price: US$75.00 Asin: B000QTD41C Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description Customer Reviews (3)
Impressively good, but not an introduction This book illustrates several features of swarm behavior that can be leveraged for optimization. The authors writing style is equivalent to technical papers, so be prepared...this is no easy book.
A first milestone in the study of Swarm Intelligence
Algorithms inspired by social insects |
72. Artificial Intelligence: Mirrors for the Mind (Milestones in Discovery and Invention) by Harry Henderson | |
Hardcover: 176
Pages
(2007-04-13)
list price: US$35.00 -- used & new: US$12.00 (price subject to change: see help) Asin: 0816057494 Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description |
73. Handbook of Artificial Intelligence (v. 3) by Avron Barr | |
Hardcover: 360
Pages
(1986-06)
list price: US$58.50 -- used & new: US$58.50 (price subject to change: see help) Asin: 0201168901 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Customer Reviews (1)
Good overview of GOFAI After a brief introduction to A.I. in chapter one, chapter two overviews the use of search algorithms for intelligent problem solving. The emphasis initially is on the problem representations that form the basis of search techniques, such as state-space and problem-reduction representations. Game tree representations are also discussed. The algorithms that implement the problem representations are then treated. If the search space is viewed merely syntactically, these are called "blind search" algorithms, which are distinguished from "heuristic" methods, which exploit various structural information about the problem in order to limit the search. Examples of blind search methods that are discussed include breadth-first, uniform-cost, depth-first, and bidirectional search. Examples of heuristic methods discussed are ordered state-space, bidirectional, and the famous A*-algorithm, the latter of which is still finding considerable use in new applications of A.I. Examples of game tree search that are covered include the minimax procedure, the negmax formalism, and alpha-beta pruning. There is discussion on the use of heuristics in game tree search, but this part is out-of-date due to the advances made in chess playing, checkers, etc, since this volume was published. Chapter three is an overview of knowledge representation in A.I. The author takes a pragmatic approach to the nature of knowledge and intelligence, and defines the "representation of knowledge" as a combination of data structures and interpretive procedures that will lead to what he calls "knowledgeable" behavior. A book needs a reader before it could be considered knowledge, argues the author. He calls this whole enterprise "experimental epistemology" , which endeavors to create programs that exhibit intelligent behavior. The chapter gives an overview of the knowledge representation schemes used in A.I. and discusses their uses and shortcomings. Also, the tension between the advocates of declarative versus procedural knowledge representations is discussed. Declarative systems are more logical/mathematically based, and were exemplified by theorem-provers based on logical resolution. The procedural approach emphasized a more directed approach to the problem of inference and one that makes the reasoning process more understandable.There is a brief discussion on semantic nets, which were invented as a model of human associative memory. The net consists of nodes, which represent concepts, objects, or events, and links between the nodes. The relevant facts about a concept can be inferred from the nodes to which they are linked directly, and so an extensive database search is not necessary. The semantics of net structures depends only on the program that uses them, and so any notion of "logical validity" of inferences from using the net is absent. Production systems are also discussed in this chapter, these being developed as models of human cognition. These systems are called "modular" knowledge representation schemes in that the database consists of rules, or "productions", that take the form of condition-action pairs. The conditions in which each rule is applicable are made explicit and thus the interactions between the rules are minimized. These systems have been used to control the interaction between declarative and procedural statements and to develop autonomous learning systems. In addition, the chapter includes a discussion of the "frame" knowledge representation system, which at the time of publication, was just getting started in A.I. research. It has been widely discussed since then, mostly in the context of studying how to implement reasoning about actions, and became to be known as the "frame problem". The proliferation of the frame axioms needed made reasoning about actions difficult or cumbersome, but was later solved using what are now called "successor-state axioms". The chapter also includes a discussion of the standard logical representational schemes: propositional and first-order predicate logic. Since the time of publication, and due to the interest in developing "common-sense" reasoning machines, second-order predicate logic has made its appearance in A.I. research, sometimes being called "ontological engineering" in the literature. Also, due to the time of publication, there is no discussion of inductive logic programming, which has recently gained importance in A.I. research and its applications. Chapter four covers the very important topic of natural language understanding. This is one of the areas in A.I. that has been the target of an enormous amount of research, for the ability of a computer to converse with a human fluently and with understanding would be a major advance for A.I., perhaps even an "acid test" that true intelligence has finally been achieved in a machine. The chapter gives a brief history of research in natural language processing and discusses the early attempts at machine translation from one language to another. There is also extensive discussion on grammars, parsing techniques, and text generation. Several examples of programs used for natural language processing that were popular at the time of publication are discussed. ... Read more |
74. Fundamentals of Artificial Intelligence - Lisp by James Noyes | |
Hardcover: 644
Pages
(1992-01-01)
list price: US$104.95 -- used & new: US$67.97 (price subject to change: see help) Asin: 0669194735 Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description |
75. Scripts, Plans, Goals, and Understanding: An Inquiry Into Human Knowledge Structures (Artificial Intelligence Series) by Roger C. Schank, Robert P. Abelson | |
Paperback: 256
Pages
(1977-07-01)
list price: US$67.50 -- used & new: US$44.95 (price subject to change: see help) Asin: 0898591384 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Customer Reviews (1)
A Classic in the field and Still Inspiring |
76. Encyclopedia of Artificial Intelligence by SC SHAPIRO | |
Hardcover: 1246
Pages
(2000-05)
list price: US$55.00 Isbn: 0471807486 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description Customer Reviews (3)
Encyclopedia of Artificial Intelligence
useful.
excelent |
77. Artificial Intelligence for Computer Games | |
Hardcover: 290
Pages
(2011-01-29)
list price: US$129.00 -- used & new: US$110.51 (price subject to change: see help) Asin: 1441972560 Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description |
78. Computational Intelligence: Principles, Techniques and Applications by Amit Konar | |
Hardcover: 708
Pages
(2005-05-31)
list price: US$149.00 -- used & new: US$79.26 (price subject to change: see help) Asin: 3540208984 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description Computational Intelligence: Principles, Techniques and Applications presents both theories and applications of computational intelligence in a clear, precise and highly comprehensive style. The textbook addresses the fundamental aspects of fuzzy sets and logic, neural networks, evolutionary computing and belief networks. The application areas include fuzzy databases, fuzzy control, image understanding, expert systems, object recognition, criminal investigation, telecommunication networks, and intelligent robots. The book contains many numerical examples and homework problems with sufficient hints so that the students can solve them on their own. A CD-ROM containing the simulations is supplied with the book, to enable interested readers to develop their own application programs with the supplied C/ C++ toolbox. Customer Reviews (1)
Computational Intelligence: Principles, Techniques and Applications |
79. Mathematical Methods in Artificial Intelligence (Practitioners) by Edward A. Bender | |
Paperback: 656
Pages
(1996-02-10)
list price: US$83.95 -- used & new: US$71.00 (price subject to change: see help) Asin: 0818672005 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description The book explores AI from three different viewpoints: goals, methods or tools, and achievements and failures. Its goals of reasoning, planning, learning, or language understanding and use are centered around the expert system idea. The tools of AI are presented in terms of what can be incorporated in the data structures. The book looks into the concepts and tools of limited structure, mathematical logic, logic-like representation, numerical information, and nonsymbolic structures. The text emphasizes the main mathematical tools for representing and manipulating knowledge symbolically. These are various forms of logic for qualitative knowledge, and probability and related concepts for quantitative knowledge. The main tools for manipulating knowledge nonsymbolically, as neural nets, are optimization methods and statistics. This material is covered in the text by topics such as trees and search, classical mathematical logic, and uncertainty and reasoning. A solutions diskette is available, please call for more information. Customer Reviews (4)
Good, but somewhat outdated
Interesting but content bit disconnected
Excellent The author gives a brief overview of the history of A.I. in chapter one, including a discussion of the issues of computational complexity in A.I. algorithms, a discussion of expert systems (with examples), and a few biographical sketches. Chapter 2 is a fairly detailed overview of search algorithms, and the author introduces some notions from the mathematical field of combinatorics, namely directed graphs and ordered trees. Induction and recursion are then reviewed as tools for search algorithms. The recursive formulation of algorithms in A.I. is of course very powerful, and one that students need to master early on. Fields such as bioinformatics and data mining are becoming increasingly dependent on search algorithms from A.I., and the author reviews these in detail, including 'simple' search methods such as breadth-first, depth-first, and iterative-deepening, along with 'heuristic' methods. The reader gets introduced to first-order predicate calculus in chapter 3. This topic could be said to be one of the most important ones in A.I., and it is discussed in this chapter using the (declarative) programming language Prolog. One could easily use the language Lisp, but Prolog makes more apparent the head/body clause structure of predicate logic. In addition, if a reader wants to move on to more modern developments in A.I., such as inductive logic programming, which can be viewed essentially as predicate logic but with inductive reasoning, a mastery of the content of this chapter is essential. Chapter 4 introduces the reader to the proof theory, namely the technique of resolution, which is discussed for propositional calculus, where it is very simple, and for predicate logic, in the latter wherein some specialized techniques must be brought in, such as Skolemization. The author also discussed proof in the context of Prolog, and introduces the cut operator, which inhibits Prolog from fully implementing resolution. He also gives an interesting discussion on the problem of negation in Prolog and the closed-world assumption. The author has been careful to not write a purely theoretical book in computer science, and evidence of this is given in chapter 5, which discusses how to implement first-order logic (FOL) into real-world applications. It is one thing to discuss the properties of logic, quite another to actually use it productively to solve problems of interest. The author discusses the limitations of FOL in these applications, and how they can be resolved through alternative reasoning tools, such as nonmonotonic logics, Bayesian networks, and fuzzy sets. One of these alternatives, nonmonotonic reasoning, is discussed in the next chapter, wherein the author gives a fairly detailed overview of default reasoning and how it is implemented in Prolog. Rule sets and semantic nets are also discussed, along with defeasible reasoning. Applications of these techniques are stymied by their computational complexity, and the author gives references for discussions of this. After a review of probability theory in chapter 7, the author discusses Bayesian networks in chapter 8. These have been extremely important in recent applications of A.I., and the author gives a fine review of their properties, especially their ability to incorporate causality by imposing a directed graph structure on the event space. The author gives a few examples of Bayesian networks, including a medical diagnosis, wherein he introduces a very important concept in A.I., namely that of abductive inference. Detailed discussion (with proofs) is given for the Kim-Pearl algorithm for singly connected networks. Chapter 9 is an introduction to fuzzy logic and belief theory. The author motivates nicely the reasons for considering fuzzy reasoning instead of probabilistic methods. The Dempster-Shafer belief theory, which has become popular in recent years, is also discussed in some detail. So as to motivate the discussion of neural networks, the next chapter overviews automatic pattern classification. Contrasting between supervised and unsupervised learning of patterns, the author then outlines the types of automatic classifiers, such as decision trees and neural networks. The chapter on neural networks is a good introduction considering the vastness of the subject. Indeed, an enormous amount of research has been done on neural networks, and their use in applications of A.I. has finally been achieving success in recent years. Concepts from information theory are of course very important in A.I. and these are discussed in chapter 12, along with more advanced topics in probability and statistics that were not treated earlier in the book. These ideas are used in the next chapter wherein neural networks and decisions trees are discussed in more detail. The most interesting part of this discussion is the idea that noise can improve the generalization capabilities of neural networks. This strategy will be obvious to the physicist reader who has studied the effects of noise on dynamical systems governed by potentials with local minima. The last chapter of the book discusses some additional topics that should be included in a study of A.I., such as genetic algorithms and more discussion of optimization, such as simulated annealing. Hidden Markov models are also briefly discussed, and this is somewhat disappointing given their importance in current applications. The reader is also introduced to robotics, certainly the most exciting of all topics in 21st century A.I.
It is a useful book for research oriented readers. |
80. The Emergence of Artificial Cognition: An Introduction to Collective Learning by Peter Bock | |
Hardcover: 323
Pages
(1993-01)
list price: US$94.00 -- used & new: US$94.00 (price subject to change: see help) Asin: 9810211694 Average Customer Review: Canada | United Kingdom | Germany | France | Japan | |
Editorial Review Product Description Customer Reviews (2)
Collective Learning explained
The definitive work on Collective Learning Systems. |
  | Back | 61-80 of 100 | Next 20 |