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Artificial Intelligence: A New Synthesis

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TLDR
Intelligent agents are employed as the central characters in this new introductory text and Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI.
Abstract
Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading researchers. Artificial Intelligence: A New Synthesis takes the reader on a complete tour of this intriguing new world of AI. * An evolutionary approach provides a unifying theme * Thorough coverage of important AI ideas, old and new * Frequent use of examples and illustrative diagrams * Extensive coverage of machine learning methods throughout the text * Citations to over 500 references * Comprehensive index Table of Contents 1 Introduction 2 Stimulus-Response Agents 3 Neural Networks 4 Machine Evolution 5 State Machines 6 Robot Vision 7 Agents that Plan 8 Uninformed Search 9 Heuristic Search 10 Planning, Acting, and Learning 11 Alternative Search Formulations and Applications 12 Adversarial Search 13 The Propositional Calculus 14 Resolution in The Propositional Calculus 15 The Predicate Calculus 16 Resolution in the Predicate Calculus 17 Knowledge-Based Systems 18 Representing Commonsense Knowledge 19 Reasoning with Uncertain Information 20 Learning and Acting with Bayes Nets 21 The Situation Calculus 22 Planning 23 Multiple Agents 24 Communication Among Agents 25 Agent Architectures

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Planning Algorithms: Introductory Material

TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
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Ant algorithms for discrete optimization

TL;DR: An overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and the ant colony optimization (ACO) metaheuristic is presented.
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Simulation for the Social Scientist

TL;DR: Social scientists in a wide range of fields will find this book an essential tool for research, particularly in sociology, economics, anthropology, geography, organizational theory, political science, social policy, cognitive psychology and cognitive science, and it will also appeal to computer scientists interested in distributed artificial intelligence, multi-agent systems and agent technologies.
Journal ArticleDOI

On agent-based software engineering

TL;DR: It will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures.
References
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Book

Genetic algorithms in search, optimization, and machine learning

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI

Optimization by Simulated Annealing

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book

Computers and Intractability: A Guide to the Theory of NP-Completeness

TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.