Artificial Intelligence: A New SynthesisIntelligent 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.
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Contents
Discussion | |
Machine Evolution | |
State Machines | |
Robot Vision | |
Search in State Spaces | |
Strategies | |
The Predicate Calculus | |
Resolution in the Predicate Calculus | |
KnowledgeBased Systems | |
Representing Commonsense | |
Reasoning with Uncertain Information | |
Networks | |
Learning and Acting with Bayes Nets | |
Uninformed Search | |
Heuristic Search | |
Efficiency | |
Planning Acting and Learning | |
Alternative Search Formulations | |
Adversarial Search | |
Knowledge Representation | |
Resolution in the Propositional | |
Planning Methods Based on Logic | |
Planning | |
Communication and Integration | |
Communication among Agents | |
Agent Architectures | |
Bibliography | |
Index | |
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Common terms and phrases
action agent algorithm alpha-beta applied arcs arity array assignment assume atom backed-up value BAT_OK best-first search block Boolean branching factor breadth-first search called cell chapter clauses computer vision constraint propagation corresponding cost data structure depth bound depth-first search discussion edges Eight-puzzle environment evaluation function example Expand node feature vector game tree goal node heuristic function heuristic search iconic model input vectors interpretation iteration Korf labeled layer learning LIFTABLE machines move neural networks nodes expanded number of nodes objects operators optimal path output pixels predicate calculus problem procedure produce propositional calculus PSAT relation constant represent representation resolution refutation robot rules of inference scene analysis search graph search tree selected sense/plan/act sensory inputs sequence set of wffs shown in Figure space state-space step strategy successors symbols task techniques tip nodes truth table uniform-cost search value True variable weight vector