Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Addison-Wesley, 1998 - Artificial intelligence - 824 pages
Combines the theoretical foundations of intelligent problem-solving with he data structures and algorithms needed for its implementation. The book presents logic, rule, object and agent-based architectures, along with example programs written in LISP and PROLOG. The practical applications of AI have been kept within the context of its broader goal: understanding the patterns of intelligence as it operates in this world of uncertainty, complexity and change. The introductory and concluding chapters take a new look at the potentials and challenges facing artificial intelligence and cognitive science.An extended treatment of knowledge-based problem-solving is given including model-based and case-based reasoning. Includes new material on:Fundamentals of search, inference and knowledge representatioAI algorithms and data structures in LISP and PROLOProduction systems, blackboards, and meta-interpreters including planers, rule-based reasoners, and inheritance systemsMachine-learning including ID3 with bagging and boosting, explanation based learning, PAC learning, and other forms of inductioNeural networks, including perceptrons, back propogation, Kohonen networks, Hopfield networks, Grossberg learning, and counterpropagationEmergent and social methods of learning and adaptation, including genetic algorithms, genetic programming and artificial lifeObject and agent-based problem solving and other forms of advanced knowledge representation
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8-puzzle allows and/or graph applied approach argument artificial intelligence backtrack behavior best-first search breadth-first search called Chapter clause complex concept conceptual graphs data-driven database defined definition defun depth-first search described elements evaluation example expert systems Figure formal function genetic goal goal-driven grammar heuristic search human implement important inference rules input instance interpretation knowledge base knowledge representation learning LISP logic match modus ponens moves natural language node noun objects operators output parameters parent parse pattern physical symbol system possible predicate calculus expressions premise problem domain problem solving production system PROLOG properties propositional calculus query reasoning recursive relationships represent representation language requires result s-expression search algorithms search space Section semantic semantic networks sentence simple solver space search specific strategies structure subgoals substitutions symbol techniques theorem theory tree true truth value unification unify variable vector