Artificial Intelligence Techniques in PrologThis unique book is a broad, clear presentation of artificial intelligence (AI) problem-solving techniques. It selects the most important among the well-defined algorithms and procedures in the field, explains them in plain language, and, where appropriate, provides ALGOL-like descriptions of them. Every technique is implemented in Prolog, a language that is quickly learned and allows for easy experimentation in a learning environment. The book includes complete source listings, and the software is available online. This book is ideal for hands-on courses in AI programming. It is also a useful primary or supplementary text in general introductory AI courses and a complete sourcebook for the practitioner. |
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addition algorithm and-or trees Arguments assert assume assumption ATMS ATMSS backtracking backward chaining Bayesian networks best-first search blackboard systems BProof breadth-first breadth-first search Bstack causal link Cert Certainty chapter clause CnumList computation Con3 concept conjunction Consider consistent constraint satisfaction contains contaminated nodes counter database defined deletion depth-first search discussed domain entropy environment example Exercise fact tokens Figure findall forward chaining forward-chaining rules function given goal GoalCnum GoalPred graph heuristic hierarchy input instantiated integers interval justificand justification label malaria matrix meta-interpreter modify MoreData MoreTstack MYCIN NewCnumList NewTstack NextNode Node1 nogood OldUp operator parallel combination partial evaluation path planner potential_fact_token preconditions predicate premise probability probability theory procedure production systems Prolog Prolog implementation propagate query quindim reasoning recently_in_guatemala represented retract retractall Rgoal Sender stack(a,b STRIPS succeed SymbolTable techniques TMSS tree truth maintenance systems update values variables verb