Artificial Intelligence, Volumen1McGraw-Hill, 1983 - 436 páginas What is artificial intelligence?; Problem solving; Problems and problem spaces; Basic problem-solving methods; Game playing; Knowledge representation; Knowledge representation using predicate logic; Knowledge representation using other logics; Structured representation of knowledge; Advanced topics; Advanced problem-solving systems; Natural language understanding; Perception; Learning; Implementing A.lI. systems: languages and machines; Conclusion; References; Index. |
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Página 149
... variables so that each quantifier binds a unique variable . Since variables are just dummy names , this process cannot affect the truth value of the wff . For example , the formula VxP ( x ) \ VxQ ( x ) would be converted to VxP ( x ) ...
... variables so that each quantifier binds a unique variable . Since variables are just dummy names , this process cannot affect the truth value of the wff . For example , the formula VxP ( x ) \ VxQ ( x ) would be converted to VxP ( x ) ...
Página 151
... variables in the set of clauses generated in step 8 . By this we mean rename the variables so that no two clauses make refer- ence to the same variable . In making this transformation , we rely on the fact that ( VxP ( x ) Ʌ Q ( x ) ...
... variables in the set of clauses generated in step 8 . By this we mean rename the variables so that no two clauses make refer- ence to the same variable . In making this transformation , we rely on the fact that ( VxP ( x ) Ʌ Q ( x ) ...
Página 262
... variables already in the stack . And whenever a candidate object is chosen to match a variable , the binding must be recorded so that other occurrences of the same variable will be bound to the same object . How should our program ...
... variables already in the stack . And whenever a candidate object is chosen to match a variable , the binding must be recorded so that other occurrences of the same variable will be bound to the same object . How should our program ...
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A.I. programs algorithm answer applied approach appropriate arcs ARMEMPTY Artificial Intelligence backtracking best-first search blocks world branching factor breadth-first search Caesar Chapter chess clauses CLEAR(A complete concept conceptual dependency consider constraints contains database described discussed domain example exploit explore fact frame game tree goal grammar graph heuristic heuristic function important input INTERLISP ISA links John knowledge representation labelings LISP Marcus match methods minimax move MYCIN natural language necessary node objects ON(B operators parsing particular path performed possible preconditions predicate logic probabilistic problem problem-solving produce production system PROLOG propositional logic question reasoning representing knowledge rules Schank script search procedure search process Section semantic net sentence shown in Figure simple situation slots solution solve specific stack statements step strategy structure successors Suppose syntactic task techniques theorem things tion tree true understanding variety