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 150
... variables are universally quantified , so the prefix can just be dropped and any proof procedure we use can simply assume that any variable it sees is universally quantified . Now the formula produced in step 4 appears as [ ~ Roman ( x ) ...
... variables are universally quantified , so the prefix can just be dropped and any proof procedure we use can simply assume that any variable it sees is universally quantified . Now the formula produced in step 4 appears as [ ~ Roman ( 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 ) ...
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A.I. programs algorithm answer applied approach appropriate arcs ARMEMPTY Artificial Intelligence backtracking best-first search branching factor breadth-first search Chapter chess clauses CLEAR(A complete Computer concept conceptual dependency consider constraints contains database described discussed domain example expert systems exploit explore fact frame game tree goal grammar graph heuristic heuristic function important input INTERLISP ISA links John knowledge representation labelings learning LISP machine Marcus match methods minimax move MYCIN natural language 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 variety