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 362
... learning is before we can design tech- niques to perform it . One way to approach this is not to try to define learning as a task , but rather to characterize the class of learning programs as distinct from other programs ...
... learning is before we can design tech- niques to perform it . One way to approach this is not to try to define learning as a task , but rather to characterize the class of learning programs as distinct from other programs ...
Página 368
... learning the differences that GPS would use . To apply GPS to the problem of discovering a good set of differences to be used by another copy of GPS working in a given problem domain , it is necessary ... LEARNING CHAP . 11 Concept Learning.
... learning the differences that GPS would use . To apply GPS to the problem of discovering a good set of differences to be used by another copy of GPS working in a given problem domain , it is necessary ... LEARNING CHAP . 11 Concept Learning.
Página 387
... learning procedure used this . o Matching . Concept learning exploits matching to detect similarities and differences between instances . o Hill climbing . Parameter adjustment and concept learning are both varieties of hill climbing ...
... learning procedure used this . o Matching . Concept learning exploits matching to detect similarities and differences between instances . o Hill climbing . Parameter adjustment and concept learning are both varieties of hill climbing ...
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A.I. programs algorithm answer applied approach appropriate arcs ARMEMPTY backtracking best-first search blocks world branching factor breadth-first search Caesar Chapter chess clauses CLEAR(A complete concept conceptual dependency consider constraint contains database described discussed domain example expert systems exploit explore fact frame game tree given grammar graph heuristic function important input INTERLISP ISA links John knowledge representation labelings learning LISP Marcus match minimax move MTRANS MYCIN node objects ON(B operators parsing particular path performed possible preconditions predicate logic probabilistic problem problem-solving produce production systems PROLOG propositional logic question reasoning representing knowledge resolution rules satisfied script search procedure search process Section semantic net semantic nets sentence shown in Figure simple situation slots solution solve specific statements step strategy structure successors Suppose syntactic task techniques theorem things tion tree true understanding UNSTACK variable variety vertex