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 35
... possible to prove an upper bound on the error it incurs ( see Section 3.7 ) . For general - purpose heuristics , such as the nearest neighbor algorithm , it is often possible to prove such error bounds , which provide reassurance that ...
... possible to prove an upper bound on the error it incurs ( see Section 3.7 ) . For general - purpose heuristics , such as the nearest neighbor algorithm , it is often possible to prove such error bounds , which provide reassurance that ...
Página 356
... possible ones becomes even smaller than 18/208 . Thus not only can this approach be extended to larger problem ... possible labelings that need to be considered can be kept to a minimum . To do this , we will first pick one vertex and ...
... possible ones becomes even smaller than 18/208 . Thus not only can this approach be extended to larger problem ... possible labelings that need to be considered can be kept to a minimum . To do this , we will first pick one vertex and ...
Página 357
... possible FORK labels . Only 8 is now possible . The complete labeling just computed is shown in Figure 10-15 ( d ) . Thus we see that by exploiting constraints on vertex labelings , we have correctly identified vertex 7 as being formed ...
... possible FORK labels . Only 8 is now possible . The complete labeling just computed is shown in Figure 10-15 ( d ) . Thus we see that by exploiting constraints on vertex labelings , we have correctly identified vertex 7 as being formed ...
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Términos y frases comunes
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