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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... subgoals . A simple example of this failure is shown in Figure 3-21 . Assuming that both node C and node E ... sub- goals will be presented . 3.6.7 Constraint Satisfaction Many problems in A.I. can be viewed as problems of constraint ...
... subgoals . A simple example of this failure is shown in Figure 3-21 . Assuming that both node C and node E ... sub- goals will be presented . 3.6.7 Constraint Satisfaction Many problems in A.I. can be viewed as problems of constraint ...
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... subgoals . Each of them , in turn , may lead to a set of ad- ditional subgoals . Sometimes it is easy to detect that there is no way that a given set of subgoals can all be satisfied at once . For example , the robot arm cannot be both ...
... subgoals . Each of them , in turn , may lead to a set of ad- ditional subgoals . Sometimes it is easy to detect that there is no way that a given set of subgoals can all be satisfied at once . For example , the robot arm cannot be both ...
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... subgoals . Whenever a precondition describes a specific piece of clinical evidence , MYCIN uses that evidence if it already has access to it . Otherwise , it asks the user to provide the information . In order that MYCIN's requests for ...
... subgoals . Whenever a precondition describes a specific piece of clinical evidence , MYCIN uses that evidence if it already has access to it . Otherwise , it asks the user to provide the information . In order that MYCIN's requests for ...
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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