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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... Learning 361 11.1 What Is Learning ? 361 11.2 Random Learning and Neural Nets 363 11.3 Rote Learning 363 11.4 Learning by Parameter Adjustment 365 11.5 Learning in GPS 367 11.6 Concept Learning 368 11.7 Discovery as Learning : AM 375 11.8 ...
... Learning 361 11.1 What Is Learning ? 361 11.2 Random Learning and Neural Nets 363 11.3 Rote Learning 363 11.4 Learning by Parameter Adjustment 365 11.5 Learning in GPS 367 11.6 Concept Learning 368 11.7 Discovery as Learning : AM 375 11.8 ...
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Elaine Rich. 11.2 RANDOM LEARNING AND NEURAL NETS The analogy between human learning and machine learning is alluring . People learn by changing the structure of the neural network that makes up their brains . Why then should we not ...
Elaine Rich. 11.2 RANDOM LEARNING AND NEURAL NETS The analogy between human learning and machine learning is alluring . People learn by changing the structure of the neural network that makes up their brains . Why then should we not ...
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... learning procedure used this . o Matching . Concept learning exploits matching to detect similarities and differences between instances . • 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 . • Hill climbing . Parameter adjustment and concept learning are both varieties of hill climbing ...
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addition algorithm already answer appear applied approach appropriate Artificial Intelligence attempt better block called Chapter choose clauses combined complete concept consider contains corresponding dependency described discussed domain example exploit explore fact frame function given goal grammar graph heuristic important indicates initial input interpretation involved John kinds knowledge labelings language learning logic look machine Marcus match means methods move natural necessary node objects occur operators particular path patterns perform position possible predicate present problem procedure produce prove question reasoning representation represented result rules satisfied script selected semantic sentence shown in Figure shows simple single situation solution solve space specific stack statement step stored structure Suppose task techniques things tion tree true understanding variety