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Luger G.F., Stubblefield W.A. — Artificial Intelligence: Structures and Strategies for Complex Problem Solving
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Íàçâàíèå: Artificial Intelligence: Structures and Strategies for Complex Problem SolvingÀâòîðû: Luger G.F., Stubblefield W.A. Àííîòàöèÿ: Combines the theoretical foundations of intelligent problem-solving with he data structures and algorithms needed for its implementation. The book presents logic, rule, object and agent-based architectures, along with example programs written in LISP and PROLOG. The practical applications of AI have been kept within the context of its broader goal: understanding the patterns of intelligence as it operates in this world of uncertainty, complexity and change.
ßçûê: Ðóáðèêà: Computer science /Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö ed2k: ed2k stats Èçäàíèå: third editionÃîä èçäàíèÿ: 1998Êîëè÷åñòâî ñòðàíèö: 824Äîáàâëåíà â êàòàëîã: 10.03.2006Îïåðàöèè: Ïîëîæèòü íà ïîëêó |
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Ïðåäìåòíûé óêàçàòåëü
LISP functions, cons 439 LISP functions, declare 481 LISP functions, defclass 499—501 LISP functions, defgeneric 501—503 LISP functions, defmethod 503 LISP functions, defun 432—433 LISP functions, eq 452 LISP functions, equal 439 LISP functions, eval 430—431 456—457 LISP functions, funcall 456—457 LISP functions, gensym 480—481 LISP functions, get 495 LISP functions, if 435 LISP functions, length 429 LISP functions, let 446—448 LISP functions, list 429 LISP functions, listp 448 LISP functions, mapcar 457—458 LISP functions, member 429 434—435 460 LISP functions, minusp 434 LISP functions, nth 428—429 LISP functions, null 429 448 LISP functions, numberp 434 LISP functions, oddp 434—435 LISP functions, or 435 LISP functions, plusp 434 LISP functions, print 435—436 LISP functions, quote 430—431 LISP functions, read 470 LISP functions, remprop 496 LISP functions, set 444 LISP functions, setf 444 446 495 LISP functions, terpri 470 LISP functions, zerop 434 LISP, a-list 467—469 LISP, applying functions 428 LISP, association lists 461—469 LISP, atom 426 LISP, best-first search 462—463 LISP, binding 428 LISP, binding variables 444—446 LISP, bound variable 444—456 LISP, breadth-first search 459—461 LISP, car/cdr recursion 441—443 LISP, class precedence list 503—505 LISP, CLOS 347 497—511 LISP, Common Lisp Object System see CLOS LISP, conditionals 433—436 LISP, control of evaluation 430—431 LISP, data abstraction 436—438 LISP, data types 448—449 LISP, defining classes 499 LISP, defining functions 431—433 LISP, delayed evaluation 482—486 LISP, depth-first search 449—455 459—460 LISP, dotted pairs 468 LISP, evaluation 427—431 LISP, expert system shell 486—494 LISP, farmer, wolf, goat cabbage problem 449—455 LISP, filters 455—457 LISP, form 428—430 LISP, free variable 445 LISP, function closure 482—483 LISP, functional arguments 457—458 LISP, generic functions 498 501—503 LISP, higher-order functions 455—458 LISP, inheritance 497 503—505 LISP, lambda expressions 457—458 LISP, lexical closure 482—483 LISP, list defined 426—427 LISP, local variables 446—448 LISP, logic programming 472—481 LISP, maps 455—457 LISP, meta-classes 505 LISP, meta-interpreters 469—472 LISP, meta-linguistic abstraction 472 LISP, multiple inheritance 503—505 LISP, nil 427 LISP, pattern matching 463—469 LISP, predicates 433—436 LISP, procedural abstraction 455—458 LISP, program control 433—436 453—454 LISP, property list 494—497 LISP, read-eval-print loop 427 469—472 LISP, recursion 438—443 LISP, s-expression 426 429—430 731 LISP, semantic networks 494—497 LISP, simulation 505—511 LISP, slot options 499—501 LISP, slot-specifiers 499—501 LISP, special declaration 481 LISP, state-space search 449—455 LISP, streams 474—476 482—486 LISP, streams and delayed evaluation 482—486 LISP, thermostat simulation 505—511 LISP, tree-recursion 441—443 LISP, unification 463—469 Literal 567 573 589 Littman, M. 747 Lloyd, J.W. 350 Locke, J. 603 778 Logic 5 7—10 13—15 19 33 37—41 46—84 90 107—120 160 164—170 179 183—184 188—196 294 297—298 318—320 343 350 358—361 427 560 566—589 593 607—608 758—759 “First-order “Automated Logic programming 347 350 472—481 587—593 Logic programming in LISP 472—481 Logic Theorist 203 560—562 Logical inference 107—108 Logically follows 64—66 LOGO 215 352 Long-term memory 173 Lovelace, A. 7—8 12 17 Loveland, D.W. 600 lt see “Logic Theorist” Lucas, R. 421 Luger, G.F. 23 30 174 198—199 217 235—237 239 246 335 562 768—769 776 780 Maass, W. 659 Machine learning 16—17 21 25 28 30 41 43 45 237 351 517—518 520 603—712 760 770—775 Machine learning, AM 25 649—650 Machine learning, analogical 607 646—649 Machine learning, autoassociative memory 696—711 Machine learning, BACON 650 Machine learning, bidirectional associative memory 702—706 Machine learning, candidate elimination algorithm 613—620 Machine learning, category formation 605 Machine learning, classification 668 672 Machine learning, CLUSTER/2 605 635 652—653 684 Machine learning, COBWEB 605 653—658 684 Machine learning, competitive learning 662 682—690 Machine learning, concept learning 605 Machine learning, concept space 606 608 Machine learning, conceptual clustering 605—606 651—653 Machine learning, conjunctive bias 636 Machine learning, connectionist 603—604 661—712 Machine learning, counterpropagation 663 683 686—690 Machine learning, covering a concept 613 Machine learning, credit assignment 623 (see also “Bucket brigade algorithm”) Machine learning, decision trees 624—633 636 Machine learning, discovery 649—650 Machine learning, emergent computation 713—749 Machine learning, empiricist’s dilemma 771 773—775 Machine learning, EURISKO 650 Machine learning, evolutionary learning see “Genetic learning” Machine learning, explanation-based 605—606 638—646 Machine learning, generalization 607—615 620—621 642—643 771 Machine learning, genetic learning 604 Machine learning, Grossberg learning 686—690 Machine learning, Hebbian learning 688—696 Machine learning, heuristics 607 619—624 634—636 645 Machine learning, hill climbing 675 Machine learning, Hopfield nets 701 706—711 Machine learning, ID3 26 249 547—548 605 619 624—636 650 680 Machine learning, IL 650 Machine learning, induction 603—633 771—775 Machine learning, inductive bias 604—605 624 633—639 662 772—773 Machine learning, information theoretic selection 628—631 Machine learning, knowledge-level learning 645 Machine learning, Kohonen networks 684—689 Machine learning, learnability 633—638 Machine learning, learning search heuristics 619—623 Machine learning, LEX 619—623 635 644 Machine learning, meta-DENDRAL 26 199 639—640 773 Machine learning, near miss 608—610 Machine learning, negative instances and overgeneralization 615—616 Machine learning, neural networks see “Connectionist models of intelligence” Machine learning, operationally criteria 641 Machine learning, overgeneralization 615—616 Machine learning, PAC learning 638 Machine learning, performance evaluation 623—624 632—633 680 721—725 Machine learning, similarity-based learning 605 638 Machine learning, specialization 608—615 Machine learning, speed-up learning 645 Machine learning, supervised Hebbian learning 694—696 Machine learning, supervised learning 605 613 694—696 Machine learning, symbol-based 603—660 Machine learning, taxonomic learning 651—658 Machine learning, top-down decision tree induction 627—628 Machine learning, unsupervised Hebbian learning 691—694 Machine learning, unsupervised learning 605 649—658 691—694 Machine learning, version space search 605 612—624 Machine learning, winner-take-all learning 682—690 Machtey, M. 34 MacLennan, B.J. 30 Macro operator 193—196 Macsyma 111—113 Maes, P. 748 Magerman, D. 549 Maier, D. 421 Malpas, J. 391 422 Manna, Z. 79 Marcus, M. 23 Markov algorithm 172 Markov chains 522 Markov models 545—546 Markov, A. 172 Martins, J. 278—279 281 290 Masterman, M. 301 304 Matisse, H. 517 McAllester, D.A. 278 281 290 McCarthy, J. 62 187 274—275 290—291 334 351 McCartney, P. 312 339 McCelland, J.L. 673 McCord, M. 422 McCorduck, P. 603 McCulloch, W.S. 662 664 711 McCulloch-Pitts neuron 662—666 McDermott, D. 121 199 271—272 289 McDermott, J. 36 174 208 215 McGraw, K.L. 245 Mead, C. 712 Means-ends analysis 204 560—566 757 766—767 Mellish, C. 350 358 421 Mercer, R. 550 Merleau-Ponty, M. 14 Meta-classes 505 Meta-DENDRAL 26 199 639—640 773 Meta-interpreters 357—358 389—391 397—415 469—472 Meta-knowledge 180 Meta-linguistic abstraction 398 472 Meta-planning 199 Metaphor 520 Mgu see “Most general unifier” Michalewicz 747 Michalski, R. 30 652 658 730 Micro-world 22 Milner.R. 775 Milton, J. 4 Mind-body problem 6—7 30 minimax 127 144—152 156—157 Minimax to fixed ply depth 147—150 Minimum distance classification 669 Minimum models 273—275 Minsky, M. 30 320 323—324 666—667 761 770 Minton, S. 640 Mitchell, M. 30 692 715 724 733 743—748 Mitchell, T. 26 612 619 639—641 643—644 Mithen.S. 762—763 Mockler, R.J. 245 Modal logics 334 Model 65—67 Model-based reasoning 206 230—235 243 Modular theories of mind 763 modus ponens 5 65—68 77—78 81 164—165 561 567 593 596 Modus tolens 66—67 80 Monotonicity 127 139 141—142 156 758 Monte Carlo replacement algorithms 716 Mooney, R. 283 640 643 Moore, J.S. 19 30 600 Moore, O.K. 562 Moore, R.C. 272 290 334 MOPS 308 328 Morignot, P. 748 Morphology 522 535 Morrison, E. 7 Morrison, P. 7 Most general unifier 70 Multi-value logics 334 360 Multiple belief reasoner 281 multiple inheritance 329 503—505 Mutation 717—718 721 727—733 736 Mutual information clustering 547—548 MYCIN 20—21 199 209 211—212 230 249 263—266 289 296 Mycroft, A. 391 393 422 Mylopoulos, J. 294 n-move look ahead 147 Nash-Webber, B.L. 304 Natural deduction 560 599—600 Natural language understanding 17 22—23 28 30 34 45 116—121 301—306 350 417—421 517—557 Natural language understanding and databases 523 551—555 Natural language understanding, applications 550—555 Natural language understanding, augmentations of logic grammars 534 Natural language understanding, augmented phrase structure grammars 534 555 Natural language understanding, augmented transition networks 528 533—538 556 Natural language understanding, bottom-up parsing 526 Natural language understanding, CART trees 546—547 Natural language understanding, case frame 540 Natural language understanding, case-grammars 555 Natural language understanding, Chomsky hierarchy 531—533 556 Natural language understanding, combining syntax and semantics 534—542 Natural language understanding, context-free grammars 524—528 531—533 Natural language understanding, context-sensitive grammars 532—533 Natural language understanding, deep structure 555 Natural language understanding, generation 527 542 Natural language understanding, grammar 116—121 524—528 532—534 555 Natural language understanding, grammatical markers 555 Natural language understanding, link grammars 550 Natural language understanding, Markov chains 522 Natural language understanding, Markov models 545—546 Natural language understanding, metaphor 556 Natural language understanding, morphology 522 535 Natural language understanding, mutual information clustering 547—548 Natural language understanding, parse tree 118—119 525—526 537—538 Natural language understanding, parsing 22 116—121 417—421 520 523—531 543—550 Natural language understanding, phonology 522 Natural language understanding, phrase structure 526 Natural language understanding, pragmatics 522 556 Natural language understanding, prosody 522 Natural language understanding, recursive descent parsing 526 Natural language understanding, semantic grammars 555 Natural language understanding, semantic interpretation 523—524 Natural language understanding, sentential form 525 Natural language understanding, SHRDLU 22 521
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