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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 Solving
Àâòîðû: 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.
The introductory and concluding chapters take a new look at the potentials and challenges facing artificial intelligence and cognitive science. An extended treatment of knowledge-based problem-solving is given including model-based and case-based reasoning. Includes new material on: Fundamentals of search, inference and knowledge representation AI algorithms and data structures in LISP and PROLOG Production systems, blackboards, and meta-interpreters including planers, rule-based reasoners, and inheritance systems. Machine-learning including ID3 with bagging and boosting, explanation basedlearning, PAC learning, and other forms of induction Neural networks, including perceptrons, back propogation, Kohonen networks, Hopfield networks, Grossberg learning, and counterpropagation. Emergent and social methods of learning and adaptation, including genetic algorithms, genetic programming and artificial life. Object and agent-based problem solving and other forms of advanced knowledge representation.
ßçûê:
Ðóáðèêà: Computer science /
Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö
ed2k: ed2k stats
Èçäàíèå: third edition
Ãîä èçäàíèÿ: 1998
Êîëè÷åñòâî ñòðàíèö: 824
Äîáàâëåíà â êàòàëîã: 10.03.2006
Îïåðàöèè: Ïîëîæèòü íà ïîëêó |
Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
Ïðåäìåòíûé óêàçàòåëü
15-puzzle 18 89
8-Puzzle 88—91 102—105 131—144 174—175 204 226
Abduction 80 206 248 281—283
Abduction, coherence-based selection 283
Abduction, cost-based 283
Abduction, logic-based 281—283
Abduction, set cover 281
Abel 267—268 290
Abelson, H. 324 511 556 775
ABSTRIPS 199 566
Ackley, D.H. 747
ACT* 174 770
Active images 353
Active values 353
Admissibility 127 139—142 156—157 758
Aeschylus 3
Agent-based systems 761—764
agents 13—17 293 345 440 770 774
Agglomerative clustering 651
Agre, P. 24 777
Aho, A. 121
Aiello, N. 198
Alexander, C. 517
Algorithm A 140
Algorithm A* 140—144 156—157 723
Allen, J. 30 199 520 542 555—556
Alpha-beta pruning 127 150—152 156
Alty, J. 245
AM 25 649—650
Amarel, S. 266 290
Analogy 25 607 646—649
Analogy, inference 646
Analogy, retrieval 646
Analogy, structure mapping 647
Analogy, systematicity 648—649
Analytic engine 7—8 17
And elimination 66—67
And introduction 66—67
And/or graph search 164—165 169
And/or graphs 109—121 154 164—169 222—223 594
Anderson, J.A. 696 712
Anderson, J.R. 30 174 301 334 562 696 712 769—770 776 780
Andrews, P.B. 79
Answer extraction 574 583—587 592
Appelt, D. 62
Arbib.M. 741
Aristotle 4—5 8—9 29—31 217 247 778
Arity 53—54 56
Artificial intelligence defined 1—2 28—29 755
Artificial life 15 604 714—715 736—747
Ashley, K. 235
Associationist representation 297—309 334
Associationist theories 297—309 334
Associative law 51
Associative memory 696—711 767 “Conceptual
Assumption-based truth maintenance 278—281
Asunuma, C. 26
Attractor network 663 701—711
Auer, P. 659
Augmentations of logic grammars 534
Augmented phrase structure grammars 534 555
Augmented Transition Networks 528 533—538 556
Austin, J.L. 14 778
Autoassociative memory 663 696—711
Autoepistemic logic 272
Automated reasoning 8—9 15 19 30 45 67 517—518 559—601
Automated reasoning and logic programming 587—593
Automated reasoning and PROLOG 587—593
Automated reasoning, answer extraction 574 583—587 592
Automated reasoning, binary resolution 567 573—578 597
Automated reasoning, breadth-first strategy 579—580
Automated reasoning, clause form 567—573 593
Automated reasoning, completeness 566
Automated reasoning, conjunctive normal form 569—572
Automated reasoning, converting to clause form 568—573 576
Automated reasoning, demodulation 598—599
Automated reasoning, factoring 575
Automated reasoning, General Problem Solver 172—174 203—204 560—566 757—758 766 768
Automated reasoning, Herbrand’s theorem 600
Automated reasoning, heuristics 578—583 593—596
Automated reasoning, horn clause 361 472 588—589 593 636
Automated reasoning, hyperresolution 575 597
Automated reasoning, linear input form 581 592
Automated reasoning, literal 567 573 589
Automated reasoning, logic theorist 203 560—562
Automated reasoning, means-ends analysis 204 560—566 757 766—767
Automated reasoning, natural deduction 560 599—600
Automated reasoning, paramodulation 598—600
Automated reasoning, prenix normal form 570
Automated reasoning, refutation 566—567 573—577
Automated reasoning, refutation completeness 566—567 579
Automated reasoning, resolution 30 66—67 350 560 566—577
Automated reasoning, resolution refutation 566—577
Automated reasoning, rule-based approaches 593—596
Automated reasoning, set of support 560 580 600
Automated reasoning, skolemization 68 569—571
Automated reasoning, soundness 65—67 566 593
Automated reasoning, subsumption 583 600
Automated reasoning, unit preference 560 580—581 592
Automated reasoning, unit resolution 581 600
Automated reasoning, weak methods 593
Axon 26
Babbage, C. 7—8 29
Back chaining 758
Backpropagation 662 668 675—682 771
Backtracking 96—101 110 130 160 163 176 198 223 361 368 592
Backward chaining 93—96 112—116
Bacon, F. 203
Baker, J.D. 21
Ballard, D. 711 780
Balzer, R. 198
Bareiss, E.R. 235
Barker, V. 174
Barkow, J.H. 762
Barr, A. 30 199
Bartlett, F. 324
Base-level categories 654
Bates, E.A. 711 780
Bateson, G. 29
Bauer, M.A. 768
Bayesian belief networks 252—258
Bayesian belief networks, causal influence measure 252
Bayesian belief networks, d-separation 255
Bayesian belief networks, qualitative influence graph 256—258
Bayesian belief networks, qualitative probabilistic network 256—258
Bayesian reasoning 249—258 263 518 769
Bayesian reasoning, Bayes theorem 251
Bayesian reasoning, belief networks 252—258
Bayesian reasoning, complexity 251
Bayes’theorem 251
Beam search 624
Bechtel, W. 780
Behaviorism 23
Benson, S. 199 201 742 748 765
Berger.A. 550
Best-first search 99 107 127—136 156 384—386 462—463 560
Best-first search, implementation 131—136
Bhaskar, R. 769
Bidirectional associative memory 702—706
Binary resolution 567 573—578 597
Binding 39 69—70 343 428
Blackboard architecture 46 160 196—199
Blake, A. 519
Bledsoe, W. 30 566 599—600
Blocks world 22 26 37 62—63 188—196 386—389 520—521 608—612
Bobrow, D. 293 335
Boole, G. 8 29
Boolean algebra 8
Bottom-up parsing 526
Bower, G.H. 301 334
Boyer.R.S. 19 30 600
Brachman, R. 79 205 304 328 330 333 335 776
Branch and bound 92
Branching factor 96 153—154 182
Breadth-first search 99—105 159 162 174 383—384 459—461 560—561 579—580
Bridges of Koenigsberg problem 7 82—84 121
Brooks, R.A. 24 715 741—742 747 765 770 777
Brown, J.S. 232 245 649—650 768
Brown, P. 550
Brownston, L. 184 199
Buchanan, B.G. 20 245 263 287 289 639
Bucket brigade algorithm 725—730
Bundy, A. 79 335 768—769
Burks, A.W. 740 747
Burroughs, W.S. 753
Burstall, R.M. 9
Burton, R.R. 245 768
Byron, A. 4 17
Candidate elimination algorithm 613—620
Car/cdr recursion 441—443
Carbonell, J.G. 239 659
Carnap, R. 778
Carroll, L. 425
CART trees 546—547
Case frame 540
Case-based reasoning 206 209—210 235—242
Case-based reasoning, case adaptation 237
Case-based reasoning, case retrieval 236—238
Case-grammars 555
Casey 235
CASNET 266—267 290
Category formation 605
Category utility 656
Causal influence measure 252
Causal networks 266—268
CBR see “Case-based reasoning”
Ceccato, S. 301
Cellular automata 713—714 736—747
Certainty factor algebra see “Certainty theory”
Certainty theory 206 249 263—266 402—411
Chang, C.L. 81 569 579 600
Chapman, D. 24 777
Charniak, E. 121 283 289 328 556
Checkers 18 127 147—148
Chess 18 42—44 164—170
Chomsky hierarchy 531—533 556
Chomsky, N. 532
Chorfas, D.N. 245
Chronological backtracking 276
Church, A. 351 775
Circumscription 249 274—275
Clancy, W.J. 231 768
Clarke, A.C. 17 713
Classifier systems 715 725—730
Classifier systems and production systems 726—728
Classifier systems, bucket brigade algorithm 725—730
Classifier systems, condition strength 728—730
Classifier systems, fitness function 727
Classifier systems, fitness measures 714—719 727 735—736 744
Clause form 567—573 593
CLIPS 174
Clocksin, W.F. 350 358 421
CLOS 347 497—511
CLOS, class precedence list 503—505
CLOS, defining classes 499
CLOS, function, defclass 499—501
CLOS, function, defgeneric 501—503
CLOS, function, defmethod 503
CLOS, generic functions 498—503
CLOS, inheritance 503—505
CLOS, meta-classes 505
CLOS, multiple inheritance 503—505
CLOS, simulation 505—511
CLOS, slot options 499—501
CLOS, slot-specifiers 499—501
CLOS, thermostat simulation 505—511
Closed world assumption 235 270 273—274 360 592—593
CLUSTER/2 605 635 773
CNF Satisfaction 717—719
COBWEB 605 769
Codd, E.F. 741
CoG 765
Cognitive neuroscience 759
Cognitive science 23 30 760—762 766—770 776 779
Cohen, PR. 199
Coincidence learning see “Hebbian learning”
Colby, K.M. 30 304
Collins, A. 298 334—335 767
Colmerauer, A. 350
Common LISP Object System see “CLOS”
Commonsense reasoning 17 21 213 521
Commutative law 51
Competitive learning 662 682—690
Completeness 65—67 566
complexity 88—92 105—106 124—127 152—155
Complexity and Bayesian reasoning 251
Composition of substitutions 69—70
Concept learning 605
Concept space 606—608
Conceptual clustering 605—606
Conceptual dependencies 294 305—309 324—328 556
Conceptual graphs 309—320 334 534 538—543 551—556 756
Conceptual graphs and frames 321
Conceptual graphs and modal logic 318—320
Conceptual graphs and predicate calculus 318—320
Conceptual graphs, absurd type 314
Conceptual graphs, canonical formation rules 316
Conceptual graphs, concepts 309—310
Conceptual graphs, conceptual relations 309—310
Conceptual graphs, copy rule 314
Conceptual graphs, existential quantification 318—319
Conceptual graphs, generalization 314—316
Conceptual graphs, individuals 311—312
Conceptual graphs, inheritance 314—316
Conceptual graphs, join 314—317
Conceptual graphs, marker 311—312
Conceptual graphs, names 311—312
Conceptual graphs, proposition nodes 316—318
Conceptual graphs, quantification 318—320
Conceptual graphs, referent 312
Conceptual graphs, restrict 314—316
Conceptual graphs, simplify 314
Conceptual graphs, specialization 314—316
Conceptual graphs, subtype 311—313
Conceptual graphs, type hierarchy 313—316
Conceptual graphs, type lattice 313
Conceptual graphs, types 311—312
Conceptual graphs, universal quantification 318—319
Conceptual graphs, universal type 314
Conceptual models 216—219
Condition 171—186
conflict resolution 171—186
Conflict resolution, recency 184
Conflict resolution, refraction 184
Conflict resolution, specificity 184
Conflict set 171—186
Conjunction 48—50 56 59—60 568
Conjunctive normal form 569—572
Connectionist models of intelligence 15 26—27 518 603—604 661—712 759—761 773—774
Connectionist models of intelligence, activation level 664
Connectionist models of intelligence, associative memory 696—711
Connectionist models of intelligence, attractor 701
Connectionist models of intelligence, attractor network 663 701—711
Connectionist models of intelligence, autoassociative memory 663 696—711
Connectionist models of intelligence, backpropagation 662 668 675—682 771
Connectionist models of intelligence, backpropagation & exclusive or 681—682
Connectionist models of intelligence, bidirectional associative memory 702—706
Connectionist models of intelligence, classification 668 672
Connectionist models of intelligence, competitive learning 662 682—690
Connectionist models of intelligence, counter propagation 663 683 686—690
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