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人工智能：一种现代的方法）
- 出版社:读买天下图书专营店
- 出版时间:2011-07
- 热度:6897
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| 商品基本信息，请以下列介绍为准 | |
| 商品名称： | 人工智能：一种现代的方法（第3版）（大学计算机教育国外教材系列（影印版）） |
| 作者： | StuartJ.Russell，PeterNorvig著 |
| 定价： | 158.0 |
| 出版社： | 清华大学出版社 |
| 出版日期： | 2011-07-01 |
| ISBN： | 9787302252955 |
| 印次： | 1 |
| 版次： | 1 |
| 装帧： | |
| 开本： | 大32开 |
| 目录 | |
| Ⅰ artificial intelligence 1 introduction 1.1what is al? 1.2the foundations of artificial intelligence 1.3the history of artificial intelligence 1.4the state of the art ummary, bibliographical and historical notes, exerciser> 2 intelligent agentr>2.1agents and environmentr>2.2good behavior: the concept of rationality 2.3the nature of environmentr>2.4the structure of agentr>2.5summary, bibliographical and historical notes, exerciser> Ⅱ problem-solving 3 solving problemy searching 3.1problem-solving agentr>3.2example problemr>3.3searching for solutionr>3.4uninformed search strategier>3.5informed (heuristic) search strategier>3.6heuristic functionr>3.7summary, bibliographical and historical notes, exerciser> 4 beyond classical search 4.1local search algorithms and optimization problemr>4.2local search in continuous spacer>4.3searching with nondeterministic actionr>4.4searching with partial observationr>4.5online search agents and unknown environmentr>4.6summary, bibliographical and historical notes, exerciser> 5 adversarial search 5.1gamer>5.2optimal decisions in gamer>5.3alpha-beta pruning 5.4imperfect real-time decisionr>5.5stochastic gamer>5.6partially observable gamer>5.7state-of-the-art game programr>5.8alternative approacher>5.9summary, bibliographical and historical notes, exerciser> 6 constraint satisfaction problemr>6.1defining constraint satisfaction problemr>6.2constraint propagation: inference in cspr>6.3backtra search for cspr>6.4local search for cspr>6.5the structure of problemr>6.6summary, bibliographical and historical notes, exerciser> Ⅲ knowledge, reasoning, and planning 7 logical agentr>7.1knowledge-based agentr>7.2the wumpus world 7.3logic 7.4propositional logic: a very simple logic 7.5propositional theorem proving 7.6effective propositional model che 7.7agentased on propositional logic 7.8summary, bibliographical and historical notes, exerciser> 8 first-order logic 8.1representation revisited 8.2syntax and semantics of first-order logic 8.3using first-order logic 8.4knowledge engineering in first-order logic 8.5summary, bibliographical and historical notes, exerciser> 9 inference in first-order logic 9.1propositional vs. first-order inference 9.2unification and lifting 9.3forward chaining 9.4backward chaining 9.5resolution 9.6summary, bibliographical and historical notes, exerciser> 10 classical planning 10.1 definition of classical planning 10.2 algorithms for planning as state-space search 10.3 planning graphr>10.4 other classical planning approacher>10.5 analysis of planning approacher>10.6 summary, bibliographical and historical notes, exerciser> 11 planning and acting in the real world 11.1 time, schedules, and resourcer>11.2 hierarchical planning 11.3 planning and acting in nondeterministic domainr>11.4 multiagent planning 11.5 summary, bibliographical and historical notes, exerciser> 12 knowledge representation 12.1 ontological engineering 12.2 categories and objectr>12.3 eventr>12.4 mental events and mental objectr>12.5 reasoning systems for categorier>12.6 reasoning with default information 12.7 the intemet shopping world 12.8 summary, bibliographical and historical notes, exerciser> Ⅳ uncertain knowledge and reasoning 13 quantifying uncertainty 13.1 acting under uncertainty 13.2 basic probability notation 13.3 inference using full joint distributionr>13.4 independence 13.5 bayes' rule and its use 13.6 the wumpus world revisited 13.7 summary, bibliographical and historical notes, exerciser> 14 probabilistic reasoning 14.1 representing knowledge in an uncertain domain 14.2 the semantics of bayesian networkr>14.3 efficient representation of conditional distributionr>14.4 exact inference in bayesian networkr>14.5 approximate inference in bayesian networkr>14.6 relational and first-order probability modelr>14.7 other approaches to uncertain reasoning 14.8 summary, bibliographical and historical notes, exerciser> 15 probabilistic reasoning over time 15.1 time and uncertainty 15.2 inference in temporal modelr>15.3 hen markov modelr>15.4 kalman filterr>15.5 dynamic bayesian networkr>15.6 keeping track of many objectr>15.7 summary, bibliographical and historical notes, exerciser> 16 m simple decisionr>16.1 combining beliefs and desires under uncertainty 16.2 the basis of utility theory 16.3 utility functionr>16.4 multiattribute utility functionr>16.5 decision networkr>16.6 the value of information 16.7 decision-theoretic expert systemr>16.8 summary, bibliographical and historical notes, exerciser> 17 m complex decisionr>17.equential decision problemr>17.2 value iteration 17.3 policy iteration 17.4 partially observable mdpr>17.5 decisions with multiple agents: game theory 17.6 mechanism design 17.7 summary, bibliographical and historical notes, exerciser> V learning 18 learning from exampler>18.1 forms of learning 18.2 supervised learning 18.3 leaming decision treer>18.4 evaluating and choosing the best hypothesir>18.5 the theory of learning 18.6 regression and classification with linear modelr>18.7 artificial neural networkr>18.8 nonparametric modelr>18.9 support vector machiner>18.10 enle learning 18.11 practical machine learning 18.ummary, bibliographical and historical notes, exerciser> 19 knowledge in learning 19.1 a logical formulation of learning 19.2 knowledge in learning 19.3 explanation-based learning 19.4 learning using relevance information 19.5 inductive logic programming 19.6 summary, bibliographical and historical notes, exercir> 20 learning probabilistic modelr>20.tatistical learning 20.2 learning with complete data 20.3 learning with hen variables: the em algorithm. 20.4 summary, bibliographical and historical notes, exercir> 21 reinforcement learning 21. l introduction 21.2 passive reinforcement learning 21.3 active reinforcement learning 21.4 generalization in reinforcement learning 21.5 policy search 21.6 applications of reinforcement learning 21.7 summary, bibliographical and historical notes, exercir> VI communicating, perceiving, and acting 22 natural language processing 22.1 language modelr>22.2 text classification 22.3 information retrieval 22.4 information extraction 22.5 summary, bibliographical and historical notes, exercir> 23 natural language for communication 23.1 phrase structure grammarr>23.2 syntactic analysis (parsing) 23.3 augmented grammars and semantic interpretation 23.4 machine translation 23.5 speech recognition 23.6 summary, bibliographical and historical notes, exercir> 24 perception 24.1 image formation 24.2 early image-processing operationr>24.3 object recognition by appearance 24.4 reconstructing the 3d world 24.5 object recognition from structural information 24.6 using vision 24.7 summary, bibliographical and historical notes, exerciser> 25 roboticr>25.1 introduction 25.2 robot hardware 25.3 robotic perception 25.4 planning to move 25.5 planning uncertain movementr>25.6 moving 25.7 robotic software architecturer>25.8 application domainr>25.9 summary, bibliographical and historical notes, exerciser> VII conclusionr>26 philosophical foundationr>26.1 weak ai: can machines act intelligently? 26.2 strong ai: can machines really think? 26.3 the ethics and risks of developing artificial intelligence 26.4 summary, bibliographical and historical notes, exerciser> 27 al: the present and future 27.1 agent componentr>27.2 agent architecturer>27.3 are we going in the right direction? 27.4 what if ai does succeed? a mathematical background a. 1complexity analysis and o0 notation a.2 vectors, matrices, and linear algebra a.3 probability distributionr>b notes on languages and algorithmr>b.1defining languages with backus-naur form (bnf) b.2describing algorithms with pseudocode b.3online help bibliography index |