مشخصات کتاب

Artificial intelligence: a modern approach

ویرایش: 3rd ed. ; International ed 
نویسندگان: Davis. Ernest, Norvig. Peter, Russell. Stuart Jonathan  
سری: Prentice Hall series in artificial intelligence 
ISBN (شابک) : 9781408225745, 1408225751 
ناشر: Addison Wesley 
سال نشر: 2011 
تعداد صفحات: 501 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 10 مگابایت 

کلمات کلیدی مربوط به کتاب هوش مصنوعی: رویکردی مدرن: الگوریتم ها، هوش مصنوعی، هوش مصنوعی، منطق نمادین و ریاضی، منطق نمادین و ریاضی

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توضیحاتی درمورد کتاب به خارجی

27-4: What if AI does succeed? -- A: Mathematical background -- A-1: Complexity analysis and O() notation -- A-2: Vectors, matrices, and linear algebra -- A-3: Probability distributions -- B: Notes on languages and algorithms -- B-1: Defining languages with Backus-Naur form (BNF) -- B-2: Describing algorithms with pseudocode -- B-3: Online help -- Bibliography -- Index.;20-4: Summary, bibliographical and historical notes, exercises -- 21: Reinforcement learning -- 21-1: 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, exercises -- 6: Communicating, Perceiving, And Acting -- 22: Natural language processing -- 22-1: Language models -- 22-2: Text classification -- 22-3: Information retrieval -- 22-4: Information extraction -- 22-5: Summary, bibliographical and historical notes, exercises -- 23: Natural language for communication -- 23-1: Phrase structure grammars -- 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, exercises -- 24: Perception -- 24-1: Image formation.;11: Planning and acting in the real world -- 11-1: Time, schedules, and resources -- 11-2: Hierarchical planning -- 11-3: Planning and acting in nondeterministic domains -- 11-4: Multiagent planning -- 11-5: Summary, bibliographical and historical notes, exercises -- 12: Knowledge representation -- 12-1: Ontological engineering -- 12-2: Categories and objects -- 12-3: Events -- 12-4: Mental events and mental objects -- 12-5: Reasoning systems for categories -- 12-6: Reasoning with default information -- 12-7: Internet shopping world -- 12-8: Summary, bibliographical and historical notes, exercises -- 4: Uncertain Knowledge And Reasoning -- 13: Quantifying uncertainty -- 13-1: Acting under uncertainty -- 13-2: Basic probability notation -- 13-3: Inference using full joint distributions -- 13-4: Independence -- 13-5: Bayes' rule and its use -- 13-6: Wumpus world revisited -- 13-7: Summary, bibliographical and historical notes, exercises -- 14: Probabilistic reasoning.;7-5: Propositional theorem proving -- 7-6: Effective propositional model checking -- 7-7: Agents based on propositional logic -- 7-8: Summary, bibliographical and historical notes, exercises -- 8: First-order logic -- 8-1: Representation revisited -- 8-2: Syntax and semantics of first-order logic -- 8-3: Using first-order logic -- 8-4: Knowledge engineering in first-order logic -- 8-5: Summary, bibliographical and historical notes, exercises -- 9: Inference in first-order logic -- 9-1: Propositional vs first-order inference -- 9-2: Unification and lifting -- 9-3: Forward chaining -- 9-4: Backward chaining -- 9-5: Resolution -- 9-6: Summary, bibliographical and historical notes, exercises -- 10: Classical planning -- 10-1: Definition of classical planning -- 10-2: Algorithms for planning as state-space search -- 10-3: Planning graphs -- 10-4: Other classical planning approaches -- 10-5: Analysis of planning approaches -- 10-6: Summary, bibliographical and historical notes, exercises.;16-7: Decision-theoretic expert systems -- 16-8: Summary, bibliographical and historical notes, exercises -- 17: Making complex decisions -- 17-1: Sequential decision problems -- 17-2: Value iteration -- 17-3: Policy iteration -- 17-4: Partially observable MDPs -- 17-5: Decisions with multiple agents: game theory -- 17-6: Mechanism design -- 17-7: Summary, bibliographical and historical notes, exercises.;4-4: Searching with partial observations -- 4-5: Online search agents and unknown environments -- 4-6: Summary, bibliographical and historical notes, exercises -- 5: Adversarial search -- 5-1: Games -- 5-2: Optimal decisions in games -- 5-3: Alpha-beta pruning -- 5-4: Imperfect real-time decisions -- 5-5: Stochastic games -- 5-6: Partially observable games -- 5-7: State-of-the-art game programs -- 5-8: Alternative approaches -- 5-9: Summary, bibliographical and historical notes, exercises -- 6: Constraint satisfaction problems -- 6-1: Defining constraint satisfaction problems -- 6-2: Constraint propagation: inference in CSPs -- 6-3: Backtracking search for CSPs -- 6-4: Local search for CSPs -- 6-5: Structure of problems -- 6-6: Summary, bibliographical and historical notes, exercises -- 3: Knowledge. Reasoning And Planning -- 7: Logical agents -- 7-1: Knowledge-based agents -- 7-2: Wumpus world -- 7-3: Logic -- 7-4: Propositional logic: a very simple logic.;24-2: Early image-processing operations -- 24-3: Object recognition by appearance -- 24-4: Reconstructing the 3D world -- 24-5: Object recognition form structural information -- 24-6: Using vision -- 24-7: Summary, bibliographical and historical notes, exercises -- 25: Robotics -- 25-1: Introduction -- 25-2: Robot hardware -- 25-3: Robotic perception -- 25-4: Planning to move -- 25-5: Planning uncertain movements -- 25-6: Moving -- 25-7: Robotic software architectures -- 25-8: Application domains -- 25-9: Summary, bibliographical and historical notes, exercises -- 7: Conclusions -- 26: Philosophical foundations -- 26-1: Weak AI: can machines act intelligently? -- 26-2: Strong AI: can machines really think? -- 26-3: Ethics and risks of developing artificial intelligence -- 26-4: Summary, bibliographical and historical notes, exercises -- 27: AI: Present and future -- 27-1: Agent components -- 27-2: Agent architectures -- 27-3: Are we going in the right direction?;1: Artificial Intelligence -- 1: Introduction -- 1-1: What is AI? -- 1-2: Foundations of artificial intelligence -- 1-3: History of artificial intelligence -- 1-4: State of the art -- 1-5: Summary, bibliographical and historical notes, exercises -- 2: Intelligent agents -- 2-1: Agents and environments -- 2-2: Good behavior: the concepts of rationality -- 2-3: Nature of environments -- 2-4: Structure of agents -- 2-5: Summary, bibliographical and historical notes, exercises -- 2: Problem-Solving -- 3: Solving problems by searching -- 3-1: Problem-solving agents -- 3-2: Example problems -- 3-3: Searching for solutions -- 3-4: Uninformed search strategies -- 3-5: Informed (heuristic) search strategies -- 3-6: Heuristic functions -- 3-7: Summary, bibliographical and historical notes, exercises -- 4: Beyond classical search -- 4-1: Local search algorithms and optimization problems -- 4-2: Local search in continuous spaces -- 4-3: Searching with nondeterministic actions.;14-1: Representing knowledge in an uncertain domain -- 14-2: Semantics of Bayesian networks -- 14-3: Efficient representation of conditional distributions -- 14-4: Exact inference in Bayesian networks -- 14-5: Approximate inference in Bayesian networks -- 14-6: Relational and first-order probability models -- 14-7: Other approaches to uncertain reasoning -- 14-8: Summary, bibliographical and historical notes, exercises -- 15: Probabilistic reasoning over time -- 15-1: Time and uncertainty -- 15-2: Inference in temporal models -- 15-3: Hidden Markov models -- 15-4: Kalman filters -- 15-5: Dynamic Bayesian Networks -- 15-6: Keeping track of many objects -- 15-7: Summary, bibliographical and historical notes, exercises -- 16: Making simple decisions -- 16-1: Combining beliefs and desires under uncertainty -- 16-2: Basis of utility theory -- 16-3: Utility functions -- 16-4: Multiattribute utility functions -- 16-5: Decision networks -- 16-6: Value of information.;Learning -- 18: Learning from examples -- 18-1: Forms of learning -- 18-2: Supervised learning -- 18-3: Learning decision trees -- 18-4: Evaluating and choosing the best hypothesis -- 18-5: Theory of learning -- 18-6: Regression and classification with linear models -- 18-7: Artificial neural networks -- 18-8: Nonparametric models -- 18-9: Support vector machines -- 18-10: Ensemble learning -- 18-11: Practical machine learning -- 18-12: Summary, bibliographical and historical notes, exercises -- 19: Knowledge in learning -- 19-1: 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, exercises -- 20: Learning probabilistic models -- 20-1: Statistical learning -- 20-2: Learning with complete data -- 20-3: Learning with hidden variables: the EM algorithm.

فهرست مطالب

1: Artificial Intelligence --
1: Introduction --
1-1: What is AI? --
1-2: Foundations of artificial intelligence --
1-3: History of artificial intelligence --
1-4: State of the art --
1-5: Summary, bibliographical and historical notes, exercises --
2: Intelligent agents --
2-1: Agents and environments --
2-2: Good behavior: the concepts of rationality --
2-3: Nature of environments --
2-4: Structure of agents --
2-5: Summary, bibliographical and historical notes, exercises --
2: Problem-Solving --
3: Solving problems by searching --
3-1: Problem-solving agents --
3-2: Example problems --
3-3: Searching for solutions --
3-4: Uninformed search strategies --
3-5: Informed (heuristic) search strategies --
3-6: Heuristic functions --
3-7: Summary, bibliographical and historical notes, exercises --
4: Beyond classical search --
4-1: Local search algorithms and optimization problems --
4-2: Local search in continuous spaces --
4-3: Searching with nondeterministic actions. 4-4: Searching with partial observations --
4-5: Online search agents and unknown environments --
4-6: Summary, bibliographical and historical notes, exercises --
5: Adversarial search --
5-1: Games --
5-2: Optimal decisions in games --
5-3: Alpha-beta pruning --
5-4: Imperfect real-time decisions --
5-5: Stochastic games --
5-6: Partially observable games --
5-7: State-of-the-art game programs --
5-8: Alternative approaches --
5-9: Summary, bibliographical and historical notes, exercises --
6: Constraint satisfaction problems --
6-1: Defining constraint satisfaction problems --
6-2: Constraint propagation: inference in CSPs --
6-3: Backtracking search for CSPs --
6-4: Local search for CSPs --
6-5: Structure of problems --
6-6: Summary, bibliographical and historical notes, exercises --
3: Knowledge. Reasoning And Planning --
7: Logical agents --
7-1: Knowledge-based agents --
7-2: Wumpus world --
7-3: Logic --
7-4: Propositional logic: a very simple logic. 7-5: Propositional theorem proving --
7-6: Effective propositional model checking --
7-7: Agents based on propositional logic --
7-8: Summary, bibliographical and historical notes, exercises --
8: First-order logic --
8-1: Representation revisited --
8-2: Syntax and semantics of first-order logic --
8-3: Using first-order logic --
8-4: Knowledge engineering in first-order logic --
8-5: Summary, bibliographical and historical notes, exercises --
9: Inference in first-order logic --
9-1: Propositional vs first-order inference --
9-2: Unification and lifting --
9-3: Forward chaining --
9-4: Backward chaining --
9-5: Resolution --
9-6: Summary, bibliographical and historical notes, exercises --
10: Classical planning --
10-1: Definition of classical planning --
10-2: Algorithms for planning as state-space search --
10-3: Planning graphs --
10-4: Other classical planning approaches --
10-5: Analysis of planning approaches --
10-6: Summary, bibliographical and historical notes, exercises. 11: Planning and acting in the real world --
11-1: Time, schedules, and resources --
11-2: Hierarchical planning --
11-3: Planning and acting in nondeterministic domains --
11-4: Multiagent planning --
11-5: Summary, bibliographical and historical notes, exercises --
12: Knowledge representation --
12-1: Ontological engineering --
12-2: Categories and objects --
12-3: Events --
12-4: Mental events and mental objects --
12-5: Reasoning systems for categories --
12-6: Reasoning with default information --
12-7: Internet shopping world --
12-8: Summary, bibliographical and historical notes, exercises --
4: Uncertain Knowledge And Reasoning --
13: Quantifying uncertainty --
13-1: Acting under uncertainty --
13-2: Basic probability notation --
13-3: Inference using full joint distributions --
13-4: Independence --
13-5: Bayes' rule and its use --
13-6: Wumpus world revisited --
13-7: Summary, bibliographical and historical notes, exercises --
14: Probabilistic reasoning. 14-1: Representing knowledge in an uncertain domain --
14-2: Semantics of Bayesian networks --
14-3: Efficient representation of conditional distributions --
14-4: Exact inference in Bayesian networks --
14-5: Approximate inference in Bayesian networks --
14-6: Relational and first-order probability models --
14-7: Other approaches to uncertain reasoning --
14-8: Summary, bibliographical and historical notes, exercises --
15: Probabilistic reasoning over time --
15-1: Time and uncertainty --
15-2: Inference in temporal models --
15-3: Hidden Markov models --
15-4: Kalman filters --
15-5: Dynamic Bayesian Networks --
15-6: Keeping track of many objects --
15-7: Summary, bibliographical and historical notes, exercises --
16: Making simple decisions --
16-1: Combining beliefs and desires under uncertainty --
16-2: Basis of utility theory --
16-3: Utility functions --
16-4: Multiattribute utility functions --
16-5: Decision networks --
16-6: Value of information. 16-7: Decision-theoretic expert systems --
16-8: Summary, bibliographical and historical notes, exercises --
17: Making complex decisions --
17-1: Sequential decision problems --
17-2: Value iteration --
17-3: Policy iteration --
17-4: Partially observable MDPs --
17-5: Decisions with multiple agents: game theory --
17-6: Mechanism design --
17-7: Summary, bibliographical and historical notes, exercises. Learning --
18: Learning from examples --
18-1: Forms of learning --
18-2: Supervised learning --
18-3: Learning decision trees --
18-4: Evaluating and choosing the best hypothesis --
18-5: Theory of learning --
18-6: Regression and classification with linear models --
18-7: Artificial neural networks --
18-8: Nonparametric models --
18-9: Support vector machines --
18-10: Ensemble learning --
18-11: Practical machine learning --
18-12: Summary, bibliographical and historical notes, exercises --
19: Knowledge in learning --
19-1: 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, exercises --
20: Learning probabilistic models --
20-1: Statistical learning --
20-2: Learning with complete data --
20-3: Learning with hidden variables: the EM algorithm. 20-4: Summary, bibliographical and historical notes, exercises --
21: Reinforcement learning --
21-1: 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, exercises --
6: Communicating, Perceiving, And Acting --
22: Natural language processing --
22-1: Language models --
22-2: Text classification --
22-3: Information retrieval --
22-4: Information extraction --
22-5: Summary, bibliographical and historical notes, exercises --
23: Natural language for communication --
23-1: Phrase structure grammars --
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, exercises --
24: Perception --
24-1: Image formation. 24-2: Early image-processing operations --
24-3: Object recognition by appearance --
24-4: Reconstructing the 3D world --
24-5: Object recognition form structural information --
24-6: Using vision --
24-7: Summary, bibliographical and historical notes, exercises --
25: Robotics --
25-1: Introduction --
25-2: Robot hardware --
25-3: Robotic perception --
25-4: Planning to move --
25-5: Planning uncertain movements --
25-6: Moving --
25-7: Robotic software architectures --
25-8: Application domains --
25-9: Summary, bibliographical and historical notes, exercises --
7: Conclusions --
26: Philosophical foundations --
26-1: Weak AI: can machines act intelligently? --
26-2: Strong AI: can machines really think? --
26-3: Ethics and risks of developing artificial intelligence --
26-4: Summary, bibliographical and historical notes, exercises --
27: AI: Present and future --
27-1: Agent components --
27-2: Agent architectures --
27-3: Are we going in the right direction? 27-4: What if AI does succeed? --
A: Mathematical background --
A-1: Complexity analysis and O() notation --
A-2: Vectors, matrices, and linear algebra --
A-3: Probability distributions --
B: Notes on languages and algorithms --
B-1: Defining languages with Backus-Naur form (BNF) --
B-2: Describing algorithms with pseudocode --
B-3: Online help --
Bibliography --
Index.

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