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E-raamat: Foundations Of Decision-making Agents: Logic, Probability, And Modality

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(Charles River Analytics Inc., Usa)
  • Formaat: 384 pages
  • Ilmumisaeg: 03-Jan-2008
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • Keel: eng
  • ISBN-13: 9789814472180
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Foundations Of Decision-making Agents: Logic, Probability, And ModalitySuurem pilt
  • Formaat: 384 pages
  • Ilmumisaeg: 03-Jan-2008
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • Keel: eng
  • ISBN-13: 9789814472180
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This self-contained book provides three fundamental and generic approaches (logical, probabilistic, and modal) to representing and reasoning with agent epistemic states, specifically in the context of decision making. Each of these approaches can be applied to the construction of intelligent software agents for making decisions, thereby creating computational foundations for decision-making agents. In addition, the book introduces a formal integration of the three approaches into a single unified approach that combines the advantages of all the approaches. Finally, the symbolic argumentation approach to decision making developed in this book, combining logic and probability, offers several advantages over the traditional approach to decision making which is based on simple rule-based expert systems or expected utility theory.
Preface vii
1. Modeling Agent Epistemic States: An Informal Overview 1
1.1 Models of Agent Epistemic States
1
1.2 Propositional Epistemic Model
3
1.3 Probabilistic Epistemic Model
8
1.4 Possible World Epistemic Model
12
1.5 Comparisons of Models
16
1.6 P3 Model for Decision-Making Agents
17
2. Mathematical Preliminaries 23
2.1 Usage of Symbols
23
2.2 Sets, Relations, and Functions
24
2.3 Graphs and Trees
29
2.4 Probability
34
2.5 Algorithmic Complexity
40
2.6 Further Readings
44
3. Classical Logics for the Propositional Epistemic Model 45
3.1 Propositional Logic
46
3.2 First-Order Logic
57
3.3 Theorem Proving Procedure
71
3.4 Resolution Theorem Proving
80
3.5 Refutation Procedure
91
3.6 Complexity Analysis
95
3.7 Further Readings
96
4. Logic Programming 97
4.1 The Concept
97
4.2 Program Clauses and Goals
99
4.3 Program Semantics
106
4.4 Definite Programs
108
4.5 Normal Programs
114
4.6 Prolog
121
4.7 Prolog Systems
141
4.8 Complexity Analysis
141
4.9 Further Readings
142
5. Logical Rules for Making Decisions 143
5.1 Evolution of Rules
144
5.2 Bayesian Probability Theory for Handling Uncertainty
146
5.3 Dempster-Shafer Theory for Handling Uncertainty
150
5.4 Measuring Consensus
157
5.5 Combining Sources of Varying Confidence
162
5.6 Advantages and Disadvantages of Rule-Based Systems
163
5.7 Background and Further Readings
164
6. Bayesian Belief Networks 165
6.1 Bayesian Belief Networks
166
6.2 Conditional Independence in Belief Networks
171
6.3 Evidence, Belief, and Likelihood
179
6.4 Prior Probabilities in Networks without Evidence
182
6.5 Belief Revision
184
6.6 Evidence Propagation in Polytrees
190
6.7 Evidence Propagation in Directed Acyclic Graphs
211
6.8 Complexity of Inference Algorithms
229
6.9 Acquisition of Probabilities
230
6.10 Advantages and Disadvantages of Belief Networks
234
6.11 Belief Network Tools
235
6.12 Further Readings
235
7. Influence Diagrams for Making Decisions 237
7.1 Expected Utility Theory and Decision Trees
237
7.2 Influence Diagrams
240
7.3 Inferencing in Influence Diagrams
242
7.4 Compilation of Influence Diagrams
248
7.5 Inferencing in Strong Junction Tress
252
7.6 Further Readings
254
8. Modal Logics for the Possible World Epistemic Model 255
8.1 Historical Development of Modal Logics
256
8.2 Systems of Modal Logic
262
8.3 Deductions in Modal Systems
265
8.4 Modality
272
8.5 Decidability and Matrix Method
273
8.6 Relationships among Modal Systems
277
8.7 Possible World Semantics
279
8.8 Soundness and Completeness Results
286
8.9 Complexity and Decidability of Modal Systems
291
8.10 Modal First-Order Logics
294
8.11 Resolution in Modal First-Order Logics
300
8.12 Modal Epistemic Logics
307
8.13 Logic of Agents Beliefs (LAB)
309
8.14 Further Readings
323
9. Symbolic Argumentation for Decision-Making 325
9.1 Toulmin's Model of Argumentation
327
9.2 Domino Decision-Making Model for P3
328
9.3 Knowledge Representation Syntax of P3
330
9.4 Formalization of T3 via LAB
334
9.5 Aggregation via Dempster-Shafer Theory
335
9.6 Aggregation via Bayesian Belief Networks
339
9.7 Further Readings
345
References 347
Index 355
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