Foundations Of Decision-making Agents

Logic, Probability And Modality

By: Subrata Das

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Hardcover | 4 January 2008

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384 Pages

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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.

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You Can Find This Book In

Non-Fiction Business & Management Management & Management Techniques Management Decision Making Mathematics Mathematical Foundation Mathematical Logic Applied Mathematics Mathematical Modelling Computing & I.T.Computer Science Artificial Intelligence

Expert Systems & Knowledge-Based Systems Probability & Statistics Booktopia Publisher Services World Scientific Publishing

Preface	p. vii
Modeling Agent Epistemic States: An Informal Overview	p. 1
Models of Agent Epistemic States	p. 1
Propositional Epistemic Model	p. 3
Probabilistic Epistemic Model	p. 8
Possible World Epistemic Model	p. 12
Comparisons of Models	p. 16
p3 Model for Decision-Making Agents	p. 17
Mathematical Preliminaries	p. 23
Usage of Symbols	p. 23
Sets, Relations, and Functions	p. 24
Graphs and Trees	p. 29
Probability	p. 34
Algorithmic Complexity	p. 40
Further Readings	p. 44
Classical Logics for the Propositional Epistemic Model	p. 45
Propositional Logic	p. 46
First-Order Logic	p. 57
Theorem Proving Procedure	p. 71
Resolution Theorem Proving	p. 80
Refutation Procedure	p. 91
Complexity Analysis	p. 95
Further Readings	p. 96
Logic Programming	p. 97
The Concept	p. 97
Program Clauses and Goals	p. 99
Program Semantics	p. 106
Definite Programs	p. 108
Normal Programs	p. 114
Prolog	p. 121
Prolog Systems	p. 141
Complexity Analysis	p. 141
Further Readings	p. 142
Logical Rules for Making Decisions	p. 143
Evolution of Rules	p. 144
Bayesian Probability Theory for Handling Uncertainty	p. 146
Dempster-Shafer Theory for Handling Uncertainty	p. 150
Measuring Consensus	p. 157
Combining Sources of Varying Confidence	p. 162
Advantages and Disadvantages of Rule-Based Systems	p. 163
Background and Further Readings	p. 164
Bayesian Belief Networks	p. 165
Bayesian Belief Networks	p. 166
Conditional Independence in Belief Networks	p. 171
Evidence, Belief, and Likelihood	p. 179
Prior Probabilities in Networks without Evidence	p. 182
Belief Revision	p. 184
Evidence Propagation in Polytrees	p. 190
Evidence Propagation in Directed Acyclic Graphs	p. 211
Complexity of Inference Algorithms	p. 229
Acquisition of Probabilities	p. 230
Advantages and Disadvantages of Belief Networks	p. 234
Belief Network Tools	p. 235
Further Readings	p. 235
Influence Diagrams for Making Decisions	p. 237
Expected Utility Theory and Decision Trees	p. 237
Influence Diagrams	p. 240
Inferencing in Influence Diagrams	p. 242
Compilation of Influence Diagrams	p. 248
Inferencing in Strong Junction Tress	p. 252
Further Readings	p. 254
Modal Logics for the Possible World Epistemic Model	p. 255
Historical Development of Modal Logics	p. 256
Systems of Modal Logic	p. 262
Deductions in Modal Systems	p. 265
Modality	p. 272
Decidability and Matrix Method	p. 273
Relationships among Modal Systems	p. 277
Possible World Semantics	p. 279
Soundness and Completeness Results	p. 286
Complexity and Decidability of Modal Systems	p. 291
Modal First-Order Logics	p. 294
Resolution in Modal First-Order Logics	p. 300
Modal Epistemic Logics	p. 307
Logic of Agents Beliefs (LAB)	p. 309
Further Readings	p. 323
Symbolic Argumentation for Decision-Making	p. 325
Toulmin's Model of Argumentation	p. 327
Domino Decision-Making Model for P3	p. 328
Knowledge Representation Syntax of P3	p. 330
Formalization of P3 via LAB	p. 334
Aggregation via Dempster-Shafter Theory	p. 335
Aggregation via Bayesian Belief Networks	p. 339
Further Readings	p. 345
References	p. 347
Index	p. 355
Table of Contents provided by Ingram. All Rights Reserved.

Foundations Of Decision-making Agents

Logic, Probability And Modality

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Hardcover

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