| Preface |
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vii | |
| 1. Modeling Agent Epistemic States: An Informal Overview |
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1 | |
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1.1 Models of Agent Epistemic States |
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1 | |
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1.2 Propositional Epistemic Model |
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3 | |
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1.3 Probabilistic Epistemic Model |
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8 | |
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1.4 Possible World Epistemic Model |
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12 | |
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1.5 Comparisons of Models |
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16 | |
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1.6 P3 Model for Decision-Making Agents |
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17 | |
| 2. Mathematical Preliminaries |
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23 | |
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23 | |
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2.2 Sets, Relations, and Functions |
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24 | |
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29 | |
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34 | |
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2.5 Algorithmic Complexity |
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40 | |
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44 | |
| 3. Classical Logics for the Propositional Epistemic Model |
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45 | |
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46 | |
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57 | |
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3.3 Theorem Proving Procedure |
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71 | |
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3.4 Resolution Theorem Proving |
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80 | |
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91 | |
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95 | |
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96 | |
| 4. Logic Programming |
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97 | |
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97 | |
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4.2 Program Clauses and Goals |
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99 | |
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106 | |
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108 | |
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114 | |
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121 | |
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141 | |
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141 | |
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142 | |
| 5. Logical Rules for Making Decisions |
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143 | |
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144 | |
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5.2 Bayesian Probability Theory for Handling Uncertainty |
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146 | |
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5.3 Dempster-Shafer Theory for Handling Uncertainty |
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150 | |
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157 | |
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5.5 Combining Sources of Varying Confidence |
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162 | |
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5.6 Advantages and Disadvantages of Rule-Based Systems |
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163 | |
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5.7 Background and Further Readings |
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164 | |
| 6. Bayesian Belief Networks |
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165 | |
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6.1 Bayesian Belief Networks |
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166 | |
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6.2 Conditional Independence in Belief Networks |
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171 | |
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6.3 Evidence, Belief, and Likelihood |
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179 | |
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6.4 Prior Probabilities in Networks without Evidence |
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182 | |
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184 | |
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6.6 Evidence Propagation in Polytrees |
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190 | |
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6.7 Evidence Propagation in Directed Acyclic Graphs |
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211 | |
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6.8 Complexity of Inference Algorithms |
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229 | |
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6.9 Acquisition of Probabilities |
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230 | |
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6.10 Advantages and Disadvantages of Belief Networks |
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234 | |
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6.11 Belief Network Tools |
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235 | |
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235 | |
| 7. Influence Diagrams for Making Decisions |
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237 | |
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7.1 Expected Utility Theory and Decision Trees |
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237 | |
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240 | |
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7.3 Inferencing in Influence Diagrams |
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242 | |
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7.4 Compilation of Influence Diagrams |
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248 | |
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7.5 Inferencing in Strong Junction Tress |
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252 | |
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254 | |
| 8. Modal Logics for the Possible World Epistemic Model |
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255 | |
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8.1 Historical Development of Modal Logics |
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256 | |
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8.2 Systems of Modal Logic |
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262 | |
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8.3 Deductions in Modal Systems |
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265 | |
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272 | |
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8.5 Decidability and Matrix Method |
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273 | |
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8.6 Relationships among Modal Systems |
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277 | |
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8.7 Possible World Semantics |
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279 | |
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8.8 Soundness and Completeness Results |
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286 | |
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8.9 Complexity and Decidability of Modal Systems |
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291 | |
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8.10 Modal First-Order Logics |
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294 | |
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8.11 Resolution in Modal First-Order Logics |
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300 | |
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8.12 Modal Epistemic Logics |
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307 | |
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8.13 Logic of Agents Beliefs (LAB) |
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309 | |
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323 | |
| 9. Symbolic Argumentation for Decision-Making |
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325 | |
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9.1 Toulmin's Model of Argumentation |
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327 | |
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9.2 Domino Decision-Making Model for P3 |
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328 | |
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9.3 Knowledge Representation Syntax of P3 |
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330 | |
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9.4 Formalization of T3 via LAB |
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334 | |
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9.5 Aggregation via Dempster-Shafer Theory |
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335 | |
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9.6 Aggregation via Bayesian Belief Networks |
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339 | |
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345 | |
| References |
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347 | |
| Index |
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355 | |