LEADER 05510nam 2200601Ia 450 001 9910453551803321 005 20200520144314.0 010 $a1-281-93816-5 010 $a9786611938161 010 $a981-277-984-1 035 $a(CKB)1000000000538138 035 $a(OCoLC)560635834 035 $a(CaPaEBR)ebrary10255442 035 $a(SSID)ssj0000157509 035 $a(PQKBManifestationID)11179233 035 $a(PQKBTitleCode)TC0000157509 035 $a(PQKBWorkID)10138913 035 $a(PQKB)11696215 035 $a(MiAaPQ)EBC1681341 035 $a(WSP)00001870 035 $a(Au-PeEL)EBL1681341 035 $a(CaPaEBR)ebr10255442 035 $a(CaONFJC)MIL193816 035 $a(OCoLC)815752883 035 $a(EXLCZ)991000000000538138 100 $a20080116d2008 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFoundations of decision-making agents$b[electronic resource] $elogic, probability and modality /$fSubrata Das 210 $aSingapore ;$aHackensack, NJ $cWorld Scientific ;$a[London] $cImperial College Press$dc2008 215 $a1 online resource (385 p.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a981-277-983-3 320 $aIncludes bibliographical references (p. 347-353) and index. 327 $ach. 1. Modeling agent epistemic states: an informal overview. 1.1. Models of agent epistemic states. 1.2. Propositional epistemic model. 1.3. Probabilistic epistemic model. 1.4. Possible world epistemic model. 1.5. Comparisons of models. 1.6. P3 model for decision-making agents -- ch. 2. Mathematical preliminaries. 2.1. Usage of symbols. 2.2. Sets, relations, and functions. 2.3. Graphs and trees. 2.4. Probability. 2.5. Algorithmic complexity -- ch. 3. Classical logics for the propositional epistemic model. 3.1. Propositional logic. 3.2. First-order logic. 3.3. Theorem proving procedure. 3.4. Resolution theorem proving. 3.5. Refutation procedure. 3.6. Complexity analysis -- ch. 4. Logic programming. 4.1. The concept. 4.2. Program clauses and goals. 4.3. Program semantics. 4.4. Definite programs. 4.5. Normal programs. 4.6. Prolog. 4.7. Prolog systems. 4.8. Complexity analysis -- ch. 5. Logical rules for making decisions. 5.1. Evolution of rules. 5.2. Bayesian probability theory for handling uncertainty. 5.3. Dempster-Shafer theory for handling uncertainty. 5.4. Measuring consensus. 5.5. Combining sources of varying confidence. 5.6. Advantages and disadvantages of rule-based systems -- ch. 6. Bayesian belief networks. 6.1. Bayesian belief networks. 6.2. Conditional independence in belief networks. 6.3. Evidence, belief, and likelihood. 6.4. Prior probabilities in networks without evidence. 6.5. Belief revision. 6.6. Evidence propagation in polytrees. 6.7. Evidence propagation in directed acyclic graphs. 6.8. Complexity of inference algorithms. 6.9. Acquisition of probabilities. 6.10. Advantages and disadvantages of belief networks. 6.11. Belief network tools -- ch. 7. Influence diagrams for making decisions. 7.1. Expected utility theory and decision trees. 7.2. Influence diagrams. 7.3. Inferencing in influence diagrams. 7.4. Compilation of influence diagrams. 7.5. Inferencing in strong junction tress -- ch. 8. Modal logics for the possible world epistemic model. 8.1. Historical development of modal logics. 8.2. Systems of modal logic. 8.3. Deductions in modal systems. 8.4. Modality. 8.5. Decidability and matrix method. 8.6. Relationships among modal systems. 8.7. Possible world semantics. 8.8. Soundness and completeness results. 8.9. Complexity and decidability of modal systems. 8.10. Modal first-order logics. 8.11. Resolution in modal first-order logics. 8.12. Modal epistemic logics. 8.13. Logic of agents beliefs (LAB) -- ch. 9. Symbolic argumentation for decision-making. 9.1. Toulmin's model of argumentation. 9.2. Domino decision-making model for P3. 9.3. Knowledge representation syntax of P3. 9.4. Formalization of P3 via LAB. 9.5. Aggregation via Dempster-Shafer theory. 9.6. Aggregation via Bayesian belief networks. 330 $aThis 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. 606 $aIntelligent agents (Computer software) 606 $aArtificial intelligence$xComputer programs 608 $aElectronic books. 615 0$aIntelligent agents (Computer software) 615 0$aArtificial intelligence$xComputer programs. 676 $a006.33 700 $aDas$b Subrata Kumar$0932462 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910453551803321 996 $aFoundations of decision-making agents$92098359 997 $aUNINA