## Foundations of Decision-Making Agents: Logic, Probability and ModalityThis 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. Sample Chapter(s). Chapter 1: Modeling Agent Epistemic States: An Informal Overview (202 KB). Contents: Modeling Agent Epistemic States: An Informal Overview; Mathematical Preliminaries; Classical Logics for the Propositional Epistemic Model; Logic Programming; Logical Rules for Making Decisions; Bayesian Belief Networks; Influence Diagrams for Making Decisions; Modal Logics for the Possible World Epistemic Model; Symbolic Argumentation for Decision Making. Readership: Undergraduates and graduates majoring in artificial intelligence, computer professionals and researchers from the decision science community. |

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### Contents

An Informal Overview | 1 |

Chapter 2 Mathematical Preliminaries | 23 |

Chapter 3 Classical Logics for the Propositional Epistemic Model | 45 |

Chapter 4 Logic Programming | 97 |

Chapter 5 Logical Rules for Making Decisions | 143 |

Chapter 6 Bayesian Belief Networks | 165 |

Chapter 7 Influence Diagrams for Making Decisions | 237 |

Chapter 8 Modal Logics for the Possible World Epistemic Model | 255 |

Chapter 9 Symbolic Argumentation for DecisionMaking | 325 |

347 | |

355 | |

### Other editions - View all

Foundations of Decision-making Agents: Logic, Probability and Modality Subrata Kumar Das Limited preview - 2008 |

### Common terms and phrases

accessibility relation agent algorithm arguments atom axioms Bayesian belF belief network Cancelled Delayed chapter clique computed conditional independence conjunctive normal form d-separated decision options decision-making defined as follows definition degree of belief Dempster-Shafer theory denoted domain epistemic logic epistemic model equivalent Example Consider F BM finite first-order logic focal element formula F goal graph Heavy Rain Hence inference rule influence diagram interpretation join tree junction tree knowledge literal logic programming maximal consistent mCanDel modal logics modal operator modal system muddy negation network fragment node normal normal parent pGame polytree possible worlds posterior probability predicate symbol prior probabilities probabilistic problem procedure Prolog propagation propositional logic quantifier random variable represented resolution satisfiable semantics set of clauses shown in Figure Sprinkler Step subset sunny Suppose syntax theory unifiable unsatisfiable Utility values vector Weather world-path