Uncertainty in Artificial Intelligence

Uncertainty in Artificial Intelligence

Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, The Catholic University of America, Washington, D.C. 1993

1st Edition - November 5, 1993

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  • Editors: David Heckerman, Abe Mamdani
  • eBook ISBN: 9781483214511

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Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.

Table of Contents

  • Preface



    Causality in Bayesian Belief Networks

    From Conditional Oughts to Qualitative Decision Theory

    Applications and Empirical Comparisons

    A Probabilistic Algorithm for Calculating Structure: Borrowing from Simulated Annealing

    A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification

    Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams

    End-User Construction of Influence Diagrams for Bayesian Statistics

    On Considering Uncertainty and Alternatives in Low-Level Vision (Outstanding Student Paper)

    Forecasting Sleep Apnea with Dynamic Network Models

    Normative Engineering Risk Management Systems

    Diagnosis of Multiple Faults: A Sensitivity Analysis

    Knowledge Acquisition, Modelling, and Explanation

    Additive Belief-Network Models

    Parameter Adjustment in Bayes Networks. The Generalized Noisy OR- Gate

    A Fuzzy Relation-Based Extension of Reggia's Relational Model for Diagnosis Handling Uncertain and Incomplete Information

    Dialectic Reasoning with Inconsistent Information

    Causal Independence for Knowledge Acquisition and Inference

    Utility-Based Abstraction and Categorization

    Sensitivity Analysis for Probability Assessments in Bayesian Networks

    Causal Modeling

    Some Complexity Considerations in the Combination of Belief Networks

    Deriving A Minimal I-Map of a Belief Network Relative to a Target Ordering of its Nodes

    Probabilistic Conceptual Network: A Belief Representation Scheme for Utility-Based Categorization

    Reasoning about the Value of Decision-Model Refinement: Methods and Application

    Mixtures of Gaussians and Minimum Relative Entropy Techniques for Modeling Continuous Uncertainties

    Valuation Networks and Conditional Independence

    Relevant Explanations: Allowing Disjunctive Assignments

    A Generalization of the Noisy-Or Model

    Automated Model Construction and Learning

    Using First-Order Probability Logic for the Construction of Bayesian Networks

    Representing and Reasoning With Probabilistic Knowledge: A Bayesian Approach

    Graph-Grammar Assistance for Automated Generation of Influence Diagrams

    Using Causal Information and Local Measures to Learn Bayesian Networks (Outstanding Student Paper)

    Minimal Assumption Distribution Propagation in Belief Networks

    An Algorithm for the Construction of Bayesian Network Structures from Data (Outstanding Student Paper)

    A Construction of Bayesian Networks from Databases Based on an MDL Principle

    Knowledge-Based Decision Model Construction for the Hierarchical Diagnosis: A Preliminary Report

    Algorithms for Inference and Decision Making

    A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging

    An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks

    Incremental Probabilistic Inference

    Deliberation Scheduling for Time-Critical Sequential Decision Making

    Intercausal Reasoning with Uninstantiated Ancestor Nodes

    Inference Algorithms for Similarity Networks

    Two Procedures for Compiling Influence Diagrams

    An Efficient Approach for Finding the MPE in Belief Networks

    A Method for Planning Given Uncertain and Incomplete Information

    The Use of Conflicts in Searching Bayesian Networks

    GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks

    Using Tree-Decomposable Structures to Approximate Belief Networks

    Using Potential Influence Diagrams for Probabilistic Inference and Decision Making

    Deciding Morality of Graphs is NP-Complete

    Incremental Computation of the Value of Perfect Information in Stepwise-Decomposable Influence Diagrams

    Qualitative Reasoning

    Argumentative Inference in Uncertain and Inconsistent Knowledge Bases

    Argument Calculus and Networks

    Argumentation as a General Framework for Uncertain Reasoning

    On Reasoning in Networks with Qualitative Uncertainty

    Qualitative Measures of Ambiguity

    Interpretation and Comparison of Approaches for Reasoning Under Uncertainty

    A Bayesian Variant of Shafer's Commonalities For Modelling Unforeseen Events

    The Probability of a Possibility: Adding Uncertainty to Default Rules

    Possibilistic Decreasing Persistence

    Discounting and Combination Operations in Evidential Reasoning

    Probabilistic Assumption-Based Reasoning

    Partially Specified Belief Functions

    Jeffrey's Rule of Conditioning Generalized to Belief Functions

    Inference with Possibilistic Evidence

    Constructing Lower Probabilities

    Belief Revision in Probability Theory

    The Assumptions Behind Dempster's Rule

    A Belief-Function Based Decision Support System

    Author Index

Product details

  • No. of pages: 552
  • Language: English
  • Copyright: © Morgan Kaufmann 2014
  • Published: November 5, 1993
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9781483214511

About the Editors

David Heckerman

Abe Mamdani

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