Uncertainty in Artificial Intelligence - 1st Edition - ISBN: 9781483214511

Uncertainty in Artificial Intelligence

1st Edition

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

Editors: David Heckerman Abe Mamdani
eBook ISBN: 9781483214511
Imprint: Morgan Kaufmann
Published Date: 12th May 2014
Page Count: 552
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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




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


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Morgan Kaufmann
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About the Editor

David Heckerman

Abe Mamdani

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