Uncertainty in Artificial Intelligence - 1st Edition - ISBN: 9781483282879

Uncertainty in Artificial Intelligence

1st Edition

Proceedings of the Eighth Conference (1992), July 17–19, 1992, Eighth Conference on Uncertainty in Artificial Intelligence, Stanford University

Editors: Didier Dubois Michael P. Wellman Bruce D'Ambrosio
eBook ISBN: 9781483282879
Imprint: Morgan Kaufmann
Published Date: 12th May 2014
Page Count: 378
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Description

Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (1992) covers the papers presented at the Eighth Conference on Uncertainty in Artificial Intelligence, held at Stanford University on July 17-19, 1992. The book focuses on the processes, methodologies, technologies, and approaches involved in artificial intelligence.

The selection first offers information on Relative Evidential Support (RES), modal logics for qualitative possibility and beliefs, and optimizing causal orderings for generating DAGs from data. Discussions focus on reversal, swap, and unclique operators, modal representation of possibility, and beliefs and conditionals. The text then examines structural controllability and observability in influence diagrams, lattice-based graded logic, and dynamic network models for forecasting.

The manuscript takes a look at reformulating inference problems through selective conditioning, entropy and belief networks, parallelizing probabilistic inference, and a symbolic approach to reasoning with linguistic quantifiers. The text also ponders on sidestepping the triangulation problem in Bayesian net computations; exploring localization in Bayesian networks for large expert systems; and expressing relational and temporal knowledge in visual probabilistic networks.

The selection is a valuable reference for researchers interested in artificial intelligence.

Table of Contents


RES—A Relative Method for Evidential Reasoning

Optimizing Causal Orderings for Generating DAGs from Data

Modal Logics for Qualitative Possibility and Beliefs

Structural Controllability and Observability in Influence Diagrams

Lattice-Based Graded Logic: A Multimodal Approach

Dynamic Network Models for Forecasting

Reformulating Inference Problems Through Selective Conditioning

Entropy and Belief Networks

Parallelizing Probabilistic Inference: Some Early Explorations

Objection-Based Causal Exception Networks

A Symbolic Approach to Reasoning with Linguistic Quantifiers

Possibilistic Assumption Based Truth Maintenance System, Validation in a Data Fusion Application

An Entropy-Based Learning Algorithm of Bayesian Conditional Trees

Knowledge Integration for Conditional Probability Assessments

Integrating Model Construction and Evaluation

Reasoning with Qualitative Probabilities Can Be Tractable

A Computational Scheme for Reasoning in Dynamic Probabilistic Networks

The Dynamic of Belief in the Transferable Belief Model and Specialization-Generalization Matrices

A Note on the Measure of Discord

Semantics for Probabilistic Inference

Some Problems for Convex Bayesians

Bayesian Meta-Reasoning: Determining Model Adequacy from within a Small World

The Bounded Bayesian

Representing Context-Sensitive Knowledge in a Network Formalism: A Preliminary Report

A Probabilistic Network of Predicates

Representing Heuristic Knowledge in D-S Theory

The Topological Fusion of Bayes Nets

Calculating Uncertainty Intervals from Conditional Convex Sets of Probabilities

Sensor Validation Using Dynamic Belief Networks

Empirical Probabilities in Monadic Deductive Databases

aHUGIN: A System Creating Adaptive Causal Probabilistic Networks

MESA: Maximum Entropy by Simulated Annealing

Decision Methods for Adaptive Task-Sharing in Associate Systems

Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis

Guess-and-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems

R&D Analyst: An Interactive Approach to Normative Decision System Model Construction

Possibilistic Constraint Satisfaction Problems or "How to Handle Soft Constraints?Decision Making Using Probabilistic Inference Methods

Conditional Independence in Uncertainty Theories

The Nature of the Unnormalized Beliefs Encountered in the Transferable Belief Model

Intuitions About Ordered Beliefs Leading to Probabilistic Models

Expressing Relational and Temporal Knowledge in Visual Probabilistic Networks

A Fuzzy Logic Approach to Target Tracking

Towards Precision of Probabilistic Bounds Propagation

An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation

Generalizing Jeffrey Conditionalization

Interval Structure: A Framework for Representing Uncertain Information

Exploring Localization in Bayesian Networks for Large Expert Systems

A Decision Calculus for Belief Functions in Valuation-Based Systems

Sidestepping the Triangulation Problem in Bayesian Net Computations

Author Index

Details

No. of pages:
378
Language:
English
Copyright:
© Morgan Kaufmann 1992
Published:
Imprint:
Morgan Kaufmann
eBook ISBN:
9781483282879

About the Editor

Didier Dubois

Affiliations and Expertise

Directeur de Recherches CNRS, Paris, Laboratoire IRIT and Université Paul Sabatier, Toulouse, France

Michael P. Wellman

Bruce D'Ambrosio

Ratings and Reviews