Uncertainty in Artificial Intelligence

Uncertainty in Artificial Intelligence

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

1st Edition - July 1, 1992

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  • Editors: Didier Dubois, Michael P. Wellman, Bruce D'Ambrosio
  • eBook ISBN: 9781483282879

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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

Product details

  • No. of pages: 378
  • Language: English
  • Copyright: © Morgan Kaufmann 2014
  • Published: July 1, 1992
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9781483282879

About the Editors

Didier Dubois

Affiliations and Expertise

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

Michael P. Wellman

Bruce D'Ambrosio

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