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
- Editors: Didier J. Dubois, Michael P. Wellman, Bruce D'Ambrosio
- Language: English
- Paperback ISBN:9 7 8 - 1 - 5 5 8 6 0 - 2 5 8 - 8
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 8 2 8 7 - 9
Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (1992) covers the papers presented at the Eighth Conference on Uncertainty in Artificial Intelligence,… Read more
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Request a sales quoteUncertainty 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.
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
- No. of pages: 378
- Language: English
- Edition: 1
- Published: July 1, 1992
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9781558602588
- eBook ISBN: 9781483282879
DD
Didier J. Dubois
Affiliations and expertise
Directeur de Recherches CNRS, Paris, Laboratoire IRIT and Université Paul Sabatier, Toulouse, FranceRead Uncertainty in Artificial Intelligence on ScienceDirect