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Uncertainty in Artificial Intelligence: Proceedings of the Seventh Conference (1991) covers the papers presented at the Seventh Conference on Uncertainty in Artificial Intelligence, held on July 13-15, 1991 at the University of California at Los Angeles (UCLA). The book focuses on the processes, technologies, developments, and approaches involved in artificial intelligence.
The selection first offers information on combining multiple-valued logics in modular expert systems; constraint propagation with imprecise conditional probabilities; and Bayesian networks applied to therapy monitoring. The text then examines some properties of plausible reasoning; theory refinement on Bayesian networks; combination of upper and lower probabilities; and a probabilistic analysis of marker-passing techniques for plan-recognition.
The publication ponders on symbolic probabilistic inference (SPI) with continuous variables, SPI with evidence potential, and local expression languages for probabilistic dependence. Topics include local expression languages for probabilistic knowledge, evidence potential algorithm, symbolic inference with evidence potential, and SPI with continuous variables algorithm. The manuscript also takes a look at the compatibility of quantitative and qualitative representations of belief and a method for integrating utility analysis into an expert system for design evaluation under uncertainty.
The selection is a valuable source of data for researchers interested in artificial intelligence.
ARCO1: An Application of Belief Networks to the Oil Market
"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models
Combining Multiple-Valued Logics in Modular Expert Systems
Constraint Propagation with Imprecise Conditional Probabilities
Bayesian Networks Applied to Therapy Monitoring
Some Properties of Plausible Reasoning
Theory Refinement on Bayesian Networks
Combination of Upper and Lower Probabilities
A Probabilistic Analysis of Marker-Passing Techniques for Plan-Recognition
Symbolic Probabilistic Inference with Continuous Variables
Symbolic Probabilistic Inference with Evidence Potential
A Bayesian Method for Constructing Bayesian Belief Networks from Databases
Local Expression Languages for Probabilistic Dependence
Decision Theory and Autonomous Systems
A Reason Maintenance System Dealing with Vague Dataira
Advances in Probabilistic Reasoning
Probability Estimation in Face of Irrelevant Information
An Approximate Nonmyopic Computation for Value of Information
Search-Based Methods to Bound Diagnostic Probabilities in Very Large Belief Nets
Time-Dependent Utility and Action Under Uncertainty
Non-Monotonic Reasoning and the Reversibility of Belief Change
Belief and Surprise - A Belief-Function Formulation
Evidential Reasoning in a Categorial Perspective
Reasoning with Mass Distributions
A Logic of Graded Possibility and Certainty Coping with Partial Inconsistency
Conflict and Surprise: Heuristics for Model Revision
Reasoning Under Uncertainty: Some Monte Carlo Results
Representation Requirements for Supporting Decision Model Formulation
A Language for Planning with Statistics
A Modification to Evidential Probability
Investigation of Variances in Belief Networks
A Sensitivity Analysis of Pathfinder: A Follow-Up Study
Non-Monotonic Negation in Probabilistic Deductive Databases
Management of Uncertainty
Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm
Representing Bayesian Networks Within Probabilistic Horn Abduction
Dynamic Network Updating Techniques for Diagnostic Reasoning
High Level Path Planning with Uncertainty
Formal Model of Uncertainty for Possibilistic Rules
Deliberation and its Role in the Formation of Intentions
Handling Uncertainty During Plan Recognition in Task-Oriented Consultation Systems
Truth as Utility: A Conceptual Synthesis
Pulcinella: A General Tool for Propagating Uncertainty in Valuation Networks
Structuring Bodies of Evidence
On the Generation of Alternative Explanations with Implications for Belief Revision
Completing Knowledge by Competing Hierarchies
A Graph-Based Inference Method for Conditional Independence
A Fusion Algorithm for Solving Bayesian Decision Problems
Algorithms for Irrelevance-Based Partial MAPs
Compressed Constraints in Probabilistic Logic and Their Revision
Detecting Causal Relations in the Presence of Unmeasured Variables
A Method for Integrating Utility Analysis
From Relational Databases to Belief Networks
A Monte-Carlo Algorithm for Dempster-Shafer Belief
Compatibility of Quantitative and Qualitative Representations of Belief
An Efficient Implementation of Belief Function Propagation
A Non-Numeric Approach to Multi-Criteria/Multi-Expert Aggregation Based on Approximate Reasoning
Why Do We Need Foundations for Modelling Uncertainties?
- No. of pages:
- © Morgan Kaufmann 1991
- 1st July 1991
- Morgan Kaufmann
- eBook ISBN:
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