Uncertainty in Artificial Intelligence
Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, The Catholic University of America, Washington, D.C. 1993
- 1st Edition - November 5, 1993
- Imprint: Morgan Kaufmann
- Editors: David Heckerman, Abe Mamdani
- Language: English
- Paperback ISBN:9 7 8 - 1 - 5 5 8 6 0 - 3 0 6 - 6
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 1 4 5 1 - 1
Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in… Read more
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Request a sales quoteUncertainty 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.
Preface
Acknowledgements
Foundations
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
- Edition: 1
- Published: November 5, 1993
- No. of pages (eBook): 552
- Imprint: Morgan Kaufmann
- Language: English
- Paperback ISBN: 9781558603066
- eBook ISBN: 9781483214511
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