Uncertainty in Artificial Intelligence

Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (1994) covers the papers accepted for presentation at the Tenth Annual Conference on Uncertainty in Artificial Intelligence, held in Seattle, Washington on July 29-31, 1994. The book focuses on the processes, methodologies, and approaches involved in artificial intelligence, including approximations, computational methods, Bayesian networks, and probabilistic inference.

The selection first offers information on ending-based strategies for part-of-speech tagging; an evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets; and probabilistic constraint satisfaction with non-Gaussian noise. The text then examines Laplace's method approximations for probabilistic inference in belief networks with continuous variables; computational methods, bounds, and applications of counterfactual probabilities; and approximation algorithms for the loop cutset problem.

The book takes a look at learning in multi-level stochastic games with delayed information; properties of Bayesian belief network learning algorithms; and the relation between kappa calculus and probabilistic reasoning. The manuscript also elaborates on intercausal independence and heterogeneous factorization; evidential reasoning with conditional belief functions; and state-space abstraction for anytime evaluation of probabilistic networks.

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

Ending-Based Strategies for Part-of-Speech Tagging

An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief Networks Using Simulated Data Sets

Probabilistic Constraint Satisfaction with Non-Gaussian Noise

A Bayesian Method Reexamined

Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables

Generating New Beliefs from Old

Counterfactual Probabilities: Computational Methods, Bounds and Applications

Modus Ponens Generating Function in the Class of Λ-Valuations of Plausibility

Approximation Algorithms for the Loop Cutset Problem

Possibility and Necessity Functions Over Non-Classical Logics

Exploratory Model Building

Learning in Multi-Level Stochastic Games with Delayed Information

Planning with External Events

Properties of Bayesian Belief Network Learning Algorithms

A Stratified Simulation Scheme for Inference in Bayesian Belief Networks

Proposal: Interactive Media for Research in Uncertainty

Efficient Estimation of the Value of Information in Monte Carlo Models

Symbolic Probabilistic Inference in Large BN20 Networks

Action Networks: A Framework for Reasoning About Actions and Change Under Uncertainty

On the Relation Between Kappa Calculus and Probabilistic Reasoning

A Structured, Probabilistic Representation of Action

Integrating Planning and Execution in Stochastic Domains

Localized Partial Evaluation of Belief Networks

A Probablistic Model of Action for Least-Commitment Planning with Information Gathering

Some Properties of Joint Probability Distributions

An Ordinal View of Independence with Application to Plausible Reasoning

Penalty Logic and its Link with Dempster-Shafer Theory

Value of Evidence on Influence Diagrams

Conditional Independence in Possibility Theory

Backward Simulation in Bayesian Networks

Learning Gaussian Networks

On Testing Whether an Embedded Bayesian Network Represents a Probability Model

Epsilon-Safe Planning

Generating Bayesian Networks from Probablity Logic Knowledge Bases

Abstracting Probabilistic Actions

On Modal Logics for Qualitative Possibility in a Fuzzy Setting

A New Look at Causal Independence

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

A Decision-Based View of Causality

Probabilistic Description Logics

An Experimental Comparison of Numerical and Qualitative Probabilistic Reasoning

An Alternative Proof Method for Possibilistic Logic and its Application to Terminological Logics

Possibilistic Conditioning and Propagation

The Automated Mapping of Plans for Plan Recognition

A Logic for Default Reasoning About Probabilities

Optimal Junction Trees

From Influence Diagrams to Junction Trees

Reduction of Computational Complexity in Bayesian Networks Through Removal of Weak Dependences

Using New Data to Refine a Bayesian Network

Syntax-Based Default Reasoning as Probabilistic Model-Based Diagnosis

Induction of Selective Bayesian Classifiers

Fuzzy Geometric Relations to Represent Hierarchical Spatial Information

Constructing Belief Networks to Evaluate Plans

Operator Selection While Planning Under Uncertainty

Model-Based Diagnosis with Qualitative Temporal Uncertainty

Incremental Dynamic Construction of Layered Polytree Networks

Models of Consensus for Multiple Agent Systems

A Probabilistic Calculus of Actions

Robust Planning in Uncertain Environments

Anytime Decision Making with Imprecise Probabilities

Three Approaches to Probability Model Selection

Knowledge Engineering for Large Belief Networks

Solving Asymmetric Decision Problems with Influence Diagrams

Belief Maintenance in Bayesian Networks

Belief Updating by Enumerating High-Probability Independence-Based Assignments

Global Conditioning for Probabilistic Inference in Belief Networks

Belief Induced by the Partial Knowledge of the Probabilities

Ignorance and the Expressiveness of Single- and Set-Valued Probability Models of Belief

A Probabilistic Approach to Hierarchical Model-Based Diagnosis

Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models

Exceptional Subclasses in Qualitative Probability

A Defect in Dempster-Shafer Theory

State-Space Abstraction for Anytime Evaluation of Probabilistic Networks

General Belief Measures

Generating Graphoids from Generalised Conditional Probability

On Axiomatization of Probabilistic Conditional Independencies

Evidential Reasoning with Conditional Belief Functions

Intercausal Independence and Heterogeneous Factorization

Author Index