Knowledge-Based Decision Model Construction for Dynamic Interpretation Tasks

Abstract

The aim of this project was to identify general principles and develop concrete techniques for knowledge-based construction of probabilistic models supporting dynamics decision making under uncertainty. We focused on problems where the precis decision context (i.e., which options are available and what information is known) is highly variable, precluding specification of a fixed model in advance. The project yielded technical results in four areas of reasoning and decision making under uncertainty involving model construction: (1) path planning and scheduling under uncertainty, (2) abstraction and other approximation methods for Bayesian networks, (3) Bayesian methods for pattern and plan recognition, and (4) aggregation of beliefs across multiple agents.

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Document Details

Document Type
Technical Report
Publication Date
Apr 29, 1997
Accession Number
ADA332426

Entities

People

  • Michael P. Wellman

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Inference
  • Bayesian Networks
  • Construction
  • Information Processing
  • Language
  • Models
  • Motion Planning
  • Operations Research
  • Pattern Recognition
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Reasoning
  • Recognition

Readers

  • Artificial Intelligence
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms