Dynamic Network Techniques for Autonomous Planning and Control

Abstract

Starting with functional description of physical mechanisms we were able to derive the standard probabilistic properties of Bayesian networks and to show: (1) how the effects of unanticipated actions can be predicted from the net-work topology, (2) how qualitative causal judgments can be integrated with statistical data, (3) how actions interact with observations, (4) how counterfactuals sentences can be interpreted and evaluated, (5) how explanations and single-event causation can be defined in a given causal model.

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Document Details

Document Type
Technical Report
Publication Date
Nov 30, 2000
Accession Number
ADA391489

Entities

People

  • Judea Pearl

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Bayesian Networks
  • Causal Reasoning
  • Cognitive Science
  • Computer Programs
  • Computer Science
  • Computers
  • Data Science
  • Information Science
  • Mathematics
  • Models
  • Observation
  • Probability
  • Reasoning
  • Standards
  • Statistical Data
  • Statistics

Readers

  • Artificial Intelligence
  • Integrated Circuit Design and Technology.
  • Systems Analysis and Design

Technology Areas

  • AI & ML