Accession Number : ADA332434

Title :   Dynamic Networks Techniques for Autonomous Planning and Control. Probabilistic Counterfactuals.

Descriptive Note : Final technical rept. 1 Mar 94-28 Feb 97

Corporate Author : CALIFORNIA UNIV LOS ANGELES COGNITIVE SYSTEMS LAB

Personal Author(s) : Pearl, Judea

PDF Url : ADA332434

Report Date : 09 MAY 1997

Pagination or Media Count : 161

Abstract : We have reformulated Bayesian networks as carriers of causal information. The result is a more natural understanding of what the networks stand for, what judgments are required in constructing the network and, most importantly, how actions and plans are to be handled within the framework of standard probability theory. 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 network topology, (2) how qualitative causal judgments can be integrated with statistical data, (3) how actions interact with observations, and (4) how counterfactuals sentences can be formulated and evaluated.

Descriptors :   *NEURAL NETS, ALGORITHMS, REAL TIME, STATISTICAL DATA, LEARNING MACHINES, BAYES THEOREM.

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE