Accession Number : ADA325559

Title :   Probabilistic Temporal Networks.

Descriptive Note : Final rept.,

Corporate Author : AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH

Personal Author(s) : Santos, Eugene, Jr. ; Young, Joel D.

PDF Url : ADA325559

Report Date : 13 NOV 1996

Pagination or Media Count : 36

Abstract : Complex real-world systems consist of collections of interacting processes/events. These processes change over time in response to both internal and external stimuli as well as to the passage of time itself. Many domains such as real-time systems diagnosis, story understanding, and financial forecasting require the capability to model complex systems under a unified framework to deal with both time and uncertainty. Current models for uncertainty and current models for time already provide rich languages to capture uncertainty and temporal information respectively. Unfortunately, these semantics have made it extremely difficult to unify time and uncertainty in a way which cleanly and adequately models the problem domains at hand. This is further compounded by the practical necessity of efficient knowledge engineering under such a unified framework. Existing approaches suffer from significant trade offs between strong semantics for uncertainty and strong semantics for time. In this paper, we define and explore a new model, the Probabilistic Temporal Network, for representing temporal and atemporal information while fully embracing probabilistic semantics. The model allows representation of time constrained causality, of when and if events occur, and of the periodic and recurrent nature of processes. A constraint satisfaction formulation is presented for belief revision as well as a polynomial solvable class.

Descriptors :   *NEURAL NETS, *KNOWLEDGE BASED SYSTEMS, MATHEMATICAL MODELS, ALGORITHMS, UNCERTAINTY, TIME DEPENDENCE, REAL TIME, PROBABILITY DISTRIBUTION FUNCTIONS, RANDOM VARIABLES, SEMANTICS, SYSTEMS ANALYSIS, BAYES THEOREM, FUZZY LOGIC.

Subject Categories : Cybernetics
      Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE