Understanding Sequential Events by Cost-Based Cooperation of Multiple Abducers

Abstract

Understanding sequential events is important, for recognizing a sound sequence, diagnosing a system's varying fault-states, etc. For this purpose, the underlying coherence among observed events should be grasped. However, in many cases the variation speed of events is totally unknown and unpredictable. Therefore, previously presented and prevalent inference approaches using transition probabilities cannot be employed, and also there has been no former logical inference frameworks which overcome this crucial problem. This paper presents a Cost-based Cooperation of Multiple Abducers (CCMA), for explaining sequential events reflecting underlying common causes. Here, multiple abducers, i.e., inference systems of cost-based abduction each of which is assigned to one event observed at a time, work distributedly sending messages about their obtained hypotheses-sets to adjacent abducers. This CCMA obtains underlying common causes of sequential events, even if it is unknown and unpredictable how fast the underlying situation is varying, due to the mixture of numerical messages and logical inference of abduction without parameters on continuity of events like a conditional probabilities. We estimate the performance of this CCMA, for example problems of diagnosing varying faults of electronic circuits.