Inference-based decentralized prognosis in discrete event systems

For discrete event systems, we study the problem of predicting failures prior to their occurrence, also referred to as prognosis, in the inference-based decentralized framework where multiple decision-makers interact to come up with the global prognostic decisions. Due to the limited sensing capabilities, each decision-maker is subjected to ambiguities during the process of decision-making. In our prior work we made an observation that such ambiguities are of differing gradations and presented a framework for inferencing over the local control decisions of varying ambiguity levels to arrive at a global control decision. Here we present an inference-based decentralized decision-making framework for prognosis of failures: For each event-trace executed by a system being monitored, each local prognoser issues its own prognostic decision (failure is or is not inevitable, or unsure) tagged with a certain ambiguity level (zero being the minimum) that is computed by assessing the ambiguities of the self and the others. A global prognostic decision is taken to be the ¿winning¿ local prognostic decision, i.e., one with the minimum ambiguity level. We characterize the class of systems for which there are no missed detections (all failures can be prognosed prior to their occurrence) and no false alarms (all prognostic decisions are correct) by introducing the notion of N-inference-prognosability, where the parameter N represents the maximum ambiguity level of any winning prognostic decision. An algorithm for verifying N-inference-prognosability is presented. We also show that the notion of coprognosability introduced is the same as 0-inference-prognosability, and as the parameter N is increased, a larger class of prognosable systems is obtained.