Model Failure and Context Switching Using Logic-based Stochastic Models

Abstract We define a notion of context that represents invariant, stable-over-time behavior in an environment and we propose an algorithm for detecting context changes in a stream of data. A context change is captured through model failure when a probabilistic model, representing current behavior, is no longer able to fit the newly encountered data. We specify stochastic models using a logic-based probabilistic modeling language and use its learning mechanisms to identify context changes. We also discuss how our algorithm can be incorporated into a failure-driven context-switching probabilistic modeling framework and demonstrate several examples of its application. Keywords Probabilistic reasoning · Context · Failure-driven online learning 1 Introduction to Context-Based Diagnostics In real-time diagnosis, where observations are given as a data stream, reasoning often has to be performed under strict time constraints with limited amounts of data available at each time step. This diagnostic problem can be simplified by a contextualization approach where the data stream is partitioned into stable regions (contexts) and