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2010

Model Failure and Context Switching Using Logic-Based Stochastic Models

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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
Nikita A. Sakhanenko, George F. Luger
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JCST
Authors Nikita A. Sakhanenko, George F. Luger
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