Using Structured Knowledge Representation for Context-Sensitive Probabilistic Modeling
Nikita A. Sakhanenko, George F. Luger · 2008
We propose a context-sensitive probabilistic modeling system (COSMOS) that rea-sons about a complex, dynamic environment through a series of applications of smaller, knowledge-focused models representing contextually relevant information. COSMOS uses a failure-driven architecture to determine whether a context is sup-ported, and consequently whether the current model remains applicable. The in-dividual models are specified through sets of structured, hierarchically organized probabilistic logic statements using transfer functions that are then mapped into a representation supporting stochastic inferencing. We demonstrate COSMOS using data from a mechanical pump system.