Execution Monitoring with Quantitative Temporal Bayesian Networks

The goal of execution monitoring is to determine whether a system or person is following a plan appropriately. Monitoring information may be uncertain, and the plan being monitored may have complex temporal constraints. We develop a new framework for reasoning under uncertainty with quantitative temporal constraints – Quantitative Temporal Bayesian Networks – and we discuss its application to plan-execution monitoring. QTBNs extend the major previous approaches to temporal reasoning under uncertainty: Time Nets (Kanazawa 1991), Dynamic Bayesian Networks and Dynamic Object Oriented Bayesian Networks (Friedman, Koller, & Pfeffer 1998). We argue that Time Nets can model quantitative temporal relationships but cannot easily model the changing values of fluents, while DBNs and DOOBNs naturally model fluents, but not quantitative temporal relationships. Both capabilities are required for execution monitoring, and are sup-