Improving User Taught Task Models

摘要

Task models are essential components in many approaches to user modelling because they provide the context with which to interpret, predict, and respond to user behavior. The quality of such models is critical to their ability to support these functions. This paper describes work on improving task models that are automatically acquired from demonstration. Modifications to a standard planning algorithm are described and applied to an example learned task model, showing the utility of incorporating plan-based reasoning into task learning systems.