Local Plan Recognition in Direct Manipulation Interfaces

Plan recognition in direct manipulation interfaces must deal with the problem that the information obtained is of low quality with respect to the plan recognition task. There are two main reasons for this: the individual interactions from the user are on a low level as compared to the user’s task, and users may frequently change their intentions. We present two example applications where this is the case. The fact that users change their intentions could be used to motivate an explicit representation of user intentions. However, the low quality of available information makes such an approach unfeasible in direct manipulation interfaces. This paper addresses the same problem by maintaining a plan-parsing approach to plan recognition, but making it local to the user’s most recent actions by imposing a limited attention span. Two different approaches to implementation are given, in the context of the two presented applications.