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Artificial Intelligence
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In this paper we discuss our work on plan management in the Autominder cognitive orthotic system. Autominder is being designed as part of an initiative on the development of robotic assistants for the elderly. Autominder stores and updates user plans, tracks their execution via input from robot sensors, and provides carefully chosen and timed reminders of the activities to be performed. It will eventually also learn the typical behavior of the user with regard to the execution of these plans. A central component of Autominder is its Plan Manager (PM), which is responsible for the temporal reasoning involved in updating plans and tracking their execution. The PM models plan update problems as disjunctive temporal problems (DTPs) and uses the Epilitis DTP-solving system to handle them. We describe the plan representations and algorithms used by the Plan Manager, and briefly discuss its connections with the rest of the system.