Bart Peintner

Autominder: an intelligent cognitive orthotic system for people with memory impairment

The world’s population is aging at a phenomenal rate. Certain types of cognitive decline, in particular some forms of memory impairment, occur much more frequently in the elderly. This paper describes Autominder, a cognitive orthotic system intended to help older adults adapt to cognitive decline and continue the satisfactory performance of routine activities, thereby potentially enabling them to remain in their own homes longer. Autominder achieves this goal by providing adaptive, personalized reminders of (basic, instrumental, and extended) activities of daily living. Cognitive orthotic systems on the market today mainly provide alarms for prescribed activities at fixed times that are specified in advance. In contrast, Autominder uses a range of AI techniques to model an individual’s daily plans, observe and reason about the execution of those plans, and make decisions about whether and when it is most appropriate to issue reminders. Autominder is currently deployed on a mobile robot, and is being developed as part of the Initiative on Personal Robotic Assistants for the Elderly (the Nursebot project).

On solving soft temporal constraints using SAT techniques

In this paper, we present an algorithm for finding utilitarian optimal solutions to Simple and Disjunctive Temporal Problems with Preferences (STPPs and DTPPs) based on Benders decomposition and adopting SAT techniques. In our approach, each temporal constraint is replaced by a Boolean indicator variable and the decomposed problem is solved by a tightly integrated STP solver and SAT solver. Several hybridization techniques that take advantage of each solvers strengths are introduced. Finally, empirical evidence is presented to demonstrate the effectiveness of our method compared to other algorithms.