AIC Seminar Series

Recent planning and scheduling applications have successfully used the Temporal Constraint Satisfaction Problem (TCSP) formalism for representing events to be scheduled and temporal constraints between them. Given a TCSP, the task is to find a schedule or a set of schedules that satisfies all constraints in the problem, or to prove that none exists. TCSPs have at least two limitations in the context of planning applications: the hard bounded constraints often do not match reality; and it is difficult for a planning system to recover if any constraint is violated during plan execution. To address both limitations, we have extended the TCSP formalism to include preferences, which allow constraints to be satisfied to different degrees. Once preferences are added, the problem changes from a satisfaction problem to an optimization problem, and thus requires new algorithms for finding solutions. In this talk, I will begin by describing our Autominder planning application, how it uses TCSPs, and its requirements for a TCSP solver. Then, I will review the basics of TCSPs, show how to extend them with preferences, and describe one of the algorithms we have developed to quickly find high-valued schedules for a given TCSP with Preferences.

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