Temporal Reasoning for Planning and Scheduling in Complex Domains: Lessons Learned

Advanced Planning Technology: Technological Achievements of the ARPA/Rome Laboratory Planning Initiative

Volume

Book One

Contents

Over the past five years, we have implemented and applied efficient, general-purpose temporal reasoning as a substrate for building planning and scheduling systems. We have also investigated the kinds of temporal reasoning that will be most useful, and for what problems. Our results confirm that temporal reasoning is a sufficiently self-contained activity to be implemented entirely independently of the overlying application, modulo some assumptions about how problem-solving is to proceed. We have also shown that constraint-based temporal reasoning supports a '%ast-commitment" style of planning and scheduling that is efficacious in a wide variety of complex problem domains. There have been some surprises, as well, for example in the fact that causal reasoning in general, and projection in particular, have been less useful than we anticipated. In this paper, we sketch the design, implementation, and semantics of our current temporal reasoning engine, loosely based upon Dean’s Time Map Manager (TMM), discuss how that engine has been applied to range of planning and scheduling problems, and draw some conclusions. The primary lesson to be drawn is that constraint-based temporal reasoning provides an effective basis for building planning and scheduling systems, particularly in applications where problem-solving is only weakly directed by domain-specific solution methods.