Synthesizing Customized Planners from Speci cations

Existing plan synthesis approaches in arti cial intelligence fall into two categories { domain independent and domain dependent. The domain independent approaches are applicable across a variety of domains, but may not be very e cient in any one given domain. The domain dependent approaches need to be (re)designed for each domain separately, but can be very e cient in the domain for which they are designed. One enticing alternative to these approaches is to automatically synthesize domain independent planners given the knowledge about the domain and the theory of planning. In this paper, we investigate the feasibility of using existing automated software synthesis tools to support such synthesis. Speci cally, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used to derive a domain-customized planner through a semi-automatic combination of a declarative theory of planning, and the declarative control knowledge speci c to a given domain, to semi-automatically combine them to derive domain-customized planners. We discuss what it means to write a declarative theory of planning and control knowledge for KIDS, and illustrate our approach by generating a class of domain-speci c planners using state space re nements. Our experiments show that the synthesized planners can outperform classical re nement planners (implemented as instantiations of UCP, Kambhampati & Srivastava, 1995), using the same control knowledge. We will contrast the costs and bene ts of the synthesis approach with conventional methods for customizing domain independent planners.