  Existing plan synthesis approaches in artificial 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
efficient in any one given domain. The domain dependent approaches need to be
(re)designed for each domain separately, but can be very efficient 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. Specifically, 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 specific
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-specific planners using state space
refinements. Our experiments show that the synthesized planners can outperform
classical refinement planners (implemented as instantiations of UCP,
Kambhampati & Srivastava, 1995), using the same control knowledge. We will
contrast the costs and benefits of the synthesis approach with conventional
methods for customizing domain independent planners.
