A structured approach for synthesizing planners from specifications

Plan synthesis approaches in AI 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 can be very efficient for the domain for which they are designed, but would need to be written separately for each domain of interest. The tediousness and the error-proneness of manual coding have hither-to inhibited work on domain-dependent planners. In this paper we describe a novel way of automating the development of domain dependent planners using knowledge-based software synthesis tools. Specifically, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used in conjunction with a declarative theory of domain independent planning, and the declarative control knowledge specific to a given domain, to semi-automatically derive customized planning code. We discuss what it means to write declarative theory of planning and control knowledge for KIDS, and illustrate it by generating a range of domain-specific planners using state space and plan space refinements. We demonstrate that the synthesized planners can have superior performance compared to classical refinement planners using the same control knowledge