Reformulating Constraints for Compilability and Efficiency

KBSDE is a knowledge compiler that uses a classification-based approach to map solution constraints in a task specification onto particular search algorithm components that will be responsible for satisfying those constraints (e.g., local constraints are incorporated in generators; global constraints are incorporated in either testers or hillclimbing patchers). Associated with each type of search algorithm component is a subcompiler that specializes in mapping constraints into components of that type. Each of these subcompilers in turn uses a classification-based approach, matching a constraint passed to it against one of several schemas, and applying a compilation technique associated with that schema. While much progress has occurred in our research since we first laid out our classification-based approach [Ton91], we focus in this paper on our reformulation research. Two important reformulation issues that arise out of the choice of a schema-based approach are: (1) compilability-- Can a constraint that does not directly match any of a particular subcompiler's schemas be reformulated into one that does? and (2) Efficiency-- If the efficiency of the compiled search algorithm depends on the compiler's performance, and the compiler's performance depends on the form in which the constraint was expressed, can we find forms for constraints which compile better, or reformulate constraints whose forms can be recognized as ones that compile poorly? In this paper, we describe a set of techniques we are developing for partially addressing these issues

Proceedings of the Workshop on Change of Representation and Problem Reformulation

The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning

Automated synthesis of constrained generators

Knowledge compilation is an emerging research area that focuses on "compiling " a problem solver's inefficient, explicit knowledge representation into more efficient, implicit forms. This paper presents a technique that transforms a declarative problem description (specifying the problem but not how to solve it) into a reasonably efficient, generate-and-test problem solver. Our technique performs constraint incorporation, modifying the parameter generators so they only generate values that satisfy the problem constraints. Successful constraint incorporation depends upon choosing the right solution representation (i.e., the set of parameters). Having expressed a constraint in terms of a particular set of parameters, incorporation fails if the constraint is not factorable into constraints on the individual parameter generators. RICK, a Refinement-based constraint Incorporator for Compiling Knowledge, is a prototype program that compiles a problem specification into a problem solver using least commitment, topdown refinement to achieve constraint incorporation. RICK refines an abstract solution representation to avoid premature commitment to representations that hinder constraint incorporation. RICK is able to incorporate local constraints that constrain relatively small portions of the entire solution. We have tested these ideas by having RICK automatically construct a house floor planning problem solver