Relaxing Regression for a Heuristic GOLOG

Authors:
Michelle L. Blom;Adrian R. Pearce
Affiliations:
NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia;NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia
Venue:
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Year:
2010

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Abstract

GOLOG is an agent programming language designed to represent complex actions and procedures in the situation calculus. In this paper we apply relaxation-based heuristics --often used in classical planning --to find (near) optimal executions of a GOLOG program. In doing so we present and utilise a theory of relaxed regression for the approximate interpretation of a GOLOG program. This relaxed interpreter is used to heuristically evaluate the available choices in the search for a program execution. We compare the performance of our heuristic interpreter (in terms of the quality of executions found) with a traditional depth-first search interpreter and one guided by a greedy heuristic without a look-ahead on three domains: spacecraft control, mine operations planning, and task scheduling.