Relaxing regression for a heuristic GOLOG
AuthorBLOM, MICHELLE; PEARCE, ADRIAN
AffiliationComputing and Information Systems
Access StatusThis item is currently not available from this repository
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. 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 planning, and task scheduling.
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