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Relaxing regression for a heuristic GOLOG. (English) Zbl 1215.68064
Ă…gotnes, Thomas (ed.), STAIRS 2010. Proceedings of the fifth starting AI researchers’ symposium, Lisbon, Portugal, August 16–20, 2010. Amsterdam: IOS Press (ISBN 978-1-60750-675-1/hbk; 978-1-60750-676-8/ebook). Frontiers in Artificial Intelligence and Applications 222, 37-49 (2011).
Summary: 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.
For the entire collection see [Zbl 1213.68015].
68N20 Theory of compilers and interpreters
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
68T27 Logic in artificial intelligence
ConGolog; GOLOG
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