Knowledge-based programs as building blocks for planning

• Published in: Artificial intelligence Vol. 303; p. 103634
• Main Authors: Baier, Jorge A., McIlraith, Sheila A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2022; Elsevier Science Ltd
• Subjects: Automated planning; Contingent planning; Epistemic planning; Knowledge; Knowledge-based planning; Planning; Semantics; Knowledge-based planning; Automated planning; Epistemic planning; Contingent planning
• ISSN: 0004-3702; 1872-7921
• DOI: 10.1016/j.artint.2021.103634

Knowledge-based programs contain both world-altering actions, which upon execution change the state of the world, and sensing actions, which upon execution change the knowledge state of the agent. Knowledge-based programming has been proposed as an alternative to planning, since programs allow solving families of planning problems. Notwithstanding, agents equipped with a variety of knowledge-based procedures can compose these procedures to achieve goals, exhibiting greater degrees of flexibility. Optimized state-of-the-art planners, unfortunately, cannot be used directly to compose programs since they require operators (not programs), defined by preconditions and effects. In this article we study how to compute preconditions and effects of knowledge-based programs in order to allow state-of-the-art planners to construct plans with knowledge-based programs as building blocks. We study the problem in the language of the situation calculus, appealing to Golog to represent our programs. To this end, we propose an offline execution semantics for Golog programs with sensing. We then propose a compilation method that transforms our action theory with programs into a new theory where programs are replaced by primitive actions. This enables us to use state-of-the-art, operator-based planning techniques to plan with programs that sense for a restricted but compelling class of problems. Finally, we discuss the applicability of these results to existing operator-based planners that support sensing and illustrate the computational advantage of planning with programs that sense via an experiment.