Knowledge-Based Programs as Succinct Policies for Partially Observable Domains
Zanuttini, Bruno; Lang, Jérôme; Saffidine, Abdallah; Schwarzentruber, François (2019), Knowledge-Based Programs as Succinct Policies for Partially Observable Domains, Artificial Intelligence, 288, p. 103365. 10.1016/j.artint.2020.103365
TypeArticle accepté pour publication ou publié
Journal nameArtificial Intelligence
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Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)We suggest to express policies for contingent planning by knowledge-based programs (KBPs). KBPs, introduced by Fagin et al. (1995), are high-level protocols describing the actions that the agent should perform as a function of their current knowledge: branching conditions are epistemic formulas that are interpretable by the agent. The main aim of our paper is to show that KBPs can be seen as a succinct language for expressing policies in single-agent contingent planning.KBP are conceptually very close to languages used for expressing policies in the partially observable planning literature: like them, they have conditional and looping structures, with actions as atomic programs and Boolean formulas on beliefs for choosing the execution path. Now, the specificity of KBPs is that branching conditions refer to the belief state and not to the observations.Because of their structural proximity, KBPs and standard languages for representing policies have the same power of expressivity: every standard policy can be expressed as a KBP, and every KBP can be “unfolded” into a standard policy. However, KBPs are more succinct, more readable, and more explainable than standard policies. On the other hand, they require more online computation time, but we show that this is an unavoidable tradeoff. We study knowledge-based programs along four criteria: expressivity, succinctness, complexity of online execution, and complexity of verification.
Subjects / KeywordsPlanning under uncertainty; Contingent planning; Epistemic logic; Knowledge-based programs; Belief tracking
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