{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T08:25:20Z","timestamp":1778660720755,"version":"3.51.4"},"reference-count":27,"publisher":"Cambridge University Press (CUP)","issue":"5-6","license":[{"start":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T00:00:00Z","timestamp":1568937600000},"content-version":"unspecified","delay-in-days":19,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Theory and Practice of Logic Programming"],"published-print":{"date-parts":[[2019,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>To be responsive to dynamically changing real-world environments, an intelligent agent needs to perform complex sequential decision-making tasks that are often guided by commonsense knowledge. The previous work on this line of research led to the framework called<jats:italic>interleaved commonsense reasoning and probabilistic planning<\/jats:italic>(i<jats:sc>corpp<\/jats:sc>), which used P-log for representing commmonsense knowledge and Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs) for planning under uncertainty. A main limitation of i<jats:sc>corpp<\/jats:sc>is that its implementation requires non-trivial engineering efforts to bridge the commonsense reasoning and probabilistic planning formalisms. In this paper, we present a unified framework to integrate i<jats:sc>corpp<\/jats:sc>\u2019s reasoning and planning components. In particular, we extend probabilistic action language<jats:italic>pBC<\/jats:italic>+ to express utility, belief states, and observation as in POMDP models. Inheriting the advantages of action languages, the new action language provides an elaboration tolerant representation of POMDP that reflects commonsense knowledge. The idea led to the design of the system<jats:sc>pbcplus2pomdp<\/jats:sc>, which compiles a<jats:italic>pBC<\/jats:italic>+ action description into a POMDP model that can be directly processed by off-the-shelf POMDP solvers to compute an optimal policy of the<jats:italic>pBC<\/jats:italic>+ action description. Our experiments show that it retains the advantages of i<jats:sc>corpp<\/jats:sc>while avoiding the manual efforts in bridging the commonsense reasoner and the probabilistic planner.<\/jats:p>","DOI":"10.1017\/s1471068419000371","type":"journal-article","created":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T13:06:21Z","timestamp":1568984781000},"page":"1090-1106","source":"Crossref","is-referenced-by-count":8,"title":["Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language"],"prefix":"10.1017","volume":"19","author":[{"given":"YI","family":"WANG","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"SHIQI","family":"ZHANG","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9569-5575","authenticated-orcid":false,"given":"JOOHYUNG","family":"LEE","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2019,9,20]]},"reference":[{"key":"S1471068419000371_ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2015.2422531"},{"key":"S1471068419000371_ref25","doi-asserted-by":"crossref","unstructured":"Zhang, S. , Khandelwal, P. , and Stone, P. 2017. Dynamically constructed (PO)MDPs for adaptive robot planning. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.","DOI":"10.1609\/aaai.v31i1.11042"},{"key":"S1471068419000371_ref23","doi-asserted-by":"crossref","unstructured":"Wang, Y. and Lee, J. 2019. Elaboration tolerant representation of markov decision process via decision theoretic extension of action language pbc+. In LPNMR. To appear.","DOI":"10.1007\/978-3-030-20528-7_17"},{"key":"S1471068419000371_ref21","unstructured":"Tran, N. and Baral, C. 2004. Encoding probabilistic causal model in probabilistic action language. In Proceedings of the National Conference on Artificial Intelligence."},{"key":"S1471068419000371_ref19","unstructured":"Sridharan, M. , Gelfond, M. , Zhang, S. , and Wyatt, J. 2019. REBA: A refinement-based architecture for knowledge representation and reasoning in robotics. 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CORPP: Commonsense reasoning and probabilistic planning, as applied to dialog with a mobile robot. In Twenty-Ninth AAAI Conference on Artificial Intelligence.","DOI":"10.1609\/aaai.v29i1.9385"},{"key":"S1471068419000371_ref20","volume-title":"Reinforcement learning: An introduction","author":"Sutton","year":"2018"},{"key":"S1471068419000371_ref14","unstructured":"Lee, J. and Wang, Y. 2018. A probabilistic extension of action language BC+. Theory and Practice of Logic Programming 18(3\u20134), 607\u2013622."},{"key":"S1471068419000371_ref1","doi-asserted-by":"crossref","unstructured":"Amiri, S. , Wei, S. , Zhang, S. , Sinapov, J. , Thomason, J. , and Stone, P. 2018. Multi-modal predicate identification using dynamically learned robot controllers. 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