Probabilistic Action Language pBC+

Author Yi Wang

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Yi Wang
  • Arizona State University, School of Computing, Informatics, and Decision Systems Engineering, Fulton Schools of Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ 85287-8809, United States

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Yi Wang. Probabilistic Action Language pBC+. In Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018). Open Access Series in Informatics (OASIcs), Volume 64, pp. 15:1-15:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


We present an ongoing research on a probabilistic extension of action language BC+. Just like BC+ is defined as a high-level notation of answer set programs for describing transition systems, the proposed language, which we call pBC+, is defined as a high-level notation of LP^{MLN} programs - a probabilistic extension of answer set programs. As preliminary results accomplished, we illustrate how probabilistic reasoning about transition systems, such as prediction, postdiction, and planning problems, as well as probabilistic diagnosis for dynamic domains, can be modeled in pBC+ and computed using an implementation of LP^{MLN}. For future work, we plan to develop a compiler that automatically translates pBC+ description into LP^{MLN} programs, as well as parameter learning in probabilistic action domains through LP^{MLN} weight learning. We will work on defining useful extensions of pBC+ to facilitate hypothetical/counterfactual reasoning. We will also find real-world applications, possibly in robotic domains, to empirically study the performance of this approach to probabilistic reasoning in action domains.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Knowledge representation and reasoning
  • action language
  • probabilistic reasoning
  • LP^{MLN}


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