Commonsense reasoning and probabilistic planning are two of the most important research areas in artificial intelligence. This paper focuses on Integrated commonsense Reasoning and probabilistic Planning (IRP) problems. On one hand, commonsense reasoning algorithms aim at drawing conclusions using structured knowledge that is typically provided in a declarative way. On the other hand, probabilistic planning algorithms aim at generating an action policy that can be used for action selection under uncertainty. Intuitively, reasoning and planning techniques are good at ``understanding the world'' and ``accomplishing the task'' respectively. This paper discusses the complementary features of the two computing paradigms, presents the (potential) advantages of their integration, and summarizes existing research on this topic.

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In Proceedings of 2017 ICAPS Workshop on Planning and Robotics, Pittsburgh, Pennsylvania, June 2017.

@inproceedings{PlanRob17-Zhang, title={Integrated Commonsense Reasoning and Probabilistic Planning}, author={Shiqi Zhang and Peter Stone}, booktitle={Proceedings of 2017 ICAPS Workshop on Planning and Robotics}, month={June}, address={Pittsburgh, Pennsylvania}, url="http://nn.cs.utexas.edu/?zhang:icaps17", year={2017} }