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  1. ; ; ; ; ; ; ( , IEEE/ASME Transactions on Mechatronics)
    Free, publicly-accessible full text available August 25, 2025
  2. ; ; ; ; ( , Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence)
  3. ; ; ; ; ( , UAI '23: Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence)
  4. ; ; ; ( , Neurocomputing)
    Full Text Available 
  5. ; ; ; ; ; ( , Autonomous Robots)
    Full Text Available 
  6. ; ; ( , IEEE Robotics and Automation Letters)
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  7. ; ; ( , Proceedings of Robotics: Science and Systems)
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  8. ; ; ; ; ; ( , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
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  9. ; ; ; ( , IEEE International Conference on Robotics and Automation (ICRA))
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  10. ; ( , AI Magazine)
    Abstract

    Reasoning with declarative knowledge (RDK) and sequential decision‐making (SDM) are two key research areas in artificial intelligence. RDK methods reason with declarative domain knowledge, including commonsense knowledge, that is either provided a priori or acquired over time, while SDM methods (probabilistic planning [PP] and reinforcement learning [RL]) seek to compute action policies that maximize the expected cumulative utility over a time horizon; both classes of methods reason in the presence of uncertainty. Despite the rich literature in these two areas, researchers have not fully explored their complementary strengths. In this paper, we survey algorithms that leverage RDK methods while making sequential decisions under uncertainty. We discuss significant developments, open problems, and directions for future work.

     
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