• Model-lite case-based planning:

    Previous planning systems require complete domain models as input, which is often difficult in real-world applicaitons. A more realistic assumption is that, provided limited domain knowledge (e.g., background knowledge, represented by incomplete domain models for example) and a set of historical plan cases, we can efficiently generate robust plan solutions to new planning problems. In this project, we build a model-lite planning system that can compute plans for new planning problems with incomplete domain models.

  • Publications:

    Hankz Hankui Zhuo and Subbarao Kambhampati. Model-Lite Planning: Case-Based vs. Model-Based Approaches. Artificial Intelligence. 2017. [link]

    Hankz Hankui Zhuo, Tuan Nguyen and Subbarao Kambhampati. Refining Incomplete Planning Domain Models Through Plan Traces. International Joint Conference on Artificial Intelligence (IJCAI-13),2451-2457,2013. [PDF]

    Hankz Hankui Zhuo, Subbarao Kambhampati and Tuan Nguyen. Model-Lite Case-Based Planning. Association for the Advancement of Artificial Intelligence (AAAI-13). 1077-1083,2013. [PDF]

  • Code will be added soon.