Refining incomplete planning domain models through plan traces

  • Authors:

  • Hankz Hankui Zhuo;Tuan Nguyen;Subbarao Kambhampati

  • Affiliations:

  • Dept. of Computer Science, Sun Yat-sen University, Guangzhou, China;Dept. of Computer Science and Engineering, Arizona State University;Dept. of Computer Science and Engineering, Arizona State University

  • Venue:
  • IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
  • Year:
  • 2013

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Abstract

Most existing work on learning planning models assumes that the entire model needs to be learned from scratch. A more realistic situation is that the planning agent has an incomplete model which it needs to refine through learning. In this paper we propose and evaluate a method for doing this. Our method takes as input an incomplete model (with missing preconditions and effects in the actions), as well as a set of plan traces that are known to be correct. It outputs a "refined" model that not only captures additional precondition/effect knowledge about the given actions, but also "macro actions". We use a MAX-SAT framework for learning, where the constraints are derived from the executability of the given plan traces, as well as the preconditions/ effects of the given incomplete model. Unlike traditional macro-action learners which use macros to increase the efficiency of planning (in the context of a complete model), our motivation for learning macros is to increase the accuracy (robustness) of the plans generated with the refined model. We demonstrate the effectiveness of our approach through a systematic empirical evaluation.