Refining incomplete planning domain models through plan traces

Hankz Hankui Zhuo, Tuan Nguyen, Subbarao Kambhampati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Pages2451-2457
Number of pages7
StatePublished - Dec 1 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: Aug 3 2013Aug 9 2013

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Other

Other23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
CountryChina
CityBeijing
Period8/3/138/9/13

ASJC Scopus subject areas

  • Artificial Intelligence

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