The first learning track of the international planning competition

  • Authors:

  • Alan Fern;Roni Khardon;Prasad Tadepalli

  • Affiliations:

  • School of Electrical Engineering and Computer Science, Oregon State University, Kelley Engineering Center, Corvallis, USA;Department of Computer Science, Tufts University, Medford, USA;School of Electrical Engineering and Computer Science, Oregon State University, Kelley Engineering Center, Corvallis, USA

  • Venue:
  • Machine Learning
  • Year:
  • 2011

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Abstract

The International Planning Competition is a biennial event organized in the context of the International Conference on Automated Planning and Scheduling. The 2008 competition included, for the first time, a learning track for comparing approaches for improving automated planners via learning. In this paper, we describe the structure of the learning track, the planning domains used for evaluation, the participating systems, the results, and our observations. Towards supporting the goal of domain-independent learning, one of the key features of the competition was to disallow any code changes or parameter tweaks after the training domains were revealed to the participants. The competition results show that at this stage no learning for planning system outperforms state-of-the-art planners in a domain independent manner across a wide range of domains. However, they appear to be close to providing such performance. Evaluating learning for planning systems in a blind competition raises important questions concerning criteria that should be taken into account in future competitions.