Exploiting N-Gram Analysis to Predict Operator Sequences

Authors

  • Christian Muise University of Toronto
  • Sheila McIlraith University of Toronto
  • Jorge Baier University of Toronto
  • Michael Reimer University of Toronto

DOI:

https://doi.org/10.1609/icaps.v19i1.13392

Keywords:

N-grams, learning, planning

Abstract

N-gram analysis provides a means of probabilistically predicting the next item in a sequence. Due originally to Shannon, it has proven an effective technique for word prediction in natural language processing and for gene sequence analysis. In this paper, we investigate the utility of n-gram analysis in predicting operator sequences in plans. Given a set of sample plans, we perform n-gram analysis to predict the likelihood of subsequent operators, relative to a partial plan. We identify several ways in which this information might be integrated into a planner. In this paper, we investigate one of these directions in further detail. Preliminary results demonstrate the promise of n-gram analysis as a tool for improving planning performance.

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Published

2009-10-16

How to Cite

Muise, C., McIlraith, S., Baier, J., & Reimer, M. (2009). Exploiting N-Gram Analysis to Predict Operator Sequences. Proceedings of the International Conference on Automated Planning and Scheduling, 19(1), 374-377. https://doi.org/10.1609/icaps.v19i1.13392

Issue

Vol. 19 (2009): Nineteenth International Conference on Automated Planning and Scheduling

Section

Short Papers