EFFICIENTLY FINDING (NEARLY) MINIMAL FST OF REPETITIVE UNSEGMENTED DEMONSTRATION DATA

Frederick L. Crabbe

Abstract

This paper presents an algorithm that enables a robot to learn from demonstration by inferring a nearly minimal plan instead of the more common policy. The algorithm uses only the demon- strated actions to build the plan, without relying on observation of the world states during the demonstration. By making assumptions about the format of the data, it can generate this plan in O(n5).

References

  1. Argall, B., Chernova, S., Veloso, M., and Brown-ing, B. (2009). A survey of robot learn- ing from demonstration. Robotics and Au- tonomous Systems, 57(5):469- 483.
  2. Bertsekas, D. P. (2005). Dynamic Programming and Optimal Control, volume 1. Athena Scientific, Belmont, MA.
  3. Blumer, A., Ehrenfeucht, A., Haussler, D., and Warmuth, M. (1987). Occam's razor. Information Processing Letters, 24(6):377-380.
  4. Bugalho, M. and Oliveira, A. (2005). Inference of regular languages using state merging algorithms. Pattern Recognition, 38:1457-1467.
  5. Crabbe, F. (2011). Experiments on a technique for finding small fsts of repetitive unsegmented demonstration data. Usna-cs-2011-02, US Naval Academy.
  6. Gold, E. M. (1978). Complexity of automoton identification from given data. Iform. Control, 37:302-320.
  7. Hopcroft, J. (1971). Theory of machines and computations, chapter An n log n algorithm for minimizing states in a finite automaton, pages 189-196. Academic Press.
  8. Lang, K. J., Pearlmutter, B. A., and Price, R. A. (1998). Results of the abbadingo one dfa learning competition and a new evidence-driven state merging algorithm. In ICGI, pages 1-12.
  9. McNamara, J. J. (2010). 10 steps to load, stow and secure a freight container. The Journal of Commerce.
  10. Mitchell, T. M. (1982). Generalization as search. Artificial Intelligence Journal, 18(2):203-226.
  11. Pitt, L. and Warmuth, M. (1993). The minimum consistent dfa problem cannot be approximated within any polynomial. J. Assoc. Comput. Mach., 40(1):95-142.
  12. Sim, J. S., Iliopoulos, C. S., Park, K., and Smyth, W. (2001). Approximate periods of strings. Theoretical Computer Science, 262(557-568).
  13. Veeraraghavan, H. and Veloso, M. M. (2008). Teaching sequential tasks with repetition through demonstration. In Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems(AAMAS), volume 3, pages 1357-1360.
Download


Paper Citation


in Harvard Style

L. Crabbe F. (2012). EFFICIENTLY FINDING (NEARLY) MINIMAL FST OF REPETITIVE UNSEGMENTED DEMONSTRATION DATA . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: SSIR, (ICAART 2012) ISBN 978-989-8425-95-9, pages 673-678. DOI: 10.5220/0003881906730678


in Bibtex Style

@conference{ssir12,
author={Frederick L. Crabbe},
title={EFFICIENTLY FINDING (NEARLY) MINIMAL FST OF REPETITIVE UNSEGMENTED DEMONSTRATION DATA},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: SSIR, (ICAART 2012)},
year={2012},
pages={673-678},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003881906730678},
isbn={978-989-8425-95-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: SSIR, (ICAART 2012)
TI - EFFICIENTLY FINDING (NEARLY) MINIMAL FST OF REPETITIVE UNSEGMENTED DEMONSTRATION DATA
SN - 978-989-8425-95-9
AU - L. Crabbe F.
PY - 2012
SP - 673
EP - 678
DO - 10.5220/0003881906730678