Hierarchical Qualitative Descriptions of Perceptions for Robotic Environments

Enrique Muñoz, Takehiko Nakama, E. Ruspini

2013

Abstract

The development and uptake of robotic technologies, outside the research community, has been hindered by the fact that robotic systems are notably lacking in flexibility. Introducing humans in robot teams promises to improve their flexibility. However, the major underlying difficulty in the development of human-robot teams is the inability of robots to emulate important cognitive capabilities of human beings due to the lack of approaches to generate and effectively abstract salient semantic aspects of information and big data sets. In this paper we develop a general framework for information abstraction that allows robots to obtain high level descriptions of their perceptions. These descriptions are represented using a formal predicate logic that emulates natural language structures, facilitating human understanding while it remains easy to interpret by robots. In addition, the proposed formal logic constitutes a precisiation language that generalizes Zadeh's Precisiated Natural Language, providing new tools for the computation with perceptions.

References

  1. Anderson, D., Luke, R. H., Keller, J. M., and Skubic, M. (2008). Extension of a soft-computing framework for activity analysis from linguistic summarizations of video. In Fuzzy Systems, 2008. FUZZ-IEEE 2008.(IEEE World Congress on Computational Intelligence). IEEE International Conference on, pages 1404-1410.
  2. Dubois, D. and Prade, H. (1996). Approximate and commonsense reasoning: From theory to practice. In Proceedings of the 9th International Symposium on Foundations of Intelligent Systems, ISMIS 7896, pages 19- 33, London, UK, UK. Springer-Verlag.
  3. Fogel, D. B. (2002). Blondie24: playing at the edge of AI. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
  4. Halliday, M. A. K. and Matthiessen, C. M. I. M. (2004). An introduction to functional grammar / M.A.K. Halliday. Hodder Arnold, London :, 3rd ed. / rev. by christian M.I.M. matthiessen. edition.
  5. Klir, G. J. and Yuan, B. (1995). Fuzzy sets and fuzzy logic: theory and applications. Prentice-Hall, Inc., Upper Saddle River, NJ, USA.
  6. Marble, J. L., Bruemmer, D. J., Few, D. A., and Dudenhoeffer, D. D. (2004). Evaluation of supervisory vs. peer-peer interaction with human-robot teams. In Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 5 - Volume 5.
  7. Martin, T., Majeed, B., Lee, B.-S., and Clarke, N. (2006). Fuzzy ambient intelligence for next generation telecare. In Fuzzy Systems, 2006 IEEE International Conference on, pages 894-901.
  8. Nakama, T., Mun˜oz, E., and Ruspini, E. (2013a). Generalization and formalization of precisiation language with applications to human-robot interaction. To appear in Proceedings of the 5th International Conference on Fuzzy Computation Theory and Applications, FCTA 2013.
  9. Nakama, T., Mun˜oz, E., and Ruspini, E. (2013b). Generalizing precisiated natural language: A formal logic as a precisiation language. To appear in Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology, EUSFLAT-2013.
  10. Romero-Zaliz, R., Rubio-Escudero, C., Cobb, J., Herrera, F., Cordon, O., and Zwir, I. (2008). A multiobjective evolutionary conceptual clustering methodology for gene annotation within structural databases: A case of study on the gene ontology database. Evolutionary Computation, IEEE Transactions on, 12(6):679 -701.
  11. Sugeno, M. (1977). Fuzzy measures and fuzzy integrals: a survey. In Gupta, M., Saridis, G., and Gains, B., editors, Fuzzy automata and decision processes, pages 89-102. North Holland, Amsterdam.
  12. Tang, F. and Parker, L. E. (2006). Peer-to-peer human-robot teaming through reconfigurable schemas. In AAAI Spring Symposium: To Boldly Go Where No HumanRobot Team Has Gone Before, pages 26-29. AAAI.
  13. Wilbik, A. (2010). Linguistic summaries of time series using fuzzy sets and their application for performance analysis of mutual funds. PhD thesis, Polish Academy of Sciences.
  14. Wilbik, A., Keller, J., and Bezdek, J. (2012). Generation of prototypes from sets of linguistic summaries. In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pages 1 -8.
  15. Yager, R. R. (1982). A new approach to the summarization of data. Information Sciences, 28(1):69-86.
  16. Zadeh, L. A. (1983). A computational approach to fuzzy quantifiers in natural languages. Computers & Mathematics with Applications, 9(1):149-184.
  17. Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst., 90(2):111-127.
  18. Zadeh, L. A. (2001). A new direction in AI: toward a computational theory of perceptions. AI magazine, 22(1):73.
  19. Zadeh, L. A. (2004). Precisiated natural language (PNL). AI magazine, 25(3):74.
  20. Zwir, I. S. and Ruspini, E. H. (1999). Qualitative object description: Initial reports of the exploration of the frontier. In proceedings of the Joint EUROFUSE-SIC'99 International Conference.
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Paper Citation


in Harvard Style

Muñoz E., Nakama T. and Ruspini E. (2013). Hierarchical Qualitative Descriptions of Perceptions for Robotic Environments . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: SCA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 309-319. DOI: 10.5220/0004657703090319


in Bibtex Style

@conference{sca13,
author={Enrique Muñoz and Takehiko Nakama and E. Ruspini},
title={Hierarchical Qualitative Descriptions of Perceptions for Robotic Environments},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: SCA, (IJCCI 2013)},
year={2013},
pages={309-319},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004657703090319},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: SCA, (IJCCI 2013)
TI - Hierarchical Qualitative Descriptions of Perceptions for Robotic Environments
SN - 978-989-8565-77-8
AU - Muñoz E.
AU - Nakama T.
AU - Ruspini E.
PY - 2013
SP - 309
EP - 319
DO - 10.5220/0004657703090319