Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Ontology-based User Intention Recognition for Proactive Planning of Intelligent Robot Behavior

지능형로봇 행동의 능동적 계획수립을 위한 온톨로지 기반 사용자 의도인식

  • 전호철 (한양대학교 컴퓨터공학과) ;
  • 최중민 (한양대학교 컴퓨터공학과)
  • Received : 2010.08.30
  • Accepted : 2010.12.10
  • Published : 2011.02.25
Download PDF
⟨ Previous Next ⟩

Abstract

Due to the uncertainty of intention recognition for behaviors of users, the intention is differently recognized according to the situation for the same behavior by the same user, the accuracy of user intention recognition by minimizing the uncertainty is able to be improved. This paper suggests a novel ontology-based method to recognize user intentions, and able to minimize the uncertainties that are the obstacles against the precise recognition of user intention. This approach creates ontology for user intention, makes a hierarchy and relationship among user intentions by using RuleML as well as Dynamic Bayesian Network, and improves the accuracy of user intention recognition by using the defined RuleML as well as the gathered sensor data such as temperature, humidity, vision, and auditory. To evaluate the performance of robot proactive planning mechanism, we developed a simulator, carried out some experiments to measure the accuracy of user intention recognition for all possible situations, and analyzed and detailed described the results. The result of our experiments represented relatively high level the accuracy of user intention recognition. On the other hand, the result of experiments tells us the fact that the actions including the uncertainty get in the way the precise user intention recognition.

사용자의 행동에 따른 의도 인식의 불확실성 때문에 사용자가 동일한 행동을 하더라도 상황에 따라 그 의도는 다르게 해석되며, 불확실성을 최소화함으로써 사용자 의도 인식의 정확성을 향상 시킬 수 있다. 본 논문에서는 사용자 의도 인식을 위한 온톨로지 기반의 새로운 방법을 제안하고, 불확실성을 최소화하는 방법을 제안한다. 제안하는 방법은 사용자 의도에 대한 온톨로지를 생성하고, 사용자 의도간 계층적 구조와 관계를 RuleML과 동적 베이지안 네트워크를 이용해서 정의하며, 온도, 습도, 시각 등의 수집된 센서 데이터와 정의된 RuleML을 통해 사용자 의도 인식을 보다 정확하게 하는 것이다. 로봇의 능동적 계획수립 방법의 성능을 평가하기 위해 시뮬레이터를 개발했고, 밝생 가능한 모든 상황에 대해 의도인식의 정확도를 측정하는 실험을 했으며, 이에 대한 결과를 제시하였다. 실험결과 비교적 높은 수준의 의도인식 정확도를 나타냈다. 그러나 불확실성을 내재한 행동이 보다 정확한 의도 인식을 방해한다는 것을 알 수 있었다.

Keywords

References

  1. R. Want, T. Pering, D. Tennenhouse, “Comparing Autonomic and Proactive Computing,” IBM Systems Journal, Vol. 42, No. 1, pp. 129-135, 2003. https://doi.org/10.1147/sj.421.0129
  2. Proactive Computing, Intel Research, 2002.
  3. J. Miura, Y. Shirai, “Parallel Scheduling of Planning and Action of a Mobile Robot based on Planning-Action Consistency, ” IJCAI, 1999.
  4. Z. Butler and D. Rus, “Distributed planning and control for modular robots with unit-compressible modules,” International Journal of Robotics Research, Vol. 22, No. 9, pp. 699-716, September 2003. https://doi.org/10.1177/02783649030229002
  5. I. Nourbakhsh, “Interleaving planning and execution for autonomous robots,” Dordrecht, Netherlands: Kluwer Academic. Ph.D. thesis. 1997.
  6. I. Nourbakhsh, “Interleaving planning and execution for autonomous robots,” technical report STAN-CS-TR-97-1593, Department of Computer Science, Stanford University, Stanford, CA, 1997.
  7. D. Aarno and D. Kragic, “Motion intention recognition in robot assisted applications,” Robot. Auton. System, Vol. 56, No. 8, pp. 692–705, 2008.
  8. Human Activity Recognition Project, Intel Research, 2005.
  9. J. Smith, K. Fishkin, B. Jiang, A. Mamishev, M. Philipose, A. Rea, S. Roy, K. Sundara-Rajan, “RFID-based Techniques for Human-activity Detection,” Communications of the ACM, Vol. 48, No. 9, pp. 39-44, 2005. https://doi.org/10.1145/1081992.1082018
  10. J. Jung, C. Lee, J. Lee and Z. Bien, “User Intention Recognition for Intelligent Bed Robot System,” in Proceedings of the 8th ICORR, pp. 100-103, 2003.
  11. E..Durfee, “Distributed problem solving and planning,” in Weiss G, editor. Multiagent systems: a modern approach to distributed artificial intelligence, Cambridge, MA:MIT Press, pp. 121-64, [chapter 3], 2000.
  12. S. Saidani, “Self-reconfigurable robots topodynamic,” in Proceedings IEEE International Conference on Robotics & Automation (ICRA’04), New Orleans, Louisiana, USA, pp. 2883-2887, 2004.
  13. K. Z. Haigh and M. Veloso, “Interleaving Planning and Robot Execution for Asynchronous User Requests, Autonomous Robots,” Vol. 5 No. 1, pp. 79-95, March 1998. https://doi.org/10.1023/A:1008817110013
  14. D. Garcia, A. Garcia, and G. Simari, “Defeasible Reasoning and Partial Order Planning,” in Foundations of Information and Knowledge Systems: 5th International Symposium, Foiks 2008, Pisa, Italy, February 11-15, 2008, Proceedings. Springer-Verlag New York Inc, 2008.
  15. Young, R. M., , Pollack, M. E., and Moore, J. D., “Decomposition and causality in partialorder planning,” in Proceedings of the 2nd International Conference on AI and Planning Systems, Chicago, IL, pp. 188-193, July, 1994.
  16. S. Minton, J. Bresina, M. Drummond, “Total-order and partial-order planning: A comparative analysis,” Journal of Artificial Intelligence Research, 2, pp. 227-261, 1994.
  17. A. Stentz, “The focussed $D{\ast}$ algorithm for real-time replanning,” in Proceedings of the International Joint Conference on Artijicial Intelligence, pp. 1652-1659, 1995.
  18. M. Likhachev, D. Ferguson, G. Gordon, A. Stentz, and S. Thrun, “Anytime Dynamic $A{\ast}$: An Anytime, Replanning Algorithm,” in Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pp. 262-271, 2005.
  19. Y. Xu, W. Yue and K. Su, “The BDD-Based Dynamic $A{\ast}$ Algorithm for Real-Time Replanning,” in Proceedings of the 3rd International Workshop on Frontiers in Algorithmics, Vol. 5598 pp. 271-282, 2009.
  20. J. L. Ambite, G. Barish, CA Knoblock, M. Muslea, J. Oh, S. Minton, “Getting from Here to There: Interactive Planning and Agent Execution for Optimizing Travel,” in Proceedings 14th Conference Innovative Applications of Artificial Intelligence (IAAI 2002), AAAI Press, Menlo Park, Calif., pp. 862-869, 2002.
  21. U. Kuter, D. Nau, and J. F. Lemmer, “Interactive planning under uncertainty with causal modeling and analysis,” Dept. Comput. Sci., Univ. Maryland, College Park, Tech. Rep. CS-TR-4434, 2003.
  22. H.Chen, F.Perich, T.Finin, and A.Joshi, "SOUPA: Standard Ontology for Ubiquitous and Pervasive Applications," First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous'04), pp.258-267, 2004.
  23. O. Schrempf, U. Hanebeck, A. Schmid, H. Worn, “A Novel Approach to Proactive Human-Robot Cooperation,” IEEE International Workshop on Robot and Human Interactive Communication, pp.555-560, 2005.