Browse > Article
http://dx.doi.org/10.5391/JKIIS.2011.21.1.86

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

Jeon, Ho-Cheol (한양대학교 컴퓨터공학과)
Choi, Joong-Min (한양대학교 컴퓨터공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.21, no.1, 2011 , pp. 86-99 More about this Journal
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.
Keywords
Ontology; User intention recognition; Intelligent robot; Proactive planning; Proactive task execution;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.
2 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.
3 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.
4 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.
5 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.
6 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.
7 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.
8 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.
9 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.
10 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.   DOI
11 I. Nourbakhsh, “Interleaving planning and execution for autonomous robots,” Dordrecht, Netherlands: Kluwer Academic. Ph.D. thesis. 1997.
12 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.
13 D. Aarno and D. Kragic, “Motion intention recognition in robot assisted applications,” Robot. Auton. System, Vol. 56, No. 8, pp. 692–705, 2008.
14 Human Activity Recognition Project, Intel Research, 2005.
15 S. Saidani, “Self-reconfigurable robots topodynamic,” in Proceedings IEEE International Conference on Robotics & Automation (ICRA’04), New Orleans, Louisiana, USA, pp. 2883-2887, 2004.
16 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.   DOI   ScienceOn
17 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.
18 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.
19 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.   DOI
20 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.
21 J. Miura, Y. Shirai, “Parallel Scheduling of Planning and Action of a Mobile Robot based on Planning-Action Consistency, ” IJCAI, 1999.
22 R. Want, T. Pering, D. Tennenhouse, “Comparing Autonomic and Proactive Computing,” IBM Systems Journal, Vol. 42, No. 1, pp. 129-135, 2003.   DOI
23 Proactive Computing, Intel Research, 2002.