Recognizing the intention of others in real time is a critical aspect of many human tasks. This article describes a technique for interpreting the near-term intention of an agent performing a task in real time by inferring the behavioral context of the observed agent. Equally significant, the knowledge used in this approach can be captured semi-automatically through observation of an agent performing tasks on a simulator in the context to be recognized. A hierarchical, template-based reasoning technique is used as the basis for intention recognition, where there is a one-to-one correspondence between templates and behavioral contexts or sub-contexts. In this approach, the total weight associated with each template is critical to the correct selection of a template that identifies the agent's current intention. A template's total weight is based on the contributions of individual weighted attributes describing the agent's state and its surrounding environment. The investigation described develops and implements a novel means of learning these weight assignments by observing actual human performance. It accomplishes this using back-propagation neural networks and fuzzy sets. This permits early discrimination between different pre-categorized behavioral contexts/sub-contexts on the human-controlled agent such as a military or passenger vehicle. We describe an application of this concept and the experimentation to determine the viability of this approach.
1. Gonzalez, A. J. & Ahlers, R.1998. Context-based representation of intelligent behavior in training simulations. Transactions of the Society for Computer Simulation, 15(4), 153–166.
2.
2. Schmidt, CF, Sridharan, N. S. and Goodson, J. L.1978. “The Plan Recognition Problem: An Intersection of Psychology and Artificial Intelligence”, Artificial Intelligence, Vol. 11, No. 1&2, pp. 45–83.
3.
3. Clark, A. N.1994. Pattern recognition of noisy sequences of behavioral events using functional combinators. The Computer Journal, 37(5), 385–398.
4.
4. Wang, D. & Arbib, M.1993. Timing and chunking in processing temporal order. IEEE Transactions on Systems, Man, and Cybernetics, 23(4), 993–1009.
5.
5. Maskara, A. & Noetzel, A.1993. Training auto-associative recurrent neural network with preprocessed training data. Proceedings of the SPIE-The International Society for Optical Engineering: Science of Artificial Neural Networks II, 1966 (pp. 420–428). Orlando, FL.
6.
6. Liu, A. & Pentland, A.1997. Towards real-time recognition of driver intentions. Proceedings of the 1997 IEEE Conference on Intelligent Transportation Systems (pp. 236–241). Boston MA.
7.
7. Narendra, K. S., Balakrishnan, J., & Ciliz, M. K.1995. Adaptation and learning using multiple models, switching, and tuning. IEEE Control Systems Magazine, 15(3), 37–51.
8.
8. Pentland, A. & Liu, A.1999. Modeling and prediction of human behavior. Neural Computation, 11, 229–242.
9.
9. Austin, K. B. & Rose, G. M.1997. Automated behavior recognition using continuous-wave Doppler radar and neural networks. Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Magnificent Milestones and Emerging Opportunities in Medical Engineering, 4, 1997, Chicago, IL, October 30 to November 2, pp. 1458–1461.
10.
10. Weng, J. J. & Hwang, W.1998. Toward automation of learning: the state self-organization problem for a face recognizer. Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, 1 (pp. 384–389). Nara, Japan.
11.
11. Strohal, M. & Onken, R.1998. Intent and error recognition as part of a knowledge-based cockpit assistant. In S. K. Rodgers, D. B. Fogel, J. C. Bezdek & B. Bosacchi (Eds.), Applications and Science of Computational Intelligence: Proceedings of the SPIE, 3390, 287–299.
12.
12. Lesh, N., Rich, C. and Sidner, C. L.1999. Using plan recognition in human-computer collaboration. In J. Kay (Ed.), Proceedings of UM99: Seventh International Conference on User Modeling (pp. 23–32). Wien, Austria: Springer.
13.
13. Wobke, W.2002. “Two Logical Theories of Plan Recognition”, Journal of Logic and Computation, 12(3), pp. 371–412, June 2002.
14.
14. Jiang, Y. F. and Ma, N.2002. “Plan Recognition Algorithm based on Plan Knowledge Graph”, Journal of Software, 13(4), pp. 686–692, April, 2002.
15.
15. Patterson, D., Liao, L., Fox, D., and Kautz, H.2003. “Inferring High Level Behaviors from Low Level Sensors”, Fifth Annual Conference on Ubiquitous Computing (UBICOMP 2003), Seattle, WA.
16.
16. Intille, S. S. and Bobick, A. F.2001. “Recognizing Planned Multiperson Action”, Computer Vision and Image Understanding, 81(3), pp. 414–445.
17.
17. Kerkez, B. and Cox, M. T.2002. “Local Predictions for Case-based Plan Recognition”, Proceedings of the 2002 European Conference on Case-Based Reasoning, pp. 189–203.
18.
18. Charniak, E. and Goldman, R. P.1993. “A Bayesian Model of Plan Recognition”, Artificial Intelligence, 64(1), pp. 53–79.
19.
19. Tambe, M.1996. “Tracking Dynamic Team Activity”, Proceedings of the National Conference on Artificial Intelligence (AAAI), 1996.
20.
20. Kaminka, G. A and Tambe, M.2000. “Robust Multi-agent Teams via Socially-attentive Monitoring”, Journal of Artificial Intelligence Research, 12, pp. 105–147.
21.
21. Kaminka, G. A., Pynadath, D. V, and Tambe, M.2002. Monitoring Teams by Overhearing: A Multi-Agent Plan Recognition Approach”, Journal of Artficial Intelligence Research, 1(124).
22.
22. Huber, M. J. 1996. “Plan-based Plan Recognition for Effective Coordination of Agents Through Observation”, PhD dissertation, U. of Michigan.
23.
23. Han, K. and Veloso, M.1999. “Automated Robot Behavior Recognition Applied to Robotic Soccer”, Proc. of IJCAI-1999 Workshop on Team Behavior and Plan Recognition, 1999.
24.
24. Pynadath, D. V. and Wellman, M. P.2000. “Probabilistic State-Dependent Grammars for Plan Recognition”, Proc. of UAI-2000, pp. 507–514.
25.
25. Devaney, M. and Ram, A.1998. “Needles in a Haystack: Plan Recognition in Large Spatial Domains involving Multiple Agents”, Proceedings of the 15th national Conference on Artificial Intelligence, pp 942–947.
26.
26. Goldman, R. P., Geib, C. W. and Miller, C. A.1999. “A New Model of Plan Recognition”, Proc. of the Conf. on Uncertainty in Artificial Intelligence, 1999.
27.
27. Drewes, P. J., Gonzalez, A. J. and Gerber, W.2000. “Interpreting Trainee Intent in Real Time in a Simulation-based Training System”, Transactions of the Society for Computer Simulation, Vol. 17, No. 3, September 2000, pp. 120–134.
28.
28. Turner, R. M.1994. Adaptive reasoning for real-world problems: a schema based approach. Hillsdale, NJ: Lawrence Erlbaum Associates.
29.
29. Turner, R. M. 1998. Context-mediated behavior for intelligent agents. In B. R. Gaines (Ed.), International Journal of Human-Computer Studies: Incorporating Knowledge Acquisition, 48(3), 307–330.
30.
30. Brezillon, P.2004. “Representation of Procedures and Practices in Contextual Graphs”, The Knowledge Engineering Review.
31.
31. Bass, E. J., Zenyuh, J.P., Small, R.L. and Fortin, S.T.1996. “A Context-based Approach to Training Situation Awareness”, Proceedings of the Third Annual Symposium on Human Interaction with Complex Systems, Los Alamitos, CA: IEEE Computer Society Press, pp. 89–95
32.
32. Gerber, W. J. 2001. Real-Time Synchronization of Behavioral Models With Human Performance in a Simulation. Doctoral Dissertation, University of Central Florida, Orlando.
33.
33. Drewes, P. J. 1997. Automated Student Performance Monitoring in Training Simulation. Doctoral Dissertation, University of Central Florida, Orlando.
34.
34. Hertz, J. A., Krough, A. S. & Palmer, R. G.1991. Introduction to the Theory of Neural Computation. Redwood City, CA: Addison-Wesley Publishing Company.
35.
37. Gerber, W. J. and Gonzalez, A. J.2001. Behavior recognition results for behavioral vehicle model synchronization in distributed simulations. Proceedings of the 2001 Interservice/Industry Training, Simulation and Education Conference (pp. 260–270). Orlando, FL.
36.
35. Zadeh, L. A.1987. Fuzzy sets. In R. R. Yager, S Ovchinnikov, R. M. Tong & H. T. Nguyen (Eds.), Fuzzy Sets and Applications: Selected Papers by L. A. Zadeh (pp. 29–44). New York: John Wiley & Sons, Inc. (Reprinted from Information and Control, 8, 338–353, 1965, New York: Academic Press)
37.
36. Langari, R. & Yen, J.1995. Introduction to fuzzy logic control. In J. Yen, R Langari & L A. Zadeh (Eds.), Industrial Applications of Fuzzy Logic and Intelligent Systems (pp. 3–39). Piscataway, NJ: IEEE Press.
