A logical hierarchical hidden semi-Markov model for team intention recognition. (English) Zbl 1422.68225
Summary: Intention recognition is significant in many applications. In this paper, we focus on team intention recognition, which identifies the intention of each team member and the team working mode. To model the team intention as well as the world state and observation, we propose a Logical Hierarchical Hidden Semi-Markov Model (LHHSMM), which has advantages of conducting statistical relational learning and can present a complex mission hierarchically. Additionally, the LHHSMM explicitly models the duration of team working mode, the intention termination, and relations between the world state and observation. A Logical Particle Filter (LPF) algorithm is also designed to infer team intentions modeled by the LHHSMM. In experiments, we simulate agents’ movements in a combat field and employ agents’ traces to evaluate performances of the LHHSMM and LPF. The results indicate that the team working mode and the target of each agent can be effectively recognized by our methods. When intentions are interrupted
within a high probability, the LHHSMM outperforms a modified logical hierarchical hidden Markov model in terms of precision, recall, and \(F\)-measure. By comparing performances of LHHSMMs with different duration distributions, we prove that the explicit duration modeling of the working mode is effective in team intention recognition.
MSC:
| 68T42 | Agent technology and artificial intelligence |
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 68T10 | Pattern recognition, speech recognition |
| 68T37 | Reasoning under uncertainty in the context of artificial intelligence |
References:
| [1] | Sadri, F., Logic-based approaches to intention recognition, Handbook of Research on Ambient Intelligence: Trends and Perspectives, 346-375 (2011), IGI Global |
| [2] | Synnaeve, G.; Bessière, P., A Bayesian model for plan recognition in RTS games applied to starcraft, Proceedings of the 7th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE ’11) |
| [3] | Elloumi, S. Z.; Roy, V.; Marmorat, J.; Maïzi, N., Securing harbors by modeling and classifying ship behaviour, Proceedings of the 20th Annual Conference on Behavior Representation in Modeling and Simulation (BRiMS ’11) |
| [4] | Rabiner, L. R.; Juang, B.-H., An introduction to hidden Markov models, IEEE ASSP Magazine, 3, 1, 4-16 (1986) · doi:10.1109/massp.1986.1165342 |
| [5] | Yu, S.-Z., Hidden semi-Markov models, Artificial Intelligence, 174, 2, 215-243 (2010) · Zbl 1344.68181 · doi:10.1016/j.artint.2009.11.011 |
| [6] | Fine, S.; Singer, Y.; Tishby, N., The hierarchical hidden markov model: analysis and applications, Machine Learning, 32, 1, 41-62 (1998) · Zbl 0901.68178 · doi:10.1023/a:1007469218079 |
| [7] | Bui, H. H.; Venkatesh, S.; West, G., Policy recognition in the abstract hidden Markov model, Journal of Artificial Intelligence Research, 17, 451-499 (2002) · Zbl 1053.68101 |
| [8] | Duong, T.; Phung, D.; Bui, H.; Venkatesh, S., Efficient duration and hierarchical modeling for human activity recognition, Artificial Intelligence, 173, 7-8, 830-856 (2009) · doi:10.1016/j.artint.2008.12.005 |
| [9] | Kersting, K.; Natarajan, S.; Poole, D., Statistical relational AI: logic, probability and computation, Proceedings of the 11th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR ’11) |
| [10] | Kersting, K.; De Raedt, L.; Raiko, T., Logical hidden Markov models, Journal of Artificial Intelligence Research, 25, 425-456 (2006) · Zbl 1182.68353 |
| [11] | Zha, Y.-B.; Yue, S.-G.; Yin, Q.-J.; Liu, X.-C., Activity recognition using logical hidden semi-Markov models, Proceedings of the IEEE 10th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP ’13) · doi:10.1109/iccwamtip.2013.6716604 |
| [12] | Natarajan, S.; Bui, H. H.; Tadepalli, P.; Kersting, K.; Wong, W.-K., Logical hierarchical hidden Markov models for modeling user activities, Inductive Logic Programming: 18th International Conference, ILP 2008 Prague, Czech Republic, September 10-12, 2008 Proceedings. Inductive Logic Programming: 18th International Conference, ILP 2008 Prague, Czech Republic, September 10-12, 2008 Proceedings, Lecture Notes in Computer Science, 5194, 192-209 (2008), Berlin, Germany: Springer, Berlin, Germany · doi:10.1007/978-3-540-85928-4_17 |
| [13] | Yue, S. G.; Zha, Y. B.; Yin, Q. J.; Qin, L., Multi-agent intention recognition using logical hidden semi-markov models, Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications · doi:10.5220/0005036707010708 |
| [14] | Schmidt, C. F.; Sridharan, N. S.; Goodson, J. L., The plan recognition problem: an intersection of psychology and artificial intelligence, Artificial Intelligence, 11, 1, 45-83 (1978) · doi:10.1016/0004-3702(78)90012-7 |
| [15] | Han, T. A.; Pereira, L. M., State-of-the-art of intention recognition and its use in decision making, AI Communications, 26, 2, 237-246 (2013) · doi:10.3233/AIC-130559 |
| [16] | Kautz, H. A.; Allen, J. F., Generalized plan precognition, Proceedings of the 5th National Conference on Artificial Intelligence |
| [17] | Avrahami-Zilberbrand, D.; Kaminka, G. A., Fast and complete symbolic plan recognition, Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI ’05) |
| [18] | Albrecht, D. W.; Zukerman, I.; Nicholson, A. E.; Bud, A., Towards a Bayesian model for keyhole plan recognition in large domains, Proceedings of the 6th International Conference on User-Modeling |
| [19] | Pynadath, D. V., Probabilistic grammars for plan recognition [Ph.D. thesis] (1999), Ann Arbor, Mich, USA: University of Michigan, Ann Arbor, Mich, USA |
| [20] | Dereszynski, E. W.; Hostetler, J.; Fern, A.; Dietterich, T. G.; Hoang, T. T.; Udarbe, M., Learning probabilistic behavior models in real-time strategy games, Proceedings of the 7th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment |
| [21] | Yin, J.; Hu, D. H.; Yang, Q., Spatio-temporal event detection using dynamic conditional random fields, Proceedings of the 21st International Joint Conference on Artificial Intelligence |
| [22] | Avrahami-Zilberbrand, D.; Kaminka, G. A., Hybrid symbolic-probabilistic plan recognizer: initial steps, Proceedings of the AAAI Workshop on Modeling Others from Observations |
| [23] | Geib, C. W.; Goldman, R. P., A probabilistic plan recognition algorithm based on plan tree grammars, Artificial Intelligence, 173, 11, 1101-1132 (2009) · doi:10.1016/j.artint.2009.01.003 |
| [24] | Richardson, M.; Domingos, P., Markov logic networks, Machine Learning, 62, 1-2, 107-136 (2006) · Zbl 1470.68221 · doi:10.1007/s10994-006-5833-1 |
| [25] | Ha, E. Y.; Rowe, J. P.; Mott, B. W.; Lester, J. C., Goal recognition with Markov logic networks for player-adaptive games, Proceedings of the 7th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE ’11) |
| [26] | Koller, N.; Friedman, N., Probabilistic Graphical Models: Principles and Techniques (2009), Boston, Mass, USA: MIT Press, Boston, Mass, USA |
| [27] | Masato, D.; Norman, T. J.; Vasconcelos, W. W.; Sycara, K., Agent-oriented incremental team and activity recognition, Proceedings of the 22nd International Joint Conference on Artificial Intelligence |
| [28] | Mao, W. J.; Gratch, J.; Li, X. C., Probabilistic plan inference for group behavior prediction, IEEE Intelligent Systems, 27, 4, 27-36 (2012) · doi:10.1109/mis.2010.133 |
| [29] | Pfeffer, A.; Das, S.; Lawless, D.; Ng, B., Factored reasoning for monitoring dynamic team and goal formation, Information Fusion, 10, 1, 99-106 (2009) · doi:10.1016/j.inffus.2008.05.005 |
| [30] | Saria, S.; Mahadevan, S., Probabilistic plan recognition in multi-agent systems, Proceedings of the 24th International Conference on Automated Planning and Scheduling |
| [31] | Gaitanis, K., Team behavior recognition using dynamic Bayesian networks [Ph.D. thesis] (2008), Louvain-la-Neuve, Belgium: Universite Catholique de Louvain, Louvain-la-Neuve, Belgium |
| [32] | Sadilek, A.; Kautz, H., Location-based reasoning about complex multi-agent behavior, Journal of Artificial Intelligence Research, 43, 87-133 (2012) · Zbl 1237.68161 · doi:10.1613/jair.3421 |
| [33] | Auslander, B.; Gupta, K. M.; Aha, D. W., Maritime threat detection using probabilistic graphical models, Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference (FLAIRS ’12) |
| [34] | Hladky, S.; Bulitko, V., An evaluation of models for predicting opponent positions in first-person shooter video games, Proceedings of the IEEE Symposium on Computational Intelligence and Games (CIG ’08), IEEE · doi:10.1109/cig.2008.5035619 |
| [35] | Southey, F.; Loh, W.; Wilkinson, D., Inferring complex agent motions from partial trajectory observations, Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI ’07) |
| [36] | van Kasteren, T. L. M.; Englebienne, G.; Kröse, B. J. A., Activity recognition using semi-Markov models on real world smart home datasets, Journal of Ambient Intelligence and Smart Environments, 2, 3, 311-325 (2010) · doi:10.3233/ais-2010-0070 |
| [37] | Bui, H. H.; Venkatesh, S.; West, G., Tracking and surveillance in wide-area spatial environments using the Abstract Hidden Markov Model, International Journal of Pattern Recognition and Artificial Intelligence, 15, 1, 177-196 (2001) · doi:10.1142/s0218001401000782 |
| [38] | Hu, Y.; Baraldi, P.; Maio, F. D.; Zio, E., A particle filtering and kernel smoothing-based approach for new design component prognostics, Reliability Engineering and System Safety, 134, 19-31 (2014) · doi:10.1016/j.ress.2014.10.003 |
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