Abstract
Modeling societies of individuals is a challenging task increasingly attracting the interest of the machine learning community. Here we present an application of graphical model methods in order to model the behavior of an ant colony. Ants are tagged with RFID so that their paths through the environment can be constantly recorded. A Structured Hidden Markov Model has been used to build the model of single individual activities. Then, the global profile of the colony has been traced during the migration from one nest to another. The method provided significant information concerning the social dynamics of ant colonies.
This work was supported in part by the Sillages project (N o ANR - 05 - BLAN - 017701) financed by the ANR (Agence Nationale de la Recherche).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Galassi, U.: Structured Hidden Markov Models: A General Tool for Modeling Process Behavior. PhD thesis, Università degli Studi di Torino, Dottorato di ricerca in Informatica (April 2008)
Galassi, U., Giordana, A., Saitta, L.: Incremental construction of structured hidden markov models. In: Veloso, M.M. (ed.) IJCAI, pp. 798–803 (2007)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of IEEE 77(2), 257–286 (1989)
Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D thesis, Dpt. of Computer Science, UC, Berkeley (2002)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. 18th International Conf. on Machine Learning, pp. 282–289. Morgan Kaufmann, San Francisco (2001)
Natarajan, P., Nevatia, R.: View and scale invariant action recognition using multiview shape-flow models. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)
Levinson, S.E.: Continuous variable duration hidden markov models for automatic speech recognition. Computer Speech and Language 1, 29–45 (1986)
Pylkkonen, J., Kurimo, M.: Using phone durations in finnish large vocabulary continuous speech recognition (2004)
Tweed, D., Fisher, R., Bins, J., List, T.: Efficient hidden semi-markov model inference for structured video sequences. In: Proc. 2nd Joint IEEE Int. Workshop on VSPETS, Beijing, China, pp. 247–254 (2005)
Josep, A.B.: Duration modeling with expanded hmm applied to speech recognition
Galassi, U., Giordana, A., Saitta, L.: Structured hidden markov models: A general tool for modeling agent behaviors. In: Soft Computing Applications in Business. Studies in Fuzziness and Soft Computing, vol. 230, pp. 273–292. Springer, Heidelberg (2008)
Forney, G.D.: The viterbi algorithm. Proceedings of IEEE 61, 268–278 (1973)
Cabanes, G., Bennani, Y., Chartagnat, C., Fresneau, D.: Topographic connectionist unsupervised learning for RFID behavior data mining. In: IWRT, pp. 63–72 (2008)
Cabanes, G., Bennani, Y.: A local density-based simultaneous two-level algorithm for topographic clustering. In: IJCNN, pp. 1176–1182. IEEE, Los Alamitos (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cabanes, G., Fresnau, D., Galassi, U., Giordana, A. (2009). Modeling Ant Activity by Means of Structured HMMs. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-04125-9_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04124-2
Online ISBN: 978-3-642-04125-9
eBook Packages: Computer ScienceComputer Science (R0)