Abstract
Sign languages can be learned effectively only with frequent feedback from an expert in the field. The expert needs to watch a performed sign, and decide whether the sign has been performed well based on his/her previous knowledge about the sign. The expert’s role can be imitated by an automatic system, which uses a training set as its knowledge base to train a classifier that can decide whether the performed sign is correct. However, when the system does not have enough previous knowledge about a given sign, the decision will not be accurate. Accordingly, we propose a multiagent architecture in which agents cooperate with each other to decide on the correct classification of performed signs. We apply different cooperation strategies and test their performances in varying environments. Further, through analysis of the multiagent system, we can discover inherent properties of sign languages, such as the existence of dialects.
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References
Aran, O., Ari, I., Akarun, L., Sankur, B., Benoit, A., Caplier, A., Campr, P., Carrillo, A., Fanard, F.: Signtutor: An interactive system for sign language tutoring. IEEE MultiMedia 16(1), 81–93 (2009)
Wooldridge, M., Jennings, N.R.: Intelligent agents: Theory and practice. Knowledge Engineering Review 10(2), 115–152 (1995)
Kuncheva, L.: Combining pattern classifiers: methods and algorithms. Wiley-Interscience, Hoboken (2004)
Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 11(3), 387–434 (2005)
Kittler, J.: Combining classifiers: A theoretical framework. Pattern Analysis & Applications 1(1), 18–27 (1998)
Melvin, I., Weston, J., Leslie, C.S., Noble, W.S.: Combining classifiers for improved classification of proteins from sequence or structure. BMC Bioinformatics 9(1), 389 (2008)
Weiss, G.: Multiagent systems: a modern approach to distributed artificial intelligence. MIT Press, Cambridge (1999)
Aran, O., Burger, T., Caplier, A., Akarun, L.: A belief-based sequential fusion approach for fusing manual signs and non-manual signals. Pattern Recognition (2008)
Gokberk, B., Akarun, L.: Comparative analysis of decision-level fusion algorithms for 3D face recognition. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR), vol. 3, pp. 1018–1021 (2006)
Robinson, J.: An iterative method of solving a game. Annals of Mathematics, 296–301 (1951)
Van, M., Erp, S.L.: Variants of the borda count method for combining ranked classifier hypotheses. In: The Seventh International Workshop on Frontiers in Handwriting Recognition, pp. 443–452 (2000)
Jewell, E.J., Abate, F.: The New Oxford American Dictionary. Oxford University Press, Oxford (2001)
Yolum, P., Singh, M.P.: Engineering self-organizing referral networks for trustworthy service selection. IEEE Transactions on Systems, Man and Cybernetics, Part A 35(3), 396–407 (2005)
Chalkiadakis, G., Boutilier, C.: Coordination in multiagent reinforcement learning: A bayesian approach. In: Proceedings of the 2nd AAMAS, pp. 709–716 (2003)
Parker, J.R.: Rank and response combination from confusion matrix data. Information Fusion 2(2), 113–120 (2001)
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Yıldırım, İ., Aran, O., Yolum, P., Akarun, L. (2009). Cooperative Sign Language Tutoring: A Multiagent Approach. In: Aldewereld, H., Dignum, V., Picard, G. (eds) Engineering Societies in the Agents World X. ESAW 2009. Lecture Notes in Computer Science(), vol 5881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10203-5_18
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DOI: https://doi.org/10.1007/978-3-642-10203-5_18
Publisher Name: Springer, Berlin, Heidelberg
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