Abstract
Binding — the ability to combine two or more modal representations of the same entity into a single shared representation is vital for every cognitive system operating in a complex environment. In order to successfully adapt to changes in an dynamic environment the binding mechanism has to be supplemented with cross-modal learning. In this paper we define the problems of high-level binding and cross-modal learning. By these definitions we model a binding mechanism and a cross-modal learner in a Markov logic network and test the system on a synthetic object database.
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References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proc. of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., pp. 207–216 (May 1993)
Bartels, A., Zeki, S.: The temporal order of binding visual attributes. Vision Research 46(14), 2280–2286 (2006)
Besag, J.: Statistical analysis of non-lattice data. Journal of the Royal Statistical Society. Series D (The Statistician) 24(3), 179–195 (1975)
Chella, A., Frixione, M., Gaglio, S.: A cognitive architecture for artificial vision. Artif. Intell. 89(1-2), 73–111 (1997)
Gilks, W.R., Spiegelhalter, D.J.: Markov chain Monte Carlo in practice. Chapman & Hall/CRC (1996)
Harnad, S.: The symbol grounding problem. Physica D: Nonlinear Phenomena 42, 335–346 (1990)
Jacobsson, H., Hawes, N., Kruijff, G.-J., Wyatt, J.: Crossmodal content binding in information-processing architectures. In: Proc. of the 3rd ACM/IEEE International Conference on Human-Robot Interaction, Amsterdam (March 2008)
Jacobsson, H., Hawes, N., Skočaj, D., Kruijff, G.-J.: Interactive learning and cross-modal binding - a combined approach. In: Symposium on Language and Robots, Aveiro, Portugal (2007)
Kok, S., Marc Sumner, M., Richardson, M., Singla, P., Poon, H., Lowd, D., Wang, J., Domingos, P.: The alchemy system for statistical relational ai. Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA (2009)
Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1-2), 107–136 (2006)
Roth, D.: On the hardness of approximate reasoning. Artif. Intell. 82(1-2), 273–302 (1996)
Roy, D.: Learning visually-grounded words and syntax for a scene description task. Computer Speech and Language 16(3-4), 353–385 (2002)
Roy, D.: Grounding words in perception and action: computational insights. TRENDS in Cognitive Sciences 9(8), 389–396 (2005)
Singer, W.: Consciousness and the binding problem. Annals of the New York Academy of Sciences 929, 123–146 (2001)
Steels, L.: The Talking Heads Experiment. Words and Meanings, vol. 1. Laboratorium, Antwerpen (1999)
Vrečko, A., Skočaj, D., Hawes, N., Leonardis, A.: A computer vision integration model for a multi-modal cognitive system. In: Proc. of the 2009 IEEE/RSJ Int. Conf. on Intelligent RObots and Systems, St. Louis, pp. 3140–3147 (October 2009)
Wyatt, J., Aydemir, A., Brenner, M., Hanheide, M., Hawes, N., Jensfelt, P., Kristan, M., Kruijff, G.-J., Lison, P., Pronobis, A., Sjöö, K., Skočaj, D., Vrečko, A., Zender, H., Zillich, M.: Self-understanding & self-extension: A systems and representational approach (2010) (accepted for publication)
Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding belief propagation and its generalizations. Morgan Kaufmann Publishers Inc., San Francisco (2003)
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Vrečko, A., Skočaj, D., Leonardis, A. (2011). Binding and Cross-Modal Learning in Markov Logic Networks. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_25
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DOI: https://doi.org/10.1007/978-3-642-20267-4_25
Publisher Name: Springer, Berlin, Heidelberg
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