Abstract
This paper shows an application of Bayesian Programming to model a simple artificial life problem: that of a worm trying to live in a world full of poison. Any model of a real phenomenon is incomplete because there will always exist unknown, hidden variables that influence the phenomenon. To solve this problem we apply a new formalism, Bayesian programming, which has previously been used in autonomous robot programming. The proposed worm model has been used to train a population of worms using genetic algorithms. We will see the advantages of our method compared with a classical approach. Finally, we discuss the emergent behaviour patterns we observed in some of the worms and conclude by explaining the advantages of the applied method.
This work has been financed by Spanish Comisión Interministerial de Ciencia y Tecnología (CICYT) project number TIC2001-0245-C02-02 and by the Generalitat Valenciana project GV04B685
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References
Lebeltel, O., Bessière, P., Diard, J., Mazer, E.: Bayesian robots programming. Autonomous Robots 16, 49–79 (2004)
Bessi‘ere, P., Group, I.R.: Survei:probabilistic methodology and tecniques for artefact conception and development. In: INRIA (2003)
Koike, C., Pradalier, C., Bessiere, P., Mazer, E.: Proscriptive bayesian programming application for collision avoidance. In: Proc. of the IEEE-RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Las Vegas, USA (2003)
Coué Th, C., Fraichard, P.B., Mazer, E.: Using bayesian programming for multisensor data fusion in automotive applications. IEEE Intelligent Vehicle Symposium (2002)
Bellot, D., Siegwart, R., Bessi‘ere, P., Cou´e, C., Tapus, A., Diard, J.: Bayesian reasoning for real world robotics: Basics, scaling and examples. Book Chapter in LNCS/LNAI (2004), http://128.32.135.2/users/bellot/files/David_Bellot_LNCS_LNAI.pdf
Dysband, E.: Game Programming Gems. In: A finite-state machine class. Charles River Media, pp. 237–248 (2000)
Agre, P., Horswill, I.: Lifeworld analysis. Journal of Artificial Intelligence Research 6, 111–145 (1997)
Rasmussen, S.L., Barrett, C.: Elements of a theory of simulation. In: Morán, F., Merelo, J.J., Moreno, A., Chacon, P. (eds.) ECAL 1995. LNCS, vol. 929, pp. 515–529. Springer, Heidelberg (1995)
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Aznar Gregori, F., Del Mar Pujol López, M., Rizo Aldeguer, R., Suau Pérez, P. (2004). A New Artificial Life Formalization Model: A Worm with a Bayesian Brain. In: López, J.A., Benfenati, E., Dubitzky, W. (eds) Knowledge Exploration in Life Science Informatics. KELSI 2004. Lecture Notes in Computer Science(), vol 3303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30478-4_11
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DOI: https://doi.org/10.1007/978-3-540-30478-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23927-7
Online ISBN: 978-3-540-30478-4
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