Abstract
This paper is about the design of an artificial neural network to control an autonomous robot that is required to iteratively solve a discrimination task based on time-dependent structures. The “decision making” aspect demands the robot “to decide”, during a sequence of trials, whether or not the type of environment it encounters allows it to reach a light bulb located at the centre of a simulated world. Contrary to other similar studies, in this work the robot employs environmental structures to iteratively make its choice, without previous experience disrupting the functionality of its decision-making mechanisms.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ampatzis, C., Tuci, E., Trianni, V., Dorigo, M.: Evolving communicating agents that integrate information over time: a real robot experiment. Technical Report TR/IRIDIA/2005-12, Université Libre de Bruxelles (2005)
Dudek, G., Jenkin, M.: Computational Principles of Mobile Robotics. Cambridge University Press, Cambridge (2000)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Harvey, I., Di Paolo, E.A., Wood, R., Quinn, M., Tuci, E.: Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life 11(1–2), 79–98 (2005)
Husbands, P., Smith, T., Jakobi, N., O’Shea, M.: Better living through chemistry: Evolving GasNets for robot control. Connection Science 10(3–4), 185–210 (1998)
Jakobi, N.: Evolutionary robotics and the radical envelope of noise hypothesis. Adaptive Behavior 6, 325–368 (1997)
Di Paolo, E.A.: Evolving spike-timing dependent plasticity for single-trial learning in robots. Philosophical Transactions of the Royal Society A 361, 2299–2319 (2003)
Tuci, E., Quinn, M., Harvey, I.: An evolutionary ecological approach to the study of learning behaviour using a robot-based model. Adaptive Behavior 10(3-4), 201–221 (2003)
Tuci, E., Trianni, V., Dorigo, M.: Feeling the flow of time through sensory/motor coordination. Connection Science 16, 1–24 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tuci, E., Ampatzis, C., Dorigo, M. (2005). Evolving Neural Mechanisms for an Iterated Discrimination Task: A Robot Based Model. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science(), vol 3630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553090_24
Download citation
DOI: https://doi.org/10.1007/11553090_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28848-0
Online ISBN: 978-3-540-31816-3
eBook Packages: Computer ScienceComputer Science (R0)