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Julien Hubert
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Proceedings Papers
Jacqueline Heinerman, Jörg Stork, Margarita Alejandra Rebolledo Coy, Julien Hubert, Thomas Bartz-Beielstein ...
. ecal2017, ECAL 2017, the Fourteenth European Conference on Artificial Life200-207, (September 4–8, 2017) 10.1162/isal_a_036
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Social learning enables multiple robots to share learned experiences while completing a task. The literature offers contradicting examples of its benefits; robots trained with social learning reach a higher performance, an increased learning speed, or both, compared to their individual learning counterparts. No general explanation has been advanced for the difference in observations, which make the results highly dependent on the particular system and parameter setting. In this research, we show that even within one system, the observed advantages of social learning can vary between parameter settings. Using Evolutionary Robotics, we train robots individually in a foraging task. We compare the performance of 50 parameter instances of the evolutionary algorithm obtained by a definitive screening design. We apply social learning in groups of two and four robots to the parameter settings that lead to the best and median performance. Our results show that the observed advantages of social learning differ highly between parameter settings but in general, median quality parameter settings experience more benefit from social learning. These results serve as a reminder that tuning of the parameters should not be left as an afterthought because they can drastically impact the conclusions on the advantages of social learning. Additionally, these results suggest that social learning reduces the sensitivity of the learning process to the choice of parameters.
Proceedings Papers
. alif2016, ALIFE 2016, the Fifteenth International Conference on the Synthesis and Simulation of Living Systems406-407, (July 4–6, 2016) 10.1162/978-0-262-33936-0-ch067
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Time perception is the capacity to sense the passing of time, but in most living creatures it also involves memorizing how much time passed, and eventually acting when it reaches a specific amount. The later is referred as interval timing. This capacity allows animals to detect temporally repeating events in their environment, avoid them if necessary, or exploit them if beneficial(Saigusa et al., 2008). While the research in animals has focused on interval timing (Connor, 1985; Durstewitz, 2003), research in artificial life has limited itself to time perception (Maniadakis et al., 2014; Trianni, 2008). Indeed, alife models rely on the intrinsic temporal properties of neural networks to encode the passing of time and, therefore, cannot estimate how much time passed since the onset of a stimulus. Our work attempts to make one step closer to interval timing by designing an agent which must learn the duration of a stimulus, but also replay it later on.
Proceedings Papers
. alife2014, ALIFE 14: The Fourteenth International Conference on the Synthesis and Simulation of Living Systems769-770, (July 30–August 2, 2014) 10.1162/978-0-262-32621-6-ch124
Proceedings Papers
. ecal2013, ECAL 2013: The Twelfth European Conference on Artificial Life1075-1082, (September 2–6, 2013) 10.1162/978-0-262-31709-2-ch161
Proceedings Papers
. ecal2013, ECAL 2013: The Twelfth European Conference on Artificial Life698-705, (September 2–6, 2013) 10.1162/978-0-262-31709-2-ch100
Proceedings Papers
Robotic approach to understand the role of vicarious trial-and-error in a T-maze task (full article)
. ecal2011, ECAL 2011: The 11th European Conference on Artificial Life79, (August 8–12, 2011) 10.7551/978-0-262-29714-1-ch079