Is social learning more than parameter tuning?
Authors: 
Jacqueline Heinerman, Jörg Stork, Margarita Alejandra Rebolledo Coy, Julien Hubert, A. E. Eiben, 
Thomas Bartz-Beielstein, Evert HaasdijkAuthors Info & Claims
Jacqueline Heinerman, Jörg Stork, Margarita Alejandra Rebolledo Coy, Julien Hubert, A. E. Eiben, Thomas Bartz-Beielstein, Evert HaasdijkAuthors Info & ClaimsPages 63 - 64
Abstract
Social learning enables multiple robots to share learned experiences while completing a task. The literature offers examples where robots trained with social learning reach a higher performance compared to their individual learning counterparts [e.g, 2, 4]. No explanation has been advanced for that observation. In this research, we present experimental results suggesting that a lack of tuning of the parameters in social learning experiments could be the cause. In other words: the better the parameter settings are tuned, the less social learning can improve the system performance.
References
[1]
Miguel Duarte, Fernando Silva, Tiago Rodrigues, Sancho Moura Oliveira, and Anders Lyhne Christensen. 2014. JBotEvolver: A versatile simulation platform for evolutionary robotics. In Proceedings of the 14th International Conference on the Synthesis & Simulation of Living Systems. MIT Press, Cambridge, MA, 210--211.
[2]
Jacqueline Heinerman, Massimiliano Rango, and A.E. Eiben. 2015. Evolution, Individual Learning, and Social Learning in a Swarm of Real Robots. In Proceedings of the 2015 IEEE International Conference on Evolvable Systems (ICES). IEEE Press, New York, NY, USA, 1055--1062.
[3]
Kenneth O Stanley and Risto Miikkulainen. 2002. Evolving neural networks through augmenting topologies. Evolutionary computation 10, 2 (2002), 99--127.
[4]
Wesley Tansey, Eliana Feasley, and Risto Miikkulainen. 2012. Accelerating evolution via egalitarian social learning. In Proceedings of the 14th annual conference on Genetic and evolutionary computation, Terence Soule (Ed.). ACM, New York, NY, USA, 919--926.
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- Is social learning more than parameter tuning?
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Published In
July 2017
1934 pages
ISBN:9781450349390
DOI:10.1145/3067695
Copyright © 2017 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
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Published: 15 July 2017
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