Benefits of Social Learning in Physical Robots

Heinerman, Jacqueline; Bussmann, Bart; Groenendijk, Rick; Krieken, Emile van; Slik, Jesper; Tezza, Alessandro; Haasdijk, Evert; Eiben, A. E.

doi:10.1109/ssci.2018.8628857

Cited by 1 publication

(1 citation statement)

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“…Some studies investigate, also comparatively, both individual and social learning (Mesoudi et al, 2016). Heinerman et al (2018) consider learning occurring in a group of physical robots, and two different scenarios, where each robot is controlled by an ANN. They show that social learning is an efficient strategy to reduce the time required for finding the optimal solution.…”

Section: Related Workmentioning

confidence: 99%

Mechanisms of Social Learning in Evolved Artificial Life

Bartoli¹,

Catto²,

Lorenzo³

et al. 2020

The 2020 Conference on Artificial Life

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Adaptation of agents in artificial life scenarios is especially effective when agents may evolve, i.e., inherit traits from their parents, and learn by interacting with the environment. The learning process may be boosted with forms of social learning, i.e., by allowing an agent to learn by combining its experiences with knowledge transferred among agents. In this work, we tackle two specific questions regarding social learning and evolution: (a) from whom learners should learn? (b) how should knowledge be transferred? We address these questions by experimentally investigating two scenarios: a simple one in which the mechanism for evolution and learning is easily interpretable; a more complex and realistic artificial life scenario in which agents compete for survival. Experimental results show that social learning is more profitable when (a) the learners learn from a small set of good teachers and (b) the knowledge to be transferred is determined by teachers experience, rather than learner experience.

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Section: Related Workmentioning

confidence: 99%