Abstract
We examine how synthetic agents interact in social environments employing a variety of agent training strategies against diverse opponents. Such agent training and playing methods indicate that quality playing relies more on the correct set-up of the learning mechanism than on experience. The experimentation provides valuable insight into the potential of an agent to compete against other agents in its environment and yet manage to also co-operate so that this particular environment allows for the emergence of a competitive champion agent, which will represent its group in further contests. Additionally, by investigating performance while constraining the number of moves we gain interesting insight into competitive learning and playing with resource constraints.
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Acknowledgment
The first author has been partially supported by the Hellenic Artificial Intelligence Society (EETN).
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Kiourt, C., Kalles, D. (2016). Learning in Multi Agent Social Environments with Opponent Models. In: Rovatsos, M., Vouros, G., Julian, V. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2015 2015. Lecture Notes in Computer Science(), vol 9571. Springer, Cham. https://doi.org/10.1007/978-3-319-33509-4_12
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DOI: https://doi.org/10.1007/978-3-319-33509-4_12
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