Baumann, T; Graepel, T; Shawe-Taylor, J; (2020) Adaptive Mechanism Design: Learning to Promote Cooperation. In: Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN). IEEE: Glasgow, UK.

Preview

Text Adaptive_Mechanism_Design__IJCNN_submission.pdf - Accepted Version Download (403kB) | Preview

Abstract

In the future, artificial learning agents are likely to become increasingly widespread in our society. They will interact with both other learning agents and humans in a variety of complex settings including social dilemmas. We consider the problem of how an external agent can promote cooperation between artificial learners by distributing additional rewards and punishments based on observing the learners' actions. We propose a rule for automatically learning how to create the right incentives by considering the players' anticipated parameter updates. Using this learning rule leads to cooperation with high social welfare in matrix games in which the agents would otherwise learn to defect with high probability. We show that the resulting cooperative outcome is stable in certain games even if the planning agent is turned off after a given number of episodes, while other games require ongoing intervention to maintain mutual cooperation. However, even in the latter case, the amount of necessary additional incentives decreases over time.

Download activity - last month

Export as CSVXMLJSON

Download activity - last 12 months

Export as CSVXMLJSON

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item