NDLI: The influence of social display in competitive multiagent learning

Content Provider	IEEE Xplore Digital Library
Author	Sequeira, P. Melo, F.S. Paiva, A.
Copyright Year	2014
Description	Author affiliation: INESC-ID & Inst. Super. Tecnico, Univ. de Lisboa, Porto Salvo, Portugal (Sequeira, P.; Melo, F.S.; Paiva, A.)
Abstract	In this paper we analyze the impact of simple social signaling mechanisms in the performance of learning agents within competitive multiagent settings. In our framework, self-interested reinforcement learning agents interact and compete with each other for limited resources. The agents can exchange social signals that influence the total amount of reward received throughout time. In a series of experiments, we vary the amount resources available in the environment, the frequency of interactions and the importance each agent in the population gives to the social displays of others. We measure the combined performance of the population according to distinct selection paradigms based on the individual performances of each agent. The results of our study show that by focusing on the social displays of others, agents learn to collectively coordinate their feeding behavior by trading-off immediate benefit for long-term social welfare. Also, given populations where the impact of the social signal on the reward differs, the individuals with the highest fitness appear in the most socially-aware populations. The presence of social signaling gives also origin to more social inequalities where the more fit agents benefit from their higher status being appreciated by other members of the population.
Starting Page	56
Ending Page	61
File Size	470409
Page Count	6
File Format	PDF
ISBN	9781479975402
DOI	10.1109/DEVLRN.2014.6982954
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-13
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Sociology Games System-on-chip History Statistics Joints
Content Type	Text
Resource Type	Article

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