By Jianye Hao, Ho-fung Leung
This publication generally goals at fixing the issues in either cooperative and aggressive multi-agent platforms (MASs), exploring elements reminiscent of how brokers can successfully discover ways to in achieving the shared optimum answer in keeping with their neighborhood info and the way they could learn how to raise their person software through exploiting the weak point in their competitors. The ebook describes primary and complicated innovations of ways multi-agent platforms could be engineered in the direction of the target of making sure equity, social optimality, and person rationality; a variety of extra suitable themes also are lined either theoretically and experimentally. The booklet can be invaluable to researchers within the fields of multi-agent structures, online game conception and synthetic intelligence ordinarily, in addition to practitioners constructing useful multi-agent systems.
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Additional info for Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality
5, we can see that agent 2 always chooses action R1 and agent 1 chooses action R2 most of the time, except there are a few time steps that agent 1 tries action R1 at the beginning of each period, which results in conflict, and then agent 1 returns to choose R2 after the unsuccessful trials for R1. Thus we can see that when the value of ˇ is small, fairness is never achieved in the repeated game no matter how large the value of ˛ is. 8 shows the curves of each agent’s average accumulated payoff when the value of ˛ varies with ˇ fixed to its upper limit value ˇ D 1:0.
Springer, Berlin/New York, pp 155–164 28. Matlock M, Sen S (2007) Effective tag mechanisms for evolving coordination. In: Proceedings of AAMAS’07, Honolulu, p 251 29. Matlock M, Sen S (2009) Effective tag mechanisms for evolving coperation. In: Proceedings of AAMAS’09, Budapest, pp 489–496 30. Chao I, Ardaiz O, Sanguesa R (2008) Tag mechanisms evaluated for coordination in open multi-agent systems. In: Proceedings of 8th international workshop on engineering societies in the agents world, Athens, pp 254–269 31.
4 The value of m reflects the tolerance degree of the agents to malicious agents and thus can be set to different values accordingly. , always choose the action they prefer). In other words, this mechanism guarantees that the agent will always choose the action it prefers after it receives lower payoff than its opponent for consecutive m periods. Therefore the agent adopting the adaptive strategy can be resistant to malicious exploitation of its opponent which does not follow this strategy. This mechanism can be regarded as a form of trigger strategy so that by implementing it the agents will have no incentive to deviate from this adaptive strategy.
Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality by Jianye Hao, Ho-fung Leung