caa a22 4500 605514763 CHVBK 20210128100706.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s10458-014-9265-1 doi (NATIONALLICENCE)springer-10.1007/s10458-014-9265-1 Introducing decision entrustment mechanism into repeated bilateral agent interactions to achieve social optimality [Elektronische Daten] [Jianye Hao, Ho-fung Leung] During multiagent interactions, robust strategies are needed to help the agents to coordinate their actions on efficient outcomes. A large body of previous work focuses on designing strategies towards the goal of Nash equilibrium under self-play, which can be extremely inefficient in many situations such as prisoner's dilemma game. To this end, we propose an alternative solution concept, socially optimal outcome sustained by Nash equilibrium (SOSNE), which refers to those outcomes that maximize the sum of all agents' payoffs among all the possible outcomes that can correspond to a Nash equilibrium payoff profile in the infinitely repeated games. Adopting the solution concept of SOSNE guarantees that the system-level performance can be maximized provided that no agent will sacrifice its individual profits. On the other hand, apart from performing well under self-play, a good strategy should also be able to well respond against those opponents adopting different strategies as much as possible. To this end, we consider a particular class of rational opponents and we target at influencing those opponents to coordinate on SOSNE outcomes. We propose a novel learning strategy TaFSO which combines the characteristics of both teacher and follower strategies to effectively influence the opponent's behavior towards SOSNE outcomes by exploiting their limitations. Extensive simulations show that our strategy TaFSO achieves better performance in terms of average payoffs obtained than previous work under both self-play and against the same class of rational opponents. The Author(s), 2014 Multiagent learning nationallicence Repeated games nationallicence Socially optimal outcomes sustained by Nash equilibrium nationallicence Hao Jianye Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China aut Leung Ho-fung Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China aut Autonomous Agents and Multi-Agent Systems Springer US; http://www.springer-ny.com 29/4(2015-07-01), 658-682 1387-2532 29:4<658 2015 29 10458 https://doi.org/10.1007/s10458-014-9265-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10458-014-9265-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Hao Jianye Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China aut NATIONALLICENCE 700 1- Leung Ho-fung Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China aut NATIONALLICENCE 773 0- Autonomous Agents and Multi-Agent Systems Springer US; http://www.springer-ny.com 29/4(2015-07-01), 658-682 1387-2532 29:4<658 2015 29 10458