The United Nations' 2030 Agenda for Sustainable Development requires that all countries collaborate to fight adversarial factors to achieve peace and prosperity for humans and the planet. This scenario can be formulated as an adversarial team game in AI literature, where a team of players play against an adversary. However, previous solution concepts for this game assume that team players have the same utility functions, which cannot cover the real-world case that countries do not always have the same utility function. This paper argues that studying adversarial team games should not ignore the difference in utility functions of team players. We show that ignoring the difference in utility functions of team players could cause the computed equilibrium to be unstable. To show the benefit of considering the difference in utility functions of team players, we introduce a novel solution concept called Co-opetition Equilibrium (CoE) for the adversarial team game. In this game, team players with different utility functions (i.e., cooperation between team players) correlate their actions to play against the adversary (i.e., competition between the team and the adversary). We further introduce the team-maximizing CoE, which is a CoE but maximizes the team's utility among all CoEs. Both equilibria can overcome the issue caused by ignoring the difference in utility functions of team players. We further show the opportunities for theoretical and algorithmic contributions based on our position of considering the difference in utility functions of team players.
翻译:联合国《2030年可持续发展议程》要求所有国家协同对抗不利因素,以实现人类与地球的和平与繁荣。这一场景在人工智能文献中可被建模为对抗性团队博弈,即一个玩家团队与对手进行对抗。然而,该博弈的既有解概念均假设团队成员具有相同的效用函数,这无法涵盖现实中各国效用函数并不总保持一致的情况。本文主张研究对抗性团队博弈不应忽视团队成员效用函数的差异。我们证明,忽视团队成员效用函数的差异可能导致计算出的均衡不稳定。为展示考虑团队成员效用函数差异的益处,我们针对对抗性团队博弈提出了一种新颖的解概念——合作竞争均衡(CoE)。在此博弈中,具有不同效用函数的团队成员(即团队成员间的合作关系)通过关联行动来对抗对手(即团队与对手间的竞争关系)。我们进一步提出了团队最大化合作竞争均衡,该均衡在全体合作竞争均衡中最大化团队效用。这两种均衡均能克服因忽视团队成员效用函数差异所引发的问题。基于我们重视团队成员效用函数差异的立场,本文进一步指出了理论与算法层面的创新机遇。