Cooperative and Non-Cooperative artificial intelligence (AI)

在我的项目中，我打算研究人工智能代理如何在一般和博弈中学会相互合作并利用彼此。这些游戏将是社会困境，意味着这些游戏的玩家可以做出个人选择来增加他们的奖励，而损害其他玩家的福利。为了开始我对这个主题的研究，我首先打算对各种确定性策略进行信息理论研究，这些策略用于解决囚犯困境（一种简单的社会困境），正如Axelrod所概述的那样，以及包括简单的naïve学习代理。这应该允许我通过问这样的问题来概述可用于对策略进行分类的指标，例如，该策略是否依赖于来自对手的信息传递？如果有，它会利用这些信息来回报行为吗？一旦使用信息理论度量对这些策略进行分类，就有可能检测到改变策略的代理，包括那些试图从与对手合作转向试图利用对手的代理。让我们准备好能够检测并对对手的欺骗行为做出反应的特工。最后，我们希望有可能使用这些指标来尝试并告知深度强化学习代理的设计，这些代理可以学习如何利用他们的对手。

For my project I intend to study how AI agents can learn to cooperate with one another and take advantage of one another in general sum games. The games will be social dilemmas, meaning the players of these games can make a personal choice to increase their reward, at the detriment to the welfare of the other players. To begin my study of this topic I first intend to do an information theoretical study of various deterministic strategies used to play the prisoners dilemma (a simple social dilemma), as outlined by Axelrod, as well as including simple naïve learning agents. This should allow me to outline metrics that can be used to classify strategies, by asking questions such as, does this strategy rely on a transfer of information from its opponent? If it does, does it use this information to reciprocate behaviours? Once these strategies have been classified using information theoretic metrics it should be possible to detect agents that change strategy, including agents who try go from cooperating with their opponent to then trying to exploit them. Allowing us to prepare agents who can detect and react to deceptive behaviours from their opponent. Finally, we expect it to be possible to use these metrics to try and inform the design of deep reinforcement learning agents that can learn to take advantage of their opponent.