Cooperative and Non-Cooperative artificial intelligence (AI)

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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.