Evolving effective behaviours to interact with tag-based populations

Tags and other characteristic, externally perceptible features that are consistent among groups of animals or humans can be used by others to determine appropriate response strategies in societies. This usage of tags can be extended to artificial environments, where agents can significantly reduce cognitive effort spent on appropriate strategy choice and behavior selection by reusing strategies for interacting with new partners based on their tags. Strategy selection mechanisms developed based on this idea have successfully evolved stable cooperation in games such as the Prisoner’s Dilemma game but relies upon payoff sharing and matching methods that limit the applicability of the tag framework. Our goal is to develop a general classification and behavior selection approach based on the tag framework. We propose and evaluate alternative tag matching and adaptation schemes for a new, incoming individual to select appropriate behavior against any population member of an existing, stable society. Our proposed approach allow agents to evolve both the optimal tag for the environment as well as appropriate strategies for existing agent groups. We show that these mechanisms will allow for robust selection of optimal strategies by agents entering a stable society and analyze the various environments where this approach is effective.