A Knowledge Representation Framework for Evolutionary Simulations with Cognitive Agents

We propose a generic knowledge representation framework that supports evolutionary game-theoretic simulations using cognitive agents. The framework allows an experimenter to test a population of such agents via generations to study how specific population behaviours evolve over time. A generation is composed of rounds which can be further divided into encounters according to model-specific requirements. As agents in the population interact, events (caused either by agent actions or by separate environment processes) take place. These events change the environment, changes are then perceived by agents that, in turn, decide to take new actions that affect the environment. This process continues until it is time to evolve a new generation, when strategies of the fittest players are selected for the next generation to start evolving. This evolutionary loop continues until all the terminating conditions of the simulation are met. We use the framework to show how to successfully repeat existing experiments from evolutionary simulations of agent cooperation. Our results validate our framework and pave the way for EVO-COGNISIM, a simulation platform that implements the key aspects of the framework in a systematic manner.