The effects of bounding rationality on the performance and learning of CHREST agents in Tileworld

Learning in complex and complicated domains is fundamental to performing suitable and timely actions within them. The ability of chess masters to learn and recall huge numbers of board configurations to produce near-optimal actions provides evidence that chunking mechanisms are likely to underpin human learning. Cognitive theories based on chunking argue in favour for the notion of bounded rationality since relatively small chunks of information are learnt in comparison to the total information present in the environment. CHREST, a computational architecture that implements chunking theory, has previously been used to investigate learning in deterministic environments such as chess, where future states are solely dependent upon the actions of agents. In this paper, the CHREST architecture is implemented in agents situated in “Tileworld”, a stochastic environment whose future state depends on both the actions of agents and factors intrinsic to the environment which agents have no control over. The effects of bounding agents’ visual input on learning and performance in various scenarios where the complexity of Tileworld is altered is analysed using computer simulations. Our results show that interactions between independent variables are complex and have important implications for agents situated in stochastic environments where a balance must be struck between learning and performance.