Modeling intrinsic motivation in ACT-R: Focusing on the relation between pattern matching and intellectual curiosity

To be keen learners, humans need not only external rewards but also internal rewards. To date, there have been many studies on environment learning using intrinsic motivation for artificial agents. In this study, we aim to build a method to express curiosity in new environments via the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture. This model focuses on the "production compilation" and "utility" modules, generic functions of ACT-R, and it regards pattern matching in the environment as a source of intellectual curiosity. We simulated a path-planning task in a maze environment using the proposed model. The model with intellectual curiosity showed that understanding of the environment was improved by the task of searching the environment. Furthermore, we implemented the model using a standard reinforcement learning agent and compared it with the ACT-R model.