Opportunistic motivated learning agents

Abstract

This paper presents an extension of the Motivated Learning model that includes environment masking, and opportunistic behavior of the motivated learning agent. Environment masking improves an agent's ability to learn by helping to filter out distractions, and the addition of a more complex environment increases the simulation's realism. If conditions call for it opportunistic behavior allows an agent to deviate from the dominant task to perform a less important but rewarding action. Numerical simulations were performed using Matlab and the implementation of a graphical simulation based on the OGRE engine is in progress. Simulation results show good performance and numerical stability of the attained solution. © 2012 Springer-Verlag Berlin Heidelberg.