We describe a novel framework developed for transfer learning within reinforcement learning (RL) problems. Then we exhibit how this framework can be extended to intelligent tutorin...
Kimberly Ferguson, Beverly Park Woolf, Sridhar Mah...
Temporal difference (TD) algorithms are attractive for reinforcement learning due to their ease-of-implementation and use of "bootstrapped" return estimates to make effi...
In this paper we propose a model for human learning and decision making in environments of repeated Cliff-Edge (CE) interactions. In CE environments, which include common daily in...
We propose a data structure that decreases complexity of unsupervised competitive learning algorithms which are based on the growing cells structures approach. The idea is based on...
Unsupervised learning algorithms have been derived for several statistical models of English grammar, but their computational complexity makes applying them to large data sets int...