Several algorithms for learning near-optimal policies in Markov Decision Processes have been analyzed and proven efficient. Empirical results have suggested that Model-based Inter...
Relativized options combine model minimization methods and a hierarchical reinforcement learning framework to derive compact reduced representations of a related family of tasks. ...
This paper investigates how graphically displayed intelligent virtual actors, mobile devices and innovative interaction modalities can support and enhance educational role-play as ...
Mei Yii Lim, Ruth Aylett, Sibylle Enz, Michael Kri...
The scope of implementing a virtual instructor is to achieve enhanced learning outcomes during an autonomous training (education) sessions of a human learner. Based on the evidenc...
In this paper we present a new method, time-striding hidden Markov model (TSHMM), to learn from long-term motion for atomic behaviors and the statistical dependencies among them. T...