Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship l...
We present a novel approach to natural language generation (NLG) that applies hierarchical reinforcement learning to text generation in the wayfinding domain. Our approach aims to...
The important role given to domain ontologies for knowledge representation implies increasing need for development and maintenance of them. However, we have a scarcity of tools sup...
We consider the problem of tactile discrimination, with the goal of estimating an underlying state parameter in a sequential setting. If the data is continuous and highdimensional...
In this paper, we use computational intelligence techniques to built quantitative models of player experience for a platform game. The models accurately predict certain key affecti...
Christopher Pedersen, Julian Togelius, Georgios N....