We develop a theory for learning scenarios where multiple learners co-exist but there are mutual compatibility constraints on their outcomes. This is natural in cognitive learning...
In this paper we present a visual education tool for efficient and effective learning. The toolkit is based on a simple premise: simple concepts should be learned before advanced ...
This paper presents the dynamics of multi-agent reinforcement learning in multiple state problems. We extend previous work that formally modelled the relation between reinforcemen...
Ontology learning is an important task in Artificial Intelligence, Semantic Web and Text Mining. This paper presents a novel framework for, and solutions to, three practical probl...
This work represents the first step towards a task library system in the reinforcement learning domain. Task libraries could be useful in speeding up the learning of new tasks th...
James L. Carroll, Todd S. Peterson, Kevin D. Seppi