Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-ba...
In the emerging world of Grid Computing, shared computational, data, other distributed resources are becoming available to enable scientific advancement through collaborative rese...
Line Pouchard, Luca Cinquini, Bob Drach, Don Middl...
We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environments that contain multiple, distributed, dynamic...
Interactive virtual worlds provide a powerful medium for experimental learning and entertainment. Nowadays, virtual environments often incorporate human-like embodied virtual agent...
We present a vision for learning environments, called Science Learning Spaces, that are rich in engaging content and activities, provide constructive experiences in scientific proc...
Kenneth R. Koedinger, Daniel D. Suthers, Kenneth D...