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GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
15 years 9 months ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein
130
Voted
AINA
2007
IEEE
15 years 8 months ago
Fuzzy Recommendation towards QoS-Aware Pervasive Learning
Pervasive learning promises an exciting learning environment such that users can access content and study them at anytime, anywhere, through any devices. Besides delivering the ri...
Zhiwen Yu, Norman Lin, Yuichi Nakamura, Shoji Kaji...
ICIA
2007
15 years 7 months ago
Task Learning by Instruction: Benefits and Challenges for Intelligent Interactive Systems
The CALO desktop assistant aims to provide assistance through many AI technologies, including several techniques for learning to perform tasks. Based on our experiences implementi...
Jim Blythe, Prateek Tandon, Mandar Tillu
IADIS
2004
15 years 6 months ago
Connect: Designing the Classroom of Tomorrow by Using Advanced Technologies to Connect Formal and Informal Learning Environments
The main objective of the CONNECT project is to develop an innovative pedagogical framework that attempts to blend formal and informal learning, proposing an educational reform to ...
Sofoklis Sotiriou, Eleni Chatzichristou, Stavros S...
143
Voted
IAAI
2003
15 years 6 months ago
Teachable Agents: Learning by Teaching Environments for Science Domains
The crisis in science education and the need for innovative computer-based learning environments has prompted us to develop a multi-agent system, Betty’s Brain that implements t...
Krittaya Leelawong, Karun Viswanath, Joan M. Davis...