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AAAI
2008
15 years 8 months ago
Potential-based Shaping in Model-based Reinforcement Learning
Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have ...
John Asmuth, Michael L. Littman, Robert Zinkov
ACSC
2005
IEEE
15 years 8 months ago
Gradiance On-Line Accelerated Learning
Gradiance On-Line Accelerated Learning GOAL is a system for creating and automatically grading homeworks, programming laboratories, and tests. Through the concept of root questi...
Jeffrey D. Ullman
CEC
2005
IEEE
15 years 8 months ago
Population based incremental learning with guided mutation versus genetic algorithms: iterated prisoners dilemma
Axelrod’s original experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent P...
Timothy Gosling, Nanlin Jin, Edward P. K. Tsang
ECTEL
2008
Springer
15 years 8 months ago
e-Learning in Higher Education: Searching for a Model of Curriculum Analysis
The main purpose behind the design of this experience is the idea of obtaining useful information to know how the online courses in our University have been developed, and trying t...
Linda J. Castaneda
NIPS
2007
15 years 7 months ago
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...
Ruslan Salakhutdinov, Geoffrey E. Hinton