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» Multi-agent Relational Reinforcement Learning
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IAT
2010
IEEE
13 years 5 months ago
Selecting Operator Queries Using Expected Myopic Gain
When its human operator cannot continuously supervise (much less teleoperate) an agent, the agent should be able to recognize its limitations and ask for help when it risks making...
Robert Cohn, Michael Maxim, Edmund H. Durfee, Sati...
KI
2002
Springer
13 years 7 months ago
Qualitative Velocity and Ball Interception
In many approaches for qualitative spatial reasoning, navigation of an agent in a more or less static environment is considered (e.g. in the double-cross calculus [12]). However, i...
Frieder Stolzenburg, Oliver Obst, Jan Murray
SIGCSE
2006
ACM
183views Education» more  SIGCSE 2006»
14 years 1 months ago
Programming fundamentals and innovation taught through windows media player skin creation
Windows Media Player user interface “skin” creation has proven an extremely effective method to reinforce practical object oriented programming techniques. Skin creation motiv...
Todd Shurn
CIMCA
2008
IEEE
14 years 1 months ago
Tree Exploration for Bayesian RL Exploration
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
Christos Dimitrakakis
UAI
2003
13 years 8 months ago
On the Convergence of Bound Optimization Algorithms
Many practitioners who use EM and related algorithms complain that they are sometimes slow. When does this happen, and what can be done about it? In this paper, we study the gener...
Ruslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahra...