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» Algorithms for Parity Games
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ICML
2010
IEEE
15 years 7 months ago
Convergence, Targeted Optimality, and Safety in Multiagent Learning
This paper introduces a novel multiagent learning algorithm, Convergence with Model Learning and Safety (or CMLeS in short), which achieves convergence, targeted optimality agains...
Doran Chakraborty, Peter Stone
PRL
2008
97views more  PRL 2008»
15 years 6 months ago
Repairing self-confident active-transductive learners using systematic exploration
We consider an active learning game within a transductive learning model. A major problem with many active learning algorithms is that an unreliable current hypothesis can mislead...
Ron Begleiter, Ran El-Yaniv, Dmitry Pechyony
ICML
2004
IEEE
16 years 7 months ago
Communication complexity as a lower bound for learning in games
A fast-growing body of research in the AI and machine learning communities addresses learning in games, where there are multiple learners with different interests. This research a...
Vincent Conitzer, Tuomas Sandholm
IEEECGIV
2005
IEEE
16 years 4 days ago
A Practical Implementation of a 3-D Game Engine
Creating a 3-D game engine is not a trivial task as gamers often demand for high quality output with top notch performance in games. In this paper, we show you how various real-ti...
Thomas C. S. Cheah, Kok-Why Ng
GECCO
2006
Springer
123views Optimization» more  GECCO 2006»
15 years 10 months ago
The parallel Nash Memory for asymmetric games
Coevolutionary algorithms search for test cases as part of the search process. The resulting adaptive evaluation function takes away the need to define a fixed evaluation function...
Frans A. Oliehoek, Edwin D. de Jong, Nikos A. Vlas...