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ICML
1998
IEEE
16 years 19 days ago
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games t...
Junling Hu, Michael P. Wellman
ACL
2008
15 years 1 months ago
Name Translation in Statistical Machine Translation - Learning When to Transliterate
We present a method to transliterate names in the framework of end-to-end statistical machine translation. The system is trained to learn when to transliterate. For Arabic to Engl...
Ulf Hermjakob, Kevin Knight, Hal Daumé III
JCS
2011
138views more  JCS 2011»
14 years 2 months ago
Automatic analysis of malware behavior using machine learning
Malicious software—so called malware—poses a major threat to the security of computer systems. The amount and diversity of its variants render classic security defenses ineffe...
Konrad Rieck, Philipp Trinius, Carsten Willems, Th...
EENERGY
2010
15 years 3 months ago
Towards energy-aware scheduling in data centers using machine learning
As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better an...
Josep Lluis Berral, Iñigo Goiri, Ramon Nou,...
ICML
1994
IEEE
15 years 3 months ago
Markov Games as a Framework for Multi-Agent Reinforcement Learning
In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function....
Michael L. Littman