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ATAL
2010
Springer
13 years 7 months ago
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
INFOCOM
2010
IEEE
13 years 4 months ago
Limitations and Possibilities of Path Trading between Autonomous Systems
Abstract—When forwarding packets in the Internet, Autonomous Systems (ASes) frequently choose the shortest path in their network to the next-hop AS in the BGP path, a strategy kn...
Yuval Shavitt, Yaron Singer
CEC
2011
IEEE
12 years 6 months ago
Stochastic Natural Gradient Descent by estimation of empirical covariances
—Stochastic relaxation aims at finding the minimum of a fitness function by identifying a proper sequence of distributions, in a given model, that minimize the expected value o...
Luigi Malagò, Matteo Matteucci, Giovanni Pi...
ATAL
2003
Springer
13 years 11 months ago
Optimizing information exchange in cooperative multi-agent systems
Decentralized control of a cooperative multi-agent system is the problem faced by multiple decision-makers that share a common set of objectives. The decision-makers may be robots...
Claudia V. Goldman, Shlomo Zilberstein
ML
2006
ACM
13 years 5 months ago
Universal parameter optimisation in games based on SPSA
Most game programs have a large number of parameters that are crucial for their performance. While tuning these parameters by hand is rather difficult, efficient and easy to use ge...
Levente Kocsis, Csaba Szepesvári