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ICML
1998
IEEE
16 years 7 months 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
EMMCVPR
2009
Springer
16 years 1 months ago
General Search Algorithms for Energy Minimization Problems
We describe a scheme for solving Energy Minimization problems, which is based on the A∗ algorithm accomplished with appropriately chosen LP-relaxations as heuristic functions. Th...
Dmitrij Schlesinger
LION
2009
Springer
112views Optimization» more  LION 2009»
16 years 1 months ago
A Graph-Based Semi-supervised Algorithm for Protein Function Prediction from Interaction Maps
Abstract. Protein function prediction represents a fundamental challenge in bioinformatics. The increasing availability of proteomics network data has enabled the development of se...
Valerio Freschi
CEC
2008
IEEE
16 years 1 months ago
A multi-agent based evolutionary algorithm in non-stationary environments
— In this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolv...
Yang Yan, Hongfeng Wang, Dingwei Wang, Shengxiang ...
EVOW
2007
Springer
16 years 1 months ago
Combining Lagrangian Decomposition with an Evolutionary Algorithm for the Knapsack Constrained Maximum Spanning Tree Problem
We present a Lagrangian decomposition approach for the Knapsack Constrained Maximum Spanning Tree problem yielding upper bounds as well as heuristic solutions. This method is furth...
Sandro Pirkwieser, Günther R. Raidl, Jakob Pu...