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...
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...
Abstract. Protein function prediction represents a fundamental challenge in bioinformatics. The increasing availability of proteomics network data has enabled the development of se...
— 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 ...
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...