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Using the max-plus algorithm for multiagent decision making in coordination graphs ; Vlassis, Nikos in Proc. RoboCup Int. Symposium, Osaka, Japan (2006) Coordination graphs offer a tractable framework for cooperative multiagent decision making by decomposing the global payoff function into a sum of local terms. Each agent can in principle select an ... [more ▼] Coordination graphs offer a tractable framework for cooperative multiagent decision making by decomposing the global payoff function into a sum of local terms. Each agent can in principle select an optimal individual action based on a variable elimination algorithm performed on this graph. This results in optimal behavior for the group, but its worst-case time complexity is exponential in the number of agents, and it can be slow in densely connected graphs. Moreover, variable elimination is not appropriate for real-time systems as it requires that the complete algorithm terminates before a solution can be reported. In this paper, we investigate the max-plus algorithm, an instance of the belief propagation algorithm in Bayesian networks, as an approximate alternative to variable elimination. In this method the agents exchange appropriate payoff messages over the coordination graph, and based on these messages compute their individual actions. We provide empirical evidence that this method converges to the optimal solution for tree-structured graphs (as shown by theory), and that it finds near optimal solutions in graphs with cycles, while being much faster than variable elimination. [less ▲] Detailed reference viewed: 48 (1 UL)Collaborative multiagent reinforcement learning by payoff propagation ; Vlassis, Nikos in Journal of Machine Learning Research (2006), 7 In this article we describe a set of scalable techniques for learning the behavior of a group of agents in a collaborative multiagent setting. As a basis we use the framework of coordination graphs of ... [more ▼] In this article we describe a set of scalable techniques for learning the behavior of a group of agents in a collaborative multiagent setting. As a basis we use the framework of coordination graphs of Guestrin, Koller, and Parr (2002a) which exploits the dependencies between agents to decompose the global payoff function into a sum of local terms. First, we deal with the single-state case and describe a payoff propagation algorithm that computes the individual actions that approximately maximize the global payoff function. The method can be viewed as the decision-making analogue of belief propagation in Bayesian networks. Second, we focus on learning the behavior of the agents in sequential decision-making tasks. We introduce different model-free reinforcement-learning techniques, unitedly called Sparse Cooperative Q-learning, which approximate the global action-value function based on the topology of a coordination graph, and perform updates using the contribution of the individual agents to the maximal global action value. The combined use of an edge-based decomposition of the action-value function and the payoff propagation algorithm for efficient action selection, result in an approach that scales only linearly in the problem size. We provide experimental evidence experimental evidence that our method outperforms related multiagent reinforcement-learning methods based on temporal differences. [less ▲] Detailed reference viewed: 43 (0 UL)Utile coordination: Learning interdependencies among cooperative agents ; ; et al in EEE Symp. on Computational Intelligence and Games, Colchester, Essex (2005) We describe Utile Coordination, an algorithm that allows a multiagent system to learn where and how to coordinate. The method starts with uncoordinated learners and maintains statistics on expected ... [more ▼] We describe Utile Coordination, an algorithm that allows a multiagent system to learn where and how to coordinate. The method starts with uncoordinated learners and maintains statistics on expected returns. Coordination dependencies are dynamically added if the statistics indicate a statistically significant benefit. This results in a compact state representation because only necessary coordination is modeled. We apply our method within the framework of coordination graphs in which value rules represent the coordination dependencies between the agents for a specific context. The algorithm is first applied on a small illustrative problem, and next on a large predator-prey problem in which two predators have to capture a single prey. [less ▲] Detailed reference viewed: 145 (0 UL)Sparse Cooperative Q-learning ; Vlassis, Nikos in Proc. 21st Int. Conf. on Machine Learning, Banff, Canada, (2004) Learning in multiagent systems suffers from the fact that both the state and the action space scale exponentially with the number of agents. In this paper we are interested in using Q-learning to learn ... [more ▼] Learning in multiagent systems suffers from the fact that both the state and the action space scale exponentially with the number of agents. In this paper we are interested in using Q-learning to learn the coordinated actions of a group of cooperative agents, using a sparse representation of the joint stateaction space of the agents. We first examine a compact representation in which the agents need to explicitly coordinate their actions only in a predefined set of states. Next, we use a coordination-graph approach in which we represent the Q-values by value rules that specify the coordination dependencies of the agents at particular states. We show how Q-learning can be efficiently applied to learn a coordinated policy for the agents in the above framework. We demonstrate the proposed method on the predator-prey domain, and we compare it with other related multiagent Q-learning methods. [less ▲] Detailed reference viewed: 63 (0 UL)Multi-Robot Decision Making Using Coordination Graphs ; ; Vlassis, Nikos in Proceedings of the International Conference on Advanced Robotics (ICAR) (2003) Within a group of cooperating agents the decision making of an individual agent depends on the actions of the other agents. In dynamic environments, these dependencies will change rapidly as a result of ... [more ▼] Within a group of cooperating agents the decision making of an individual agent depends on the actions of the other agents. In dynamic environments, these dependencies will change rapidly as a result of the continuously changing state. Via a context-specific decomposition of the problem into smaller subproblems, coordination graphs o#er scalable solutions to the problem of multiagent decision making. We will apply coordination graphs to the continuous domain by assigning roles to the agents and then coordinating the di#erent roles. Finally, we will demonstrate this method in the RoboCup soccer simulation domain. [less ▲] Detailed reference viewed: 38 (0 UL) |
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