Results 11  20
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401
Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs
 In Proc. of Int. Joint Conference on Autonomous Agents and Multi Agent Systems
, 2004
"... Partially observable decentralized decision making in robot teams is fundamentally different from decision making in fully observable problems. Team members cannot simply apply singleagent solution techniques in parallel. Instead, we must turn to game theoretic frameworks to correctly model the pro ..."
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Cited by 92 (2 self)
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Partially observable decentralized decision making in robot teams is fundamentally different from decision making in fully observable problems. Team members cannot simply apply singleagent solution techniques in parallel. Instead, we must turn to game theoretic frameworks to correctly model the problem. While partially observable stochastic games (POSGs) provide a solution model for decentralized robot teams, this model quickly becomes intractable. We propose an algorithm that approximates POSGs as a series of smaller, related Bayesian games, using heuristics such as QMDP to provide the future discounted value of actions. This algorithm trades off limited lookahead in uncertainty for computational feasibility, and results in policies that are locally optimal with respect to the selected heuristic. Empirical results are provided for both a simple problem for which the full POSG can also be constructed, as well as more complex, robotinspired, problems.
MAA*: A heuristic search algorithm for solving decentralized POMDPs
 In Proceedings of the TwentyFirst Conference on Uncertainty in Artificial Intelligence
, 2005
"... We present multiagent A * (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partiallyobservable Markov decision problems (DECPOMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate i ..."
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Cited by 92 (21 self)
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We present multiagent A * (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partiallyobservable Markov decision problems (DECPOMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multirobot coordination, network traffic control, or distributed resource allocation. Solving such problems effectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA * has significant advantages. We introduce an anytime variant of MAA * and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems. 1
Decentralized control of cooperative systems: Categorization and complexity analysis
 Journal of Artificial Intelligence Research
, 2004
"... Decentralized control of cooperative systems captures the operation of a group of decisionmakers that share a single global objective. The difficulty in solving optimally such problems arises when the agents lack full observability of the global state of the system when they operate. The general pr ..."
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Cited by 88 (9 self)
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Decentralized control of cooperative systems captures the operation of a group of decisionmakers that share a single global objective. The difficulty in solving optimally such problems arises when the agents lack full observability of the global state of the system when they operate. The general problem has been shown to be NEXPcomplete. In this paper, we identify classes of decentralized control problems whose complexity ranges between NEXP and P. In particular, we study problems characterized by independent transitions, independent observations, and goaloriented objective functions. Two algorithms are shown to solve optimally useful classes of goaloriented decentralized processes in polynomial time. This paper also studies information sharing among the decisionmakers, which can improve their performance. We distinguish between three ways in which agents can exchange information: indirect communication, direct communication and sharing state features that are not controlled by the agents. Our analysis shows that for every class of problems we consider, introducing direct or indirect communication does not change the worstcase complexity. The results provide a better understanding of the complexity of decentralized control problems that arise in practice and facilitate the development of planning algorithms for these problems. 1.
Communication Decisions in Multiagent Cooperation: Model and Experiments
 In Proceedings of the Fifth International Conference on Autonomous Agents
, 2001
"... In multiagent cooperation, agents share a common goal, which is evaluated through a global utility function. However, an agent typically cannot observe the global state of an uncertain environment, and therefore they must communicate with each other in order to share the information needed for deci ..."
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Cited by 77 (12 self)
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In multiagent cooperation, agents share a common goal, which is evaluated through a global utility function. However, an agent typically cannot observe the global state of an uncertain environment, and therefore they must communicate with each other in order to share the information needed for deciding which actions to take. We argue that, when communication incurs a cost (due to resource consumption, for example), whether to communicate or not also becomes a decision to make. Hence, communication decision becomes part of the overall agent decision problem. In order to explicitly address this problem, we present a multiagent extension to Markov decision processes in which communication can be modeled as an explicit action that incurs a cost. This framework provides a foundation for a quantied study of agent coordination policies and provides both motivation and insight to the design of heuristic approaches. An example problem is studied under this framework. From this example we can see the impact communication policies have on the overall agent policies, and what implications we can nd toward the design of agent coordination policies.
TransitionIndependent Decentralized Markov Decision Processes
, 2003
"... There has been substantial progress with formal models for sequential decision making by individual agents using the Markov decision process (MDP). However, similar treatment of multiagent systems is lacking. A recent complexity result, showing that solving decentralized MDPs is NEXPhard, provides ..."
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Cited by 76 (15 self)
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There has been substantial progress with formal models for sequential decision making by individual agents using the Markov decision process (MDP). However, similar treatment of multiagent systems is lacking. A recent complexity result, showing that solving decentralized MDPs is NEXPhard, provides a partial explanation. To overcome this complexity barrier, we identify a general class of transitionindependent decentralized MDPs that is widely applicable. The class consists of independent collaborating agents that are tied together through a global reward function that depends upon both of their histories. We present a novel algorithm for solving this class of problems and examine its properties. The result is the first effective technique to solve optimally a class of decentralized MDPs. This lays the foundation for further work in this area on both exact and approximate solutions.
Bounded Policy Iteration for Decentralized POMDPs
 In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence
, 2005
"... We present a bounded policy iteration algorithm for infinitehorizon decentralized POMDPs. Policies are represented as joint stochastic finitestate controllers, which consist of a local controller for each agent. We also let a joint controller include a correlation device that allows the agents to ..."
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Cited by 76 (19 self)
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We present a bounded policy iteration algorithm for infinitehorizon decentralized POMDPs. Policies are represented as joint stochastic finitestate controllers, which consist of a local controller for each agent. We also let a joint controller include a correlation device that allows the agents to correlate their behavior without exchanging information during execution, and show that this leads to improved performance. The algorithm uses a fixed amount of memory, and each iteration is guaranteed to produce a controller with value at least as high as the previous one for all possible initial state distributions. For the case of a single agent, the algorithm reduces to Poupart and Boutilier’s bounded policy iteration for POMDPs. 1
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... 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 t ..."
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Cited by 64 (2 self)
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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 singlestate 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 decisionmaking analogue of belief propagation in Bayesian networks. Second, we focus on learning the behavior of the agents in sequential decisionmaking tasks. We introduce different modelfree reinforcementlearning techniques, unitedly called Sparse Cooperative Qlearning, which approximate the global actionvalue 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 edgebased decomposition of the actionvalue 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 that our method outperforms related multiagent reinforcementlearning methods based on temporal differences.
Optimal and approximate Qvalue functions for decentralized POMDPs
 J. Artificial Intelligence Research
"... Decisiontheoretic planning is a popular approach to sequential decision making problems, because it treats uncertainty in sensing and acting in a principled way. In singleagent frameworks like MDPs and POMDPs, planning can be carried out by resorting to Qvalue functions: an optimal Qvalue functi ..."
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Cited by 62 (26 self)
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Decisiontheoretic planning is a popular approach to sequential decision making problems, because it treats uncertainty in sensing and acting in a principled way. In singleagent frameworks like MDPs and POMDPs, planning can be carried out by resorting to Qvalue functions: an optimal Qvalue function Q ∗ is computed in a recursive manner by dynamic programming, and then an optimal policy is extracted from Q ∗. In this paper we study whether similar Qvalue functions can be defined for decentralized POMDP models (DecPOMDPs), and how policies can be extracted from such value functions. We define two forms of the optimal Qvalue function for DecPOMDPs: one that gives a normative description as the Qvalue function of an optimal pure joint policy and another one that is sequentially rational and thus gives a recipe for computation. This computation, however, is infeasible for all but the smallest problems. Therefore, we analyze various approximate Qvalue functions that allow for efficient computation. We describe how they relate, and we prove that they all provide an upper bound to the optimal Qvalue function Q ∗. Finally, unifying some previous approaches for solving DecPOMDPs, we describe a family of algorithms for extracting policies from such Qvalue functions, and perform an experimental evaluation on existing test problems, including a new firefighting benchmark problem. 1.