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25
Exploiting Coordination Locales in Distributed POMDPs via Social Model Shaping
"... Distributed POMDPs provide an expressive framework for modeling multiagent collaboration problems, but NEXPComplete complexity hinders their scalability and application in realworld domains. This paper introduces a subclass of distributed POMDPs, and TREMOR, an algorithm to solve such distributed ..."
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Cited by 44 (16 self)
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Distributed POMDPs provide an expressive framework for modeling multiagent collaboration problems, but NEXPComplete complexity hinders their scalability and application in realworld domains. This paper introduces a subclass of distributed POMDPs, and TREMOR, an algorithm to solve such distributed POMDPs. The primary novelty of TREMOR is that agents plan individually with a single agent POMDP solver and use social model shaping to implicitly coordinate with other agents. Experiments demonstrate that TREMOR can provide solutions orders of magnitude faster than existing algorithms while achieving comparable, or even superior, solution quality.
Optimizing FixedSize Stochastic Controllers for POMDPs and Decentralized POMDPs
"... POMDPs and their decentralized multiagent counterparts, DECPOMDPs, offer a rich framework for sequential decision making under uncertainty. Their computational complexity, however, presents an important research challenge. One approach that effectively addresses the intractable memory requirements ..."
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Cited by 30 (14 self)
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POMDPs and their decentralized multiagent counterparts, DECPOMDPs, offer a rich framework for sequential decision making under uncertainty. Their computational complexity, however, presents an important research challenge. One approach that effectively addresses the intractable memory requirements of current algorithms is based on representing agent policies as finitestate controllers. In this paper, we propose a new approach that uses this representation and formulates the problem as a nonlinear program (NLP). The NLP defines an optimal policy of a desired size for each agent. This new representation allows a wide range of powerful nonlinear programming algorithms to be used to solve POMDPs and DECPOMDPs. Although solving the NLP optimally is often intractable, the results we obtain using an offtheshelf optimization method are competitive with stateoftheart POMDP algorithms and outperform stateoftheart DECPOMDP algorithms. Our approach is easy to implement and it opens up promising research directions for solving POMDPs and DECPOMDPs using nonlinear programming methods. 1.
A Survey on Sensor Networks from a MultiAgent perspective
"... Sensor networks arise as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical system (MEMS) ease the development and proliferation of this kind of networks in a wide range of realworld applications. Mult ..."
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Cited by 26 (0 self)
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Sensor networks arise as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical system (MEMS) ease the development and proliferation of this kind of networks in a wide range of realworld applications. MultiAgent systems (MAS) have been identified as one of the most suitable technologies to contribute to this domain due to their appropriateness for modeling autonomous selfaware sensors in a flexible way. Firstly, this survey summarizes the actual challenges and research areas concerning sensor networks while identifying the most relevant MAS contributions. Secondly, we propose a taxonomy for sensor networks that classifies them depending on their features (and the research problems they pose). Finally, we identify some open future research directions and opportunities for MAS research. 1.
Incremental Clustering and Expansion for Faster Optimal Planning in Decentralized POMDPs
, 2013
"... This article presents the stateoftheart in optimal solution methods for decentralized partially observable Markov decision processes (DecPOMDPs), which are general models for collaborative multiagent planning under uncertainty. Building off the generalized multiagent A * (GMAA*) algorithm, which ..."
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Cited by 18 (12 self)
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This article presents the stateoftheart in optimal solution methods for decentralized partially observable Markov decision processes (DecPOMDPs), which are general models for collaborative multiagent planning under uncertainty. Building off the generalized multiagent A * (GMAA*) algorithm, which reduces the problem to a tree of oneshot collaborative Bayesian games (CBGs), we describe several advances that greatly expand the range of DecPOMDPs that can be solved optimally. First, we introduce lossless incremental clustering of the CBGs solved by GMAA*, which achieves exponential speedups without sacrificing optimality. Second, we introduce incremental expansion of nodes in the GMAA * search tree, which avoids the need to expand all children, the number of which is in the worst case doubly exponential in the node’s depth. This is particularly beneficial when little clustering is possible. In addition, we introduce new hybrid heuristic representations that are more compact and thereby enable the solution of larger DecPOMDPs. We provide theoretical guarantees that, when a suitable heuristic is used, both incremental clustering and incremental expansion yield algorithms that are both complete and search equivalent. Finally, we present extensive empirical results demonstrating that GMAA*ICE, an algorithm that synthesizes these advances, can optimally solve DecPOMDPs of unprecedented size.
Decentralized Control of Partially Observable Markov Decision Processes
"... Abstract — Markov decision processes (MDPs) are often used to model sequential decision problems involving uncertainty under the assumption of centralized control. However, many large, distributed systems do not permit centralized control due to communication limitations (such as cost, latency or co ..."
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Cited by 14 (8 self)
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Abstract — Markov decision processes (MDPs) are often used to model sequential decision problems involving uncertainty under the assumption of centralized control. However, many large, distributed systems do not permit centralized control due to communication limitations (such as cost, latency or corruption). This paper surveys recent work on decentralized control of MDPs in which control of each agent depends on a partial view of the world. We focus on a general framework where there may be uncertainty about the state of the environment, represented as a decentralized partially observable MDP (DecPOMDP), but consider a number of subclasses with different assumptions about uncertainty and agent independence. In these models, a shared objective function is used, but plans of action must be based on a partial view of the environment. We describe the frameworks, along with the complexity of optimal control and important properties. We also provide an overview of exact and approximate solution methods as well as relevant applications. This survey provides an introduction to what has become an active area of research on these models and their solutions. I.
MultiAgent Role Allocation: Issues, Approaches, and Multiple Perspectives
 AUTON AGENT MULTIAGENT SYST
"... In cooperative multiagent systems, roles are used as a design concept when creating large systems, they are known to facilitate specialization of agents, and they can help to reduce interference in multirobot domains. The types of tasks that the agents are asked to solve and the communicative capa ..."
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Cited by 11 (0 self)
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In cooperative multiagent systems, roles are used as a design concept when creating large systems, they are known to facilitate specialization of agents, and they can help to reduce interference in multirobot domains. The types of tasks that the agents are asked to solve and the communicative capabilities of the agents significantly affect the way roles are used in cooperative multiagent systems. Along with a discussion of these issues about roles in multiagent systems, this article compares computational models of the role allocation problem, presents the notion of explicitly versus implicitly defined roles, gives a survey of the methods used to approach role allocation problems, and concludes with a list of open research questions related to roles in multiagent systems.
Abstracting Influences for Efficient Multiagent Coordination Under Uncertainty
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Quadratic Programming for MultiTarget Tracking
 In MSDM Workshop, AAMAS 2009
, 2009
"... We consider the problem of tracking multiple, partially observed targets using multiple sensors arranged in a given configuration. We model the problem as a special case of a (finite horizon) DECPOMDP. We present a quadratic program whose globally optimal solution yields an optimal tracking joint p ..."
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Cited by 1 (0 self)
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We consider the problem of tracking multiple, partially observed targets using multiple sensors arranged in a given configuration. We model the problem as a special case of a (finite horizon) DECPOMDP. We present a quadratic program whose globally optimal solution yields an optimal tracking joint policy, one that maximizes the expected targets detected over the given horizon. However, a globally optimal solution to the QP cannot always be found since the QP is nonconvex. To remedy this, we present two linearizations of the QP to equivalent 01 mixed integer linear programs (MIPs) whose optimal solutions, which may be always found through the branch and bound method, for example, yield optimal joint policies. Computational experience on different sensor configurations shows that finding an optimal joint policy by solving the proposed MIPs is much faster than using existing algorithms for the problem. 1