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Incremental Clustering and Expansion for Faster Optimal Planning in Decentralized POMDPs
, 2013
"... This article presents the state-of-the-art in optimal solution methods for decentralized partially observable Markov decision processes (Dec-POMDPs), 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 state-of-the-art in optimal solution methods for decentralized partially observable Markov decision processes (Dec-POMDPs), 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 one-shot collaborative Bayesian games (CBGs), we describe several advances that greatly expand the range of Dec-POMDPs 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 Dec-POMDPs. 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 Dec-POMDPs of unprecedented size.
Team Behavior in Interactive Dynamic Influence Diagrams with Applications to Ad Hoc Teams
"... Planning for ad hoc teamwork is challenging because it involves in-dividual agents collaborating with others without any prior coordi-nation. However, individual decision making in multiagent settings faces the task of having to reason about other agents ’ actions, which in turn involves reasoning a ..."
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Cited by 3 (3 self)
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Planning for ad hoc teamwork is challenging because it involves in-dividual agents collaborating with others without any prior coordi-nation. However, individual decision making in multiagent settings faces the task of having to reason about other agents ’ actions, which in turn involves reasoning about others. An established approxi-mation that operationalizes this approach is to bound the infinite nesting from below by introducing level 0 models, which results in suboptimal team solutions in cooperative settings. We demonstrate this limitation and mitigate it by integrating learning into planning. The augmented framework is applied to ad hoc teamwork.
systems
, 2001
"... Delocalization and conductance quantization in one-dimensional ..."
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Cited by 1 (0 self)
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Delocalization and conductance quantization in one-dimensional
Team Behavior in Interactive Dynamic Influence Diagrams with Applications to Ad Hoc Teams (Extended Abstract)
"... ABSTRACT Planning for ad hoc teamwork is challenging because it involves individual agents collaborating with others without any prior coordination. However, individual decision making in multiagent settings faces the task of having to reason about other agents' actions, which in turn involves ..."
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ABSTRACT Planning for ad hoc teamwork is challenging because it involves individual agents collaborating with others without any prior coordination. However, individual decision making in multiagent settings faces the task of having to reason about other agents' actions, which in turn involves reasoning about others. An established approximation that operationalizes this approach is to bound the infinite nesting from below by introducing level 0 models, which results in suboptimal team solutions in cooperative settings. We demonstrate this limitation and mitigate it by integrating learning into planning. The augmented framework is applied to ad hoc teamwork.
Iterative Online Planning in Multiagent Settings with Limited Model Spaces and PAC Guarantees
"... Methods for planning in multiagent settings often model other agents ’ possible behaviors. However, the space of these models – whether these are policy trees, finite-state controllers or inten-tional models – is very large and thus arbitrarily bounded. This may exclude the true model or the optimal ..."
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Methods for planning in multiagent settings often model other agents ’ possible behaviors. However, the space of these models – whether these are policy trees, finite-state controllers or inten-tional models – is very large and thus arbitrarily bounded. This may exclude the true model or the optimal model. In this paper, we present a novel iterative algorithm for online planning that consid-ers a limited model space, updates it dynamically using data from interactions, and provides a provable and probabilistic bound on the approximation error. We ground this approach in the context of graphical models for planning in partially observable multiagent settings – interactive dynamic influence diagrams. We empirically demonstrate that the limited model space facilitates fast solutions and that the true model often enters the limited model space.
4 Team Behavior in Interactive Dynamic Influence Diagrams with Applications to Ad Hoc Teams
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Fast Solving of Influence Diagrams for Multiagent Planning on GPU-enabled Architectures
"... Abstract: Planning under uncertainty in multiagent settings is highly intractable because of history and plan space com-plexities. Probabilistic graphical models exploit the structure of the problem domain to mitigate the com-putational burden. In this paper, we introduce the first parallelization o ..."
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Abstract: Planning under uncertainty in multiagent settings is highly intractable because of history and plan space com-plexities. Probabilistic graphical models exploit the structure of the problem domain to mitigate the com-putational burden. In this paper, we introduce the first parallelization of planning in multiagent settings on a CPU-GPU heterogeneous system. In particular, we focus on the algorithm for exactly solving interactive dynamic influence diagrams, which is a recognized graphical models for multiagent planning. Beyond paral-lelizing the standard Bayesian inference, the computation of decisions ’ expected utilities are parallelized. The GPU-based approach provides significant speedup on two benchmark problems. 1
