Searched for: author:"Spaan, M.T.J."
(1 - 7 of 7)
document
Satsangi, Yash (author), Whiteson, Shimon (author), Oliehoek, F.A. (author), Spaan, M.T.J. (author)
In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty about one or more hidden variables. For example, a mobile robot takes sensory actions to efficiently navigate in a new environment. While partially observable Markov decision processes (POMDPs) provide a natural model for such problems, reward...
journal article 2018
document
Oliehoek, Frans A. (author), Spaan, M.T.J. (author), Terwijn, Bas (author), Robbel, Philipp (author), Messias, João V. (author)
This article describes the MultiAgent Decision Process (MADP) toolbox, a software library to support planning and learning for intelligent agents and multiagent systems in uncertain environments. Key features are that it supports partially observable environments and stochastic transition models; has unified support for single- and multiagent...
journal article 2017
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Oliehoek, F.A. (author), Whiteson, S. (author), Spaan, M.T.J. (author)
Dec-POMDPs are a powerful framework for planning in multiagent systems, but are provably intractable to solve. This paper proposes a factored forward-sweep policy computation method that tackles the stages of the problem one by one, exploiting weakly coupled structure at each of these stages. An empirical evaluation shows that the loss in...
conference paper 2013
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Oliehoek, F.A. (author), Whiteson, S. (author), Spaan, M.T.J. (author)
Dec-POMDPs are a powerful framework for planning in multiagent systems, but are provably intractable to solve. This paper proposes a factored forward-sweep policy computation method that tackles the stages of the problem one by one, exploiting weakly coupled structure at each of these stages. An empirical evaluation shows that the loss in...
conference paper 2013
document
Oliehoek, F.A. (author), Spaan, M.T.J. (author), Amato, C. (author), Whiteson, S. (author)
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...
journal article 2013
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Spaan, M.T.J. (author), Oliehoek, F.A. (author)
Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful framework for optimal decision making under the assumption of instantaneous communication. We focus on a delayed communication setting (MPOMDP-DC), in which broadcasted information is delayed by at most one time step. In this paper, we show that computation of...
conference paper 2012
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Spaan, M.T.J. (author), Oliehoek, F.A. (author), Amato, C. (author)
We advance the state of the art in optimal solving of decentralized partially observable Markov decision processes (Dec-POMDPs), which provide a formal model for multiagent planning under uncertainty.
conference paper 2011
Searched for: author:"Spaan, M.T.J."
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