References of "Journal of Artificial Intelligence Research"
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See detailTimed ATL: Forget Memory, Just Count
Knapik, Michal; André, Étienne; Petrucci, Laure et al

in Journal of Artificial Intelligence Research (2019), 66

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See detailInterpolable Formulas in Equilibrium Logic and Answer Set Programming
Gabbay, Dov M. UL; Pearce, David; Valverde, Agust In

in Journal of Artificial Intelligence Research (2014), abs/1401.3897

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See detailOn the Link between Partial Meet, Kernel, and Infra Contraction and its Application to Horn Logic
Booth, Richard UL; Meyer, Thomas; Varzinczak, Ivan José et al

in Journal of Artificial Intelligence Research (2011), 42

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See detailOptimal and approximate Q-value functions for decentralized POMDPs
Oliehoek, Frans A.; Spaan, Matthijs T. J.; Vlassis, Nikos UL

in Journal of Artificial Intelligence Research (2008), 32

Decision-theoretic planning is a popular approach to sequential decision making problems, because it treats uncertainty in sensing and acting in a principled way. In single-agent frameworks like MDPs and ... [more ▼]

Decision-theoretic planning is a popular approach to sequential decision making problems, because it treats uncertainty in sensing and acting in a principled way. In single-agent frameworks like MDPs and POMDPs, planning can be carried out by resorting to Q-value functions: an optimal Q-value 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 Q-value functions can be defined for decentralized POMDP models (Dec-POMDPs), and how policies can be extracted from such value functions. We define two forms of the optimal Q-value function for Dec-POMDPs: one that gives a normative description as the Q-value 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 Q-value functions that allow for efficient computation. We describe how they relate, and we prove that they all provide an upper bound to the optimal Q-value function Q*. Finally, unifying some previous approaches for solving Dec-POMDPs, we describe a family of algorithms for extracting policies from such Q-value functions, and perform an experimental evaluation on existing test problems, including a new firefighting benchmark problem. [less ▲]

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See detailPerseus: Randomized point-based value iteration for POMDPs
Spaan, M. T. J.; Vlassis, Nikos UL

in Journal of Artificial Intelligence Research (2005), 24

Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy ... [more ▼]

Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent's belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary to other point-based methods, Perseus backs up only a (randomly selected) subset of points in the belief set, sufficient for improving the value of each belief point in the set. We show how the same idea can be extended to dealing with continuous action spaces. Experimental results show the potential of Perseus in large scale POMDP problems. [less ▲]

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