Results 51 to 60 of about 26,957 (159)

Comparing Labelled Markov Decision Processes

CoRR, 2020
35 pages, 7 ...
Stefan Kiefer, Qiyi Tang 0001
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Factored Beliefs for Machine Agents in Decentralized Partially Observable Markov Decision Processes

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022
A shared mental model (SMM) is a foundational structure in high performing, task-oriented teams and aid humans in determining their teammate's goals and intentions.
Joshua Lapso, Gilbert Peterson
doaj   +1 more source

Communicating via Markov Decision Processes

, 2021
ICML ...
Sokota, S, de Witt, CS, Igl, M, Zintgraf, L, Torr, P, Strohmeier, M, Kolter, JZ, Whiteson, S, Foerster, J   +8 more
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Markov Decision Processing Networks

CoRR
We introduce Markov Decision Processing Networks (MDPNs) as a multiclass queueing network model where service is a controlled, finite-state Markov process. The model exhibits a decision-dependent service process where actions taken influence future service availability.
Sanidhay Bhambay, Thirupathaiah Vasantam, Neil Walton   +2 more
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Robust Synchronization in Markov Decision Processes [PDF]

, 2014
We consider synchronizing properties of Markov decision processes (MDP), viewed as generators of sequences of probability distributions over states. A probability distribution is p-synchronizing if the probability mass is at least p in some state, and a sequence of probability distributions is weakly p-synchronizing, or strongly p-synchronizing if ...
Doyen, Laurent, Shirmohammadi, Mahsa, Massart, Thierry   +2 more
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Bounded-parameter Markov decision processes

Artificial Intelligence, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Robert Givan, Sonia M. Leach, Thomas L. Dean   +2 more
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Limit Synchronization in Markov Decision Processes [PDF]

, 2014
Markov decision processes (MDP) are finite-state systems with both strategic and probabilistic choices. After fixing a strategy, an MDP produces a sequence of probability distributions over states. The sequence is eventually synchronizing if the probability mass accumulates in a single state, possibly in the limit.
Doyen, Laurent, Shirmohammadi, Mahsa, Massart, Thierry   +2 more
openaire   +3 more sources

Modeling Local Search Metaheuristics Using Markov Decision Processes

Algorithms
Local search metaheuristics like tabu search or simulated annealing are popular heuristic optimization algorithms for finding near-optimal solutions for combinatorial optimization problems.
Rubén Ruiz-Torrubiano, Deepak Dhungana, Sarita Paudel, Himanshu Buckchash   +3 more
doaj   +1 more source

Planning Under Uncertainty Applications in Power Plants Using Factored Markov Decision Processes

Energies, 2020
Due to its ability to deal with non-determinism and partial observability, represent goals as an immediate reward function and find optimal solutions, planning under uncertainty using factored Markov Decision Processes (FMDPs) has increased its ...
Alberto Reyes, L. Enrique Sucar, Pablo H. Ibargüengoytia, Eduardo F. Morales   +3 more
doaj   +1 more source

Algebraic Markov Decision Processes [PDF]

, 2005
In this paper, we provide an algebraic approach to Markov Decision Processes (MDPs), which allows a unified treatment of MDPs and includes many existing models (quantitative or qualitative) as particular cases. In algebraic MDPs, rewards are expressed in a semiring structure, uncertainty is represented by a decomposable plausibility measure valued on a
Perny, Patrice, Spanjaard, Olivier, Weng, Paul   +2 more
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