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A Weighted Markov Decision Process [PDF]
Operations Research, 1992 The two most commonly considered reward criteria for Markov decision processes are the discounted reward and the long-term average reward. The first tends to “neglect” the future, concentrating on the short-term rewards, while the second one tends to do the opposite.
Krass, D, Filar, JA, Sinha, SS
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Cost-Bounded Active Classification Using Partially Observable Markov Decision Processes [PDF]
, 2018 Active classification, i.e., the sequential decision-making process aimed at data acquisition for classification purposes, arises naturally in many applications, including medical diagnosis, intrusion detection, and object tracking.
Ahmadi, Mohamadreza +3 more
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Probabilistic Hyperproperties of Markov Decision Processes [PDF]
, 2020 Hyperproperties are properties that describe the correctness of a system as a relation between multiple executions. Hyperproperties generalize trace properties and include information-flow security requirements, like noninterference, as well as requirements like symmetry, partial observation, robustness, and fault tolerance.
Dimitrova, Rayna +2 more
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An Optimal Query Assignment for Wireless Sensor Networks [PDF]
, 2014 A trade-off between two QoS requirements of wireless sensor networks: query waiting time and validity (age) of the data feeding the queries, is investigated.
Boucherie, Richard J. +6 more
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Entropic Regularization of Markov Decision Processes [PDF]
Entropy, 2019 An optimal feedback controller for a given Markov decision process (MDP) can in principle be synthesized by value or policy iteration. However, if the system dynamics and the reward function are unknown, a learning agent must discover an optimal controller via direct interaction with the environment.
Jan Peters, Jan Peters, Boris Belousov
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Markov Karar Süreci İle Modellenen Stokastik ve Çok Amaçlı Üretim/Envanter Problemlerinin Hedef Programlama Yaklaşımı İle Çözülmesi (Solving Stochastic and Multi-Objective Production/Inventory Problems Modeled By MARKOV Decision Process with Goal Programming Approach) [PDF]
Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 2009 To make decisions involving uncertainty while making future plans, Markov Decision Process (MDP), one of the stochastic approaches, may provide assistance to managers. Methods such as value iteration, policy iteration or linear programming can be used in
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Portfolio allocation under the vendor managed inventory: A Markov decision process
Journal of Applied Sciences and Environmental Management, 2017 Markov decision processes have been applied in solving a wide range of optimization problems over the years. This study provides a review of Markov decision processes and investigates its suitability for solutions to portfolio allocation problems under ...
VO Ezugwu, LI Igbinosun
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Applied Sciences, 2021 During the last decades, collaborative robots capable of operating out of their cages are widely used in industry to assist humans in mundane and harsh manufacturing tasks. Although such robots are inherently safe by design, they are commonly accompanied
Angeliki Zacharaki +2 more
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Detecting and Responding to Concept Drift in Business Processes
Algorithms, 2022 Concept drift, which refers to changes in the underlying process structure or customer behaviour over time, is inevitable in business processes, causing challenges in ensuring that the learned model is a proper representation of the new data.
Lingkai Yang +4 more
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Probabilistic opacity for Markov decision processes [PDF]
Information Processing Letters, 2015 Opacity is a generic security property, that has been defined on (non probabilistic) transition systems and later on Markov chains with labels. For a secret predicate, given as a subset of runs, and a function describing the view of an external observer, the value of interest for opacity is a measure of the set of runs disclosing the secret.
Bérard, Béatrice +2 more
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