Results 131 to 140 of about 56,185 (161)

A unified approach for semi-Markov decision processes with discounted and average reward criteria

Proceeding of the 11th World Congress on Intelligent Control and Automation, 2014
On the basis of the sensitivity-based optimization, we develop a unified optimization approach for semi-Markov decision processes (SMDPs) with infinite horizon discounted and average reward criteria. We show that the sensitivity formula under average reward criteria is a limitation case of discounted reward criteria.
Yanjie Li, Huijing Wang, Haoyao Chen
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On Markov Reward Approach to Failure Criticality Importance Assessment for Aging Multi-state System

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO), 2016
The paper presents the Markov Reward approach to failure critical importance assessment for the aging multi-state system. Aging is treated as increasing failure rate. Failure criticality importance for multi-state system is directly calculated via calculation of mean number of system failures.
Shay Toledano, Inna Gartsman, Guy Avitan, Ilia Frenkel, Lev Khvatskin   +4 more
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An Algorithmic Approach to Discrete Time Non-homogeneous Backward Semi-Markov Reward Processes with an Application to Disability Insurance

Methodology and Computing in Applied Probability, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
F. Stemberg, MANCA, Raimondo, Dmitrii Silvestrov   +2 more
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A unified approach to adaptive control of average reward Markov decision processes

OR Spektrum, 1988
The paper presents a general optimization method for adaptive average reward Markov decision problems. Optimal decisions are determined by applying after each observation of the state and estimation of the unknown parameter a policy improvement step to an auxiliary value function, converging with increasing time to the true relative value.
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A Symbolic Approach to the Analysis of Multi-Formalism Markov Reward Models

2013
When modelling large systems, modularity is an important concept, as it aids modellers to master the complexity of their model. Moreover, employing different modelling formalisms within the same modelling project has the potential to ease the description of various parts or aspects of the overall system.
Kai Lampka, Markus Siegle
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A Unified Approach to Reliability, Availability, Performability Analysis Based on Markov Processes with Rewards

2019
This paper discusses a unified approach to reliability, availability and performabilityanalysis of complex engineering systems. Theoretical basis of this approach is continuous-timediscrete state Markov processes with rewards. From reliability modeling point of view complexsystems are the systems with static and dynamic redundancy, imperfect fault ...
Vikotora, V., Lubkov, N., Stepanyants, A.   +2 more
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The Markov Reward Approach for Selecting a Traction Electric Motor Based on Reliability Features

2018
This chapter presents the Markov reward approach to the comparative analysis of different types of traction electric motors for hybrid-electric propulsion systems, that is, multi-state systems (MSSs), for icebreaker ships operating in Arctic waters. The preliminary results show that there are several appropriate machine types and that it is therefore ...
Igor Bolvashenkov, Hans-Georg Herzog, Ilia Frenkel, Lev Khvatskin, Anatoly Lisnianski   +4 more
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Error Bounds and Comparison Results: The Markov Reward Approach For Queueing Networks

2010
This chapter presents an approach to compare two Queueing Networks. Here one may typically think of one network to be a solvable modification of another unsolvable one of practical interest.
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A Dynamic and Task-Independent Reward Shaping Approach for Discrete Partially Observable Markov Decision Processes

2023
Sepideh Nahali, Hajer Ayadi, Jimmy X. Huang, Esmat Pakizeh, Mir Mohsen Pedram, Leila Safari   +5 more
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A symbolic approach to the state graph based analysis of high-level Markov reward models

2007
Markov reward models considered in this thesis are compactly described by means of Markovian extensions of well-known high-level model description formalisms. For numerically computing performance and dependability (= performability) measures of high-level system models, the latter must be transformed into low-level representations, where the ...
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