Results 41 to 50 of about 82,501 (282)
Time series semi-Markov decision process with variable costs for maintenance planning [PDF]
, 2016 Deciding when and how to maintain offshore wind turbines is becoming even more complex as the size of wind farms increases, while accessibility is challenging compared to onshore wind farms.
Dawid, R., McMillan, D., Revie, M.
core
, 2017 We consider linear programming (LP) problems in infinite dimensional spaces that are in general computationally intractable. Under suitable assumptions, we develop an approximation bridge from the infinite-dimensional LP to tractable finite convex ...
Esfahani, Peyman Mohajerin +3 more
core +1 more source
Advanced Intelligent Systems, EarlyView.This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi +3 more
wiley +1 more source
A connection-level call admission control using genetic algorithm for MultiClass multimedia services in wireless networks [PDF]
, 2006 Call admission control in a wireless cell in a personal communication system (PCS) can be modeled as an M/M/C/C queuing system with m classes of users. Semi-Markov Decision Process (SMDP) can be used to optimize channel utilization with upper bounds on ...
Hong, X, Ni, Q, Xiao, Y
core
Update or Wait: How to Keep Your Data Fresh
, 2017 In this work, we study how to optimally manage the freshness of information updates sent from a source node to a destination via a channel. A proper metric for data freshness at the destination is the age-of-information, or simply age, which is defined ...
Koksal, C. Emre +4 more
core +1 more source
Inference Strategies for Solving Semi-Markov Decision Processes
, 2012 Semi-Markov decision processes are used to formulate many control problems and also play a key role in hierarchical reinforcement learning. In this chapter we show how to translate the decision making problem into a form that can instead be solved by inference and learning techniques.
Hoffman, M, de Freitas, N
openaire +2 more sources
Advanced Intelligent Systems, EarlyView.This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy +2 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.Artificial intelligence (AI) is reshaping autonomous mobile robot navigation beyond classical pipelines. This review analyzes how AI techniques are integrated into core navigation tasks, including path planning and control, localization and mapping, perception, and context‐aware decision‐making. Learning‐based, probabilistic, and soft‐computing methods
Giovanna Guaragnella +5 more
wiley +1 more source
Multi‐agent reinforcement learning based transmission scheme for IRS‐assisted multi‐UAV systems
IET Communications, 2023 In this paper, a transmission scheme based on multi‐agent reinforcement learning for intelligent reflecting surface (IRS)‐assisted multiple unmanned aerial vehicles (UAVs) systems is proposed.
Yumo Mei +4 more
doaj +1 more source
Constrained Cost-Coupled Stochastic Games with Independent State Processes
, 2006 We consider a non-cooperative constrained stochastic games with N players with the following special structure. With each player there is an associated controlled Markov chain.
Altman, E. +5 more
core +6 more sources