Results 31 to 40 of about 68,246 (249)

Optimal Control of Partially Observable Piecewise Deterministic Markov Processes

, 2018
In this paper we consider a control problem for a Partially Observable Piecewise Deterministic Markov Process of the following type: After the jump of the process the controller receives a noisy signal about the state and the aim is to control the ...
Bäuerle, Nicole, Lange, Dirk
core   +1 more source

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

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, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source

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, Shroff, Ness B., Sun, Yin, Uysal-Biyikoglu, Elif, Yates, Roy D.   +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

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

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, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Stochastic-based pavement performance and deterioration models: A review of techniques and applications

Alexandria Engineering Journal
Infrastructure assets, such as pavements, naturally deteriorate over time due to traffic loads, environmental conditions, and other external factors. Traditionally, deterministic models have been employed to predict performance, aiding in work planning ...
Che Shobry Shahid, Zulhilmi Amir Zainal, Nur Izzi Md Yusoff, Noraziah Mohammad, Zamira Hasanah Zamzuri, Iswandaru Widyatmoko   +5 more
doaj   +1 more source

Wide sense one-dependent processes with embedded Harris chains and their applications in inventory management [PDF]

, 2003
In this paper we consider stochastic processes with an embedded Harris chain. The embedded Harris chain describes the dependence structure of the stochastic process.
Bazsa-Oldenkamp, E.M. (Emö), Iseger, P. den   +1 more
core  

Verification of Uncertain POMDPs Using Barrier Certificates

, 2018
We consider a class of partially observable Markov decision processes (POMDPs) with uncertain transition and/or observation probabilities. The uncertainty takes the form of probability intervals.
Ahmadi, Mohamadreza, Cubuktepe, Murat, Jansen, Nils, Topcu, Ufuk   +3 more
core   +1 more source

Artificial Intelligence in Autonomous Mobile Robot Navigation: From Classical Approaches to Intelligent Adaptation

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, Angelo Cardellicchio, Cosimo Patruno, Massimiliano Nitti, Nicola Pedrocchi, Vito Renò   +5 more
wiley   +1 more source

Continuous-time Markov decision processes under the risk-sensitive average cost criterion

, 2015
This paper studies continuous-time Markov decision processes under the risk-sensitive average cost criterion. The state space is a finite set, the action space is a Borel space, the cost and transition rates are bounded, and the risk-sensitivity ...
Chen, Xian, Wei, Qingda
core   +1 more source