Results 31 to 40 of about 67,931 (252)
, 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 Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +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
Multi-state models for evaluating conversion options in life insurance [PDF]
, 2017 In this paper we propose a multi-state model for the evaluation of the conversion option contract. The multi-state model is based on age-indexed semi-Markov chains that are able to reproduce many important aspects that influence the valuation of the ...
D'Amico, Guglielmo +3 more
core +3 more sources
Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning
Advanced Intelligent Discovery, EarlyView.Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang +4 more
wiley +1 more source
Alexandria Engineering JournalInfrastructure 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 +5 more
doaj +1 more source
Approximate Policy Iteration for Generalized Semi-Markov Decision Processes: an Improved Algorithm [PDF]
, 2008 In the context of time-dependent problems of planning under uncertainty, most of the problem's complexity comes from the concurrent interaction of simultaneous processes.
Fabiani, Patrick +2 more
core
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
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 Hemimetric Extension of Simulation for Semi-Markov Decision Processes [PDF]
, 2018 Semi-Markov decision processes (SMDPs) are continuous-time Markov decision processes where the residence-time on states is governed by generic distributions on the positive real line. In this paper we consider the problem of comparing two SMDPs with respect to their time-dependent behaviour.
Pedersen, Mathias Ruggaard +3 more
openaire +2 more sources