Results 41 to 50 of about 257 (142)
Reinforcement Learning for Finite-Horizon Restless Multi-Armed Multi-Action Bandits
CoRR, 2021 We study a finite-horizon restless multi-armed bandit problem with multiple actions, dubbed R(MA)^2B. The state of each arm evolves according to a controlled Markov decision process (MDP), and the reward of pulling an arm depends on both the current state of the corresponding MDP and the action taken. The goal is to sequentially choose actions for arms
Guojun Xiong +2 more
openaire +2 more sources
Similarity and Consistency in Algorithm‐Guided Exploration
Journal of Behavioral Decision Making, Volume 38, Issue 5, December 2025.ABSTRACT Algorithmic advice has the potential to significantly improve human decision‐making, especially in dynamic and complex tasks that require a balance between exploration and exploitation. This study examines conditions under which individuals are willing to accept advice from algorithms in such scenarios, focusing on the interaction between ...
Ludwig Danwitz +6 more
wiley +1 more source
Networked Restless Multi-Arm Bandits with Reinforcement Learning
CoRRRestless Multi-Armed Bandits (RMABs) are a powerful framework for sequential decision-making, widely applied in resource allocation and intervention optimization challenges in public health. However, traditional RMABs assume independence among arms, limiting their ability to account for interactions between individuals that can be common and ...
Hanmo Zhang, Zenghui Sun, Kai Wang 0040
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The Restless Multi-Armed Bandit Formulation of the Cognitive Compressive Sensing Problem
IEEE Transactions on Signal Processing, 2015 In this paper we introduce the Cognitive Compressive Sensing (CCS) problem, modeling a Cognitive Receiver (CR) that optimizes the $K$ projections of a $N>K$ dimensional vector dynamically, by optimizing the objective of correctly detecting the maximum number of idle entries, while updating each time its Bayesian beliefs on the future vector ...
Saeed Bagheri, Anna Scaglione
openaire +1 more source
AI Magazine, Volume 45, Issue 4, Page 526-536, Winter 2024.Abstract Harnessing the wide‐spread availability of cell phones, many nonprofits have launched mobile health (mHealth) programs to deliver information via voice or text to beneficiaries in underserved communities, with maternal and infant health being a key area of such mHealth programs.
Shresth Verma +8 more
wiley +1 more source
Online model selection with stochastic rising bandits [PDF]
, 2022 LAUREA MAGISTRALEQuesta tesi si pone nell'ambito dei Multi-Armed-Bandits (MABs) stocastici, ovvero l'insieme di quelle tecniche di selezione online che riescono a scegliere un'azione (in gergo tecnico chiamata arm) osservando solamente il risultato ...
Pirola, Matteo
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On Optimality of Myopic Policy for Restless Multi-Armed Bandit Problem: An Axiomatic Approach [PDF]
IEEE Transactions on Signal Processing, 2012 Due to its application in numerous engineering problems, the restless multi-armed bandit (RMAB) problem is of fundamental importance in stochastic decision theory. However, solving the RMAB problem is well known to be PSPACE-hard, with the optimal policy usually intractable due to the exponential computation complexity.
Kehao Wang 0001, Lin Chen 0002
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The role of rumors in the emergence and diffusion of pogroms
Conflict Resolution Quarterly, Volume 41, Issue 3, Page 235-252, Spring 2024.Abstract In studies on single pogroms, but especially in analyses of waves of pogroms, the central role of rumor communication in the run‐up to, but also in the spread of pogroms has been emphasized time and again. In the following, the functions and types of rumor communication will be examined in more detail in order to understand their role in the ...
Werner Bergmann
wiley +1 more source
Decision-Focused Evaluation: Analyzing Performance of Deployed Restless Multi-Arm Bandits
CoRR, 2023 11 pages, 3 figures, AI for Social Good Workshop (AAAI'23)
Paritosh Verma +4 more
openaire +2 more sources
Budget-limited multi-armed bandits [PDF]
, 2012 Decision making under uncertainty is one of the most important challenges within the research field of artificial intelligence, as they present many everyday situations that agents have to face.
Tran-Thanh, Long
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