Results 31 to 40 of about 110 (105)
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
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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
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MARBLE: Multi-Armed Restless Bandits in Latent Markovian Environment
CoRRRestless Multi-Armed Bandits (RMABs) are powerful models for decision-making under uncertainty, yet classical formulations typically assume fixed dynamics, an assumption often violated in nonstationary environments. We introduce MARBLE (Multi-Armed Restless Bandits in a Latent Markovian Environment), which augments RMABs with a latent Markov state that
Mohsen Amiri +3 more
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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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Non-Stationary Restless Multi-Armed Bandits with Provable Guarantee
CoRROnline restless multi-armed bandits (RMABs) typically assume that each arm follows a stationary Markov Decision Process (MDP) with fixed state transitions and rewards. However, in real-world applications like healthcare and recommendation systems, these assumptions often break due to non-stationary dynamics, posing significant challenges for ...
Yu-Heng Hung +2 more
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Fresh Caching of Dynamic Contents using Restless Multi-armed Bandits
2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS)We consider a dynamic content caching problem wherein the contents get updated at a central server, and local copies of a subset of contents are cached at a local cache associated with a Base station (BS). When a content request arrives, based on whether the content is in the local cache, the BS can decide whether to fetch the content from the central ...
Ankita Koley, Chandramani Singh
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Transfer restless multi-armed bandit policy for energy-efficient heterogeneous cellular network [PDF]
EURASIP Journal on Advances in Signal Processing, 2019 Abstract This paper proposes a learning policy to improve the energy efficiency (EE) of heterogeneous cellular networks. The combination of active and inactive base stations (BS) that allows for maximizing EE is identified as a combinatorial learning problem and requires high computational complexity as well as a large signaling overhead.
Modi, Navikkumar +2 more
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IRL for Restless Multi-armed Bandits with Applications in Maternal and Child Health
Public health practitioners often have the goal of monitoring patients and maximizing patients' time spent in "favorable" or healthy states while being constrained to using limited resources. Restless multi-armed bandits (RMAB) are an effective model to solve this problem as they are helpful to allocate limited resources among many agents under ...
Gauri Jain +5 more
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A Hidden Markov Restless Multi-armed Bandit Model for Playout Recommendation Systems [PDF]
, 2017 We consider a restless multi-armed bandit (RMAB) in which there are two types of arms, say A and B. Each arm can be in one of two states, say $0$ or $1.$ Playing a type A arm brings it to state $0$ with probability one and not playing it induces state transitions with arm-dependent probabilities.
Rahul Meshram +2 more
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