Results 11 to 20 of about 110 (105)
Networked Restless Multi-Armed Bandits for Mobile Interventions [PDF]
International Joint Conference on Autonomous Agents and Multiagent Systems, 2022 Motivated by a broad class of mobile intervention problems, we propose and study restless multi-armed bandits (RMABs) with network effects. In our model, arms are partially recharging and connected through a graph, so that pulling one arm also improves the state of neighboring arms, significantly extending the previously studied setting of fully ...
Han-Ching Ou +6 more
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Avoiding Starvation of Arms in Restless Multi-Armed Bandits
International Joint Conference on Autonomous Agents and Multiagent Systems, 2023 Restless multi-armed bandits (RMAB) is a popular framework for optimizing performance with limited resources under uncertainty. It is an extremely useful model for monitoring beneficiaries (arms) and executing timely interventions using health workers (limited resources) to ensure optimal benefit in public health settings.
LI, Dexun, VARAKANTHAM, Pradeep
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Towards Soft Fairness in Restless Multi-Armed Bandits
CoRR, 2022 Restless multi-armed bandits (RMAB) is a framework for allocating limited resources under uncertainty. It is an extremely useful model for monitoring beneficiaries and executing timely interventions to ensure maximum benefit in public health settings (e.g., ensuring patients take medicines in tuberculosis settings, ensuring pregnant mothers listen to ...
Dexun Li, Pradeep Varakantham
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Best Arm Identification in Restless Markov Multi-Armed Bandits
IEEE Transactions on Information Theory, 2023 We study the problem of identifying the best arm in a multi-armed bandit environment when each arm is a time-homogeneous and ergodic discrete-time Markov process on a common, finite state space. The state evolution on each arm is governed by the arm's transition probability matrix (TPM).
P. N. Karthik +2 more
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Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits
CoRR, 2022 Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more.
Dexun Li, Pradeep Varakantham
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Indexability of Finite State Restless Multi-Armed Bandit and Rollout Policy
CoRR, 2023 15 Pages, submitted to ...
Vishesh Mittal +3 more
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Interactive Restless Multi-armed Bandit Game and Swarm Intelligence Effect [PDF]
New Generation Computing, 2015 We obtain the conditions for the emergence of the swarm intelligence effect in an interactive game of restless multi-armed bandit (rMAB). A player competes with multiple agents. Each bandit has a payoff that changes with a probability $p_{c}$ per round.
Shunsuke Yoshida +2 more
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Uncertainty-of-Information Scheduling: A Restless Multiarmed Bandit Framework
IEEE Transactions on Information Theory, 2022 This paper proposes using the uncertainty of information (UoI), measured by Shannon's entropy, as a metric for information freshness. We consider a system in which a central monitor observes multiple binary Markov processes through a communication channel. The UoI of a Markov process corresponds to the monitor's uncertainty about its state.
Gongpu Chen +2 more
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Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems [PDF]
International Joint Conference on Autonomous Agents and Multiagent Systems, 2022 We propose Streaming Bandits, a Restless Multi-Armed Bandit (RMAB) framework in which heterogeneous arms may arrive and leave the system after staying on for a finite lifetime. Streaming Bandits naturally capture the health-intervention planning problem, where health workers must manage the health outcomes of a patient cohort while new patients join ...
Aditya S. Mate +4 more
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A sensing policy based on confidence bounds and a restless multi-armed bandit model [PDF]
2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012 In proceedings of the 46th Asilomar conference ...
Jan Oksanen +2 more
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