Results 71 to 80 of about 110 (105)

The refinement paradox and cumulative cultural evolution: Complex products of collective improvement favor conformist outcomes, blind copying, and hyper-credulity. [PDF]

PLoS Comput Biol
Miu E, Rendell L, Bowles S, Boyd R, Cownden D, Enquist M, Eriksson K, Feldman MW, Lillicrap T, McElreath R, Murray S, Ounsley J, Lala KN.   +12 more
europepmc   +1 more source

Multi-channel opportunistic access : a restless multi-armed bandit perspective

Accès opportuniste dans les systèmes de communication multi-canaux : une perspective du problème de bandit-manchot Dans cette thèse, nous abordons le problème fondamental de l'accès au spectre opportuniste dans un système de communication multi-canal.
openaire   +2 more sources

Restless Multi-Armed Bandits under Exogenous Global Markov Process

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022
Accepted for presentation at IEEE ICASSP 2022.
Michal Yemini, Tomer Gafni, Kobi Cohen   +2 more
exaly   +3 more sources

Risk-Aware Interventions in Public Health: Planning with Restless Multi-Armed Bandits [PDF]

International Joint Conference on Autonomous Agents and Multiagent Systems, 2021
Community Health Workers (CHWs) form an important component of health-care systems globally, especially in low-resource settings. CHWs are often tasked with monitoring the health of and intervening on their patient cohort. Previous work has developed several classes of Restless Multi-Armed Bandits (RMABs) that are computationally tractable and ...
Aditya Mate, Andrew Perrault, Milind Tambe   +2 more
openaire   +1 more source

On a Class of Restless Multi-armed Bandits with Deterministic Policies

2018 International Conference on Signal Processing and Communications (SPCOM), 2018
We describe and analyze a restless multi-armed bandit (RMAB) in which, in each time-slot, the instantaneous reward from the playing of an arm depends on the time since the arm was last played. This model is motivated by recommendation systems where the payoff from a recommendation on depends the recommendation history.
Prakirt Raj Jhunjhunwala, Sharayu Moharir, D. Manjunath, Aditya Gopalan   +3 more
openaire   +1 more source

Global Rewards in Restless Multi-Armed Bandits

Advances in Neural Information Processing Systems 37
Restless multi-armed bandits (RMAB) extend multi-armed bandits so pulling an arm impacts future states. Despite the success of RMABs, a key limiting assumption is the separability of rewards into a sum across arms. We address this deficiency by proposing restless-multi-armed bandit with global rewards (RMAB-G), a generalization of RMABs to global non ...
Naveen Raman 0001, Zheyuan Ryan Shi, Fei Fang 0001   +2 more
exaly   +4 more sources

On a restless multi-armed bandit problem with non-identical arms

2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2011
We consider the following learning problem motivated by opportunistic spectrum access in cognitive radio networks. There are N independent Gilbert-Elliott channels with possibly non-identical transition matrices. It is desired to have an online policy to maximize the long-term expected discounted reward from accessing one channel at each time ...
Naumaan Nayyar, Yi Gai, Bhaskar Krishnamachari   +2 more
openaire   +1 more source

Slow fading channel selection: A restless multi-armed bandit formulation

2012 International Symposium on Wireless Communication Systems (ISWCS), 2012
We deal with a multi-access wireless network in which transmitters dynamically select a frequency band to communicate on. The slow fading channel attenuations follow an autoregressive model. In the single user case, we formulate this selection problem as a restless multi-armed bandit problem and we propose two strategies to dynamically select a band at
Avrachenkov, Konstantin, Cottatellucci, Laura, Maggi, Lorenzo   +2 more
openaire   +2 more sources

Logarithmic weak regret of non-Bayesian restless multi-armed bandit

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
We consider the restless multi-armed bandit (RMAB) problem with unknown dynamics. At each time, a player chooses K out of N (N > K) arms to play. The state of each arm determines the reward when the arm is played and transits according to Markovian rules no matter the arm is engaged or passive.
Haoyang Liu, Keqin Liu, Qing Zhao 0001
openaire   +1 more source