Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Killian, Jackson"'
Autor:
Gordon, Lucia, Behari, Nikhil, Collier, Samuel, Bondi-Kelly, Elizabeth, Killian, Jackson A., Ressijac, Catherine, Boucher, Peter, Davies, Andrew, Tambe, Milind
Publikováno v:
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. AI for Good. Pages 5977-5985. 2023
Much of Earth's charismatic megafauna is endangered by human activities, particularly the rhino, which is at risk of extinction due to the poaching crisis in Africa. Monitoring rhinos' movement is crucial to their protection but has unfortunately pro
Externí odkaz:
http://arxiv.org/abs/2409.18104
Restless multi-armed bandits (RMABs) are a popular framework for algorithmic decision making in sequential settings with limited resources. RMABs are increasingly being used for sensitive decisions such as in public health, treatment scheduling, anti
Externí odkaz:
http://arxiv.org/abs/2308.09726
The success of many healthcare programs depends on participants' adherence. We consider the problem of scheduling interventions in low resource settings (e.g., placing timely support calls from health workers) to increase adherence and/or engagement.
Externí odkaz:
http://arxiv.org/abs/2305.12640
Motivated by applications such as machine repair, project monitoring, and anti-poaching patrol scheduling, we study intervention planning of stochastic processes under resource constraints. This planning problem has previously been modeled as restles
Externí odkaz:
http://arxiv.org/abs/2303.00799
Autor:
Ou, Han-Ching, Siebenbrunner, Christoph, Killian, Jackson, Brooks, Meredith B, Kempe, David, Vorobeychik, Yevgeniy, Tambe, Milind
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 improv
Externí odkaz:
http://arxiv.org/abs/2201.12408
We introduce robustness in \textit{restless multi-armed bandits} (RMABs), a popular model for constrained resource allocation among independent stochastic processes (arms). Nearly all RMAB techniques assume stochastic dynamics are precisely known. Ho
Externí odkaz:
http://arxiv.org/abs/2107.01689
Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which $N$ independent processes are managed. However, previous work only study the offline setting where problem dynamics are known. We
Externí odkaz:
http://arxiv.org/abs/2106.12024
Research in artificial intelligence (AI) for social good presupposes some definition of social good, but potential definitions have been seldom suggested and never agreed upon. The normative question of what AI for social good research should be "for
Externí odkaz:
http://arxiv.org/abs/2105.01774
We propose and study Collpasing Bandits, a new restless multi-armed bandit (RMAB) setting in which each arm follows a binary-state Markovian process with a special structure: when an arm is played, the state is fully observed, thus "collapsing" any u
Externí odkaz:
http://arxiv.org/abs/2007.04432
Autor:
Killian, Jackson A., Wilder, Bryan, Sharma, Amit, Shah, Daksha, Choudhary, Vinod, Dilkina, Bistra, Tambe, Milind
Publikováno v:
KDD 2019: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India whe
Externí odkaz:
http://arxiv.org/abs/1902.01506