Publication Type
Conference Proceeding Article
Version
Publisher’s Version
Publication Date
7-2019
Abstract
Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc for matching restaurants to customers. In these online to offline service problems, individuals who are responsible for supply (e.g., taxi drivers, delivery bikes or delivery van drivers) earn more by being at the ”right” place at the ”right” time. We are interested in developing approaches that learn to guide individuals to be in the ”right” place at the ”right” time (to maximize revenue) in the presence of other similar ”learning” individuals and only local aggregated observation of other agents states (e.g., only number of other taxis in same zone as current agent).
Discipline
Artificial Intelligence and Robotics | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling (ICAPS 2019): Berkeley, CA, July 11-15
First Page
655
Last Page
663
Publisher
AAAI Press
City or Country
Menlo Park, CA
Embargo Period
4-12-2020
Citation
VERMA, Tanvi; VARAKANTHAM, Pradeep; and Lau, Hoong Chuin.
Entropy based independent learning in anonymous multi-agent settings. (2019). Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling (ICAPS 2019): Berkeley, CA, July 11-15. 655-663.
Available at: https://ink.library.smu.edu.sg/sis_research/5101
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons