Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
8-2012
Abstract
Congestion games model a wide variety of real-world resource congestion problems, such as selfish network routing, traffic route guidance in congested areas, taxi fleet optimization and crowd movement in busy areas. However, existing research in congestion games assumes: (a) deterministic movement of agents between resources; and (b) perfect rationality (i.e. maximizing their own expected value) of all agents. Such assumptions are not reasonable in dynamic domains where decision support has to be provided to humans. For instance, in optimizing the performance of a taxi fleet serving a city, movement of taxis can be involuntary or nondeterministic (decided by the specific customer who hires the taxi) and more importantly, taxi drivers may not follow advice provided by the decision support system (due to bounded rationality of humans). To that end, we contribute: (a) a general framework for representing congestion games under uncertainty for populations with assorted notions of rationality. (b) a scalable approach for solving the decision problem for perfectly rational agents which are in the mix with boundedly rational agents; and (c) a detailed evaluation on a synthetic and real world data set to illustrate the usefulness of our new approach with respect to key social welfare metrics in the context of an assorted human-agent population. An interesting result from our experiments on a real-world taxi fleet optimization problem is that it is better (in terms of revenue and operational efficiency) for taxi drivers to follow perfectly rational strategies irrespective of the percentage of drivers not following the advice.
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Publication
Uncertainty in Artificial Intelligence: Proceedings of the Twenty-eighth Conference: August 15-17, 2012, Catalina Island, United States
First Page
44
Last Page
53
ISBN
9780974903989
Publisher
AUAI Press
City or Country
Corvallis, OR
Citation
AHMED, Asrar; VARAKANTHAM, Pradeep Reddy; and CHENG, Shih-Fen.
Uncertain Congestion Games with Assorted Human Agent Populations. (2012). Uncertainty in Artificial Intelligence: Proceedings of the Twenty-eighth Conference: August 15-17, 2012, Catalina Island, United States. 44-53.
Available at: https://ink.library.smu.edu.sg/sis_research/1568
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://worldcat.org/isbn/9780974903989
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons