Publication Type

Conference Proceeding Article

Publication Date

7-2012

Abstract

This research is motivated by large scale problems in urban transportation and labor mobility where there is congestion for resources and uncertainty in movement. In such domains, even though the individual agents do not have an identity of their own and do not explicitly interact with other agents, they effect other agents. While there has been much research in handling such implicit effects, it has primarily assumed deterministic movements of agents. We address the issue of decision support for individual agents that are identical and have involuntary movements in dynamic environments. For instance, in a taxi fleet serving a city, when a taxi is hired by a customer, its movements are uncontrolled and depend on (a) the customers requirement; and (b) the location of other taxis in the fleet. Towards addressing decision support in such problems, we make two key contributions: (a) A framework to represent the decision problem for selfish individuals in a dynamic population, where there is transitional uncertainty (involuntary movements); and (b) Two techniques (Fictitious Play for Symmetric Agent Populations, FP-SAP and Softmax based Flow Update, SMFU) that converge to equilibrium solutions.We show that our techniques (apart from providing equilibrium strategies) outperform “driver” strategies with respect to overall availability of taxis and the revenue obtained by the taxi drivers. We demonstrate this on a real world data set with 8,000 taxis and 83 zones (representing the entire area of Singapore).

Discipline

Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics

Publication

Proceedings of the Twenty-sixth AAAI Conference on Artificial Intelligence, 22-26 July 2010, Toronto, Ontario

First Page

1471

Last Page

1477

ISBN

9781577355687

Publisher

AAAI Press

City or Country

Menlo Park, CA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/5030