Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2023

Abstract

In large scale multi-agent systems like taxi fleets, individual agents (taxi drivers) are self interested (maximizing their own profits) and this can introduce inefficiencies in the system. One such inefficiency is with regards to the "required" availability of taxis at different time periods during the day. Since a taxi driver can work for limited number of hours in a day (e.g., 8-10 hours in a city like Singapore), there is a need to optimize the specific hours, so as to maximize individual as well as social welfare. Technically, this corresponds to solving a large scale multi-stage selfish routing game with transition uncertainty. Existing work in addressing this problem is either unable to handle “driver" constraints (e.g., breaks during work hours) or not scalable. To that end, we provide a novel mechanism that builds on replicator dynamics through ideas from behavior cloning. We demonstrate that our methods provide significantly better policies than the existing approach in terms of improving individual agent revenue and overall agent availability.

Keywords

large taxi fleet, equilibrium solution, game theory, optimization, replicator dynamics, data/policy completion

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems, London, 2023 May 29 - June 2

First Page

552

Last Page

560

Identifier

10.5555/3545946.3598683

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.5555/3545946.3598683

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