Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2019
Abstract
Our increasingly interconnected urban environments provide several opportunities to deploy intelligent agents—from self-driving cars, ships to aerial drones—that promise to radically improve productivity and safety. Achieving coordination among agents in such urban settings presents several algorithmic challenges—ability to scale to thousands of agents, addressing uncertainty, and partial observability in the environment. In addition, accurate domain models need to be learned from data that is often noisy and available only at an aggregate level. In this paper, I will overview some of our recent contributions towards developing planning and reinforcement learning strategies to address several such challenges present in largescale urban multiagent systems.
Discipline
Programming Languages and Compilers | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, August 10-16
First Page
6398
Last Page
6402
ISBN
9780999241141
Identifier
10.24963/ijcai.2019/895
Publisher
IJCAI
City or Country
Macau
Citation
KUMAR, Akshat.
Multiagent decision making and learning in urban environments. (2019). Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, August 10-16. 6398-6402.
Available at: https://ink.library.smu.edu.sg/sis_research/5060
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.24963/ijcai.2019/895