Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2016

Abstract

Rapid "urbanization" (more than 50% of world's population now resides in cities) coupled with the natural lack of coordination in usage of common resources (ex: bikes, ambulances, taxis, traffic personnel, attractions) has a detrimental effect on a wide variety of response (ex: waiting times, response time for emergency needs) and coverage metrics (ex: predictability of traffic/security patrols) in cities of today. Motivated by the need to improve response and coverage metrics in urban environments, my research group is focussed on building intelligent agent systems that make sequential decisions to continuously match available supply of resources to an uncertain demand for resources. Our broad approach to generating these sequential decision strategies is through a combination of data analytics (to obtain a model) and multistage optimization (planning/scheduling) under uncertainty (to solve the model). While we perform data analytics, our contributions are focussed on multi-stage optimization under uncertainty. We exploit key properties of urban environments, namely homogeneity and anonymity, limited influence of individual entities, abstraction and near decomposability to solve "multi-stage optimization under uncertainty" effectively and efficiently.

Discipline

Artificial Intelligence and Robotics | Urban Studies and Planning

Publication

Proceeding of the 25th International Joint Conference on Artificial Intelligence: IJCAI 2016, New York; United States; 2016 July 9-15

Publisher

AAAI Press

City or Country

New York

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