Conference Proceeding Article
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.
Artificial Intelligence and Robotics | Urban Studies and Planning
Intelligent Systems and Decision Analytics
Proceeding of the 25th International Joint Conference on Artificial Intelligence: IJCAI 2016, New York; United States; 2016 July 9-15
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Sequential decision making for improving efficiency in urban environments. (2016). Proceeding of the 25th International Joint Conference on Artificial Intelligence: IJCAI 2016, New York; United States; 2016 July 9-15. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3601
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