UNiCEN R&D effort aims to develop, demonstrate and validate an integrated approach to computational intelligence methods for analytics, simulation, optimisation and decision support, learning, and feedback in the context of urban management and related social systems.
One distinguishing feature of our research is to promote Agent Coordination at Scale. Most AI research on multi-agent systems focus on either simulating large number of relatively simple agents or coordinating a small number of highly complex agents (such as for robotic soccer, disaster relief, Mars rover space exploration). We aim to extend the techniques to coordinate a large number of complex agents, and in particular humans (not robots) moving within a confined (but large) space in real time amidst a dynamic environment. While seeking some form of mass system-wide optimality, the individual agents will be the ones who ultimately make their decisions autonomously, i.e. simultaneous mass and personal flow control.
UNiCEN current focus on 2 key research areas:
The two research areas share the following common characteristics:
- A concern with improving the flow of people or goods in urban settings
- An emphasis on addressing problems associated with crowds, congestion and queues
- An emphasis on resource crunch, and the need to intelligently match supply with demand, especially in dealing with dynamic changes and surges in demand, given fixed supply capacity that is available over a given duration of time.
Last updated on 18 Oct 2016 .