Singapore Management University professor Akshat Kumar is developing computational methods for choreographing the movements of autonomous cars and ships using multi-agent planning and automated decision-making.
"One of my research aims is to develop computationally efficient techniques to realize the promise of large cooperative teams making decisions towards a common goal," Kumar says.
He bases his computational models on actual data, employing multi-agent reinforcement learning to find optimization strategies.
"We build simulators for different urban phenomena--for maritime ecosystems or for how taxis move in urban environments, for example," Kumar notes. "Using the data coming from these simulators, we can actually optimize agent policies."
Kumar also is grappling with integrating both robotic and human decision-making systems within computational algorithms, as well as the challenge of controlling vast numbers of agents while still maintaining computational efficiency.
He says there is a need to build specific capabilities into decision-making algorithms that can behave predictively under specific circumstances.
From Asian Scientist
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