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Reasoning In Reduced Information Spaces (ONR MURI)
In this project we research algorithms for reasoning efficiently in large information spaces generated by suites of rich sensors that single and multi-agent robotic systems are typically equipped nowadays. Examles of problems studied under this project include scalable planning under uncertainty, high-dimensional planning, multi-agent planning, ways to compress information spaces, making predictions based on heterogenous information such as tracking multiple people in highly cluttered spaces.