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Student Analysis Seminar

A path-dependent variational framework for incremental information gathering
Monday, October 12, 2020
11:00-11:50 AM
Virtual East Hall Map
Information gathered along a path is inherently submodular; the incremental amount of information gained along a path decreases due to redundant observations. In addition to submodularity, the incremental amount of information gained is a function of not only the current state but also the entire history as well. This talk presents the construction of the first-order necessary optimality conditions for memory (history-dependent) Lagrangians. Path-dependent problems frequently appear in robotics and artificial intelligence, where the state such as a map is partially observable, and information can only be obtained along a trajectory by local sensing. Robotic exploration and environmental monitoring have numerous real-world applications and can be formulated using the proposed approach. Speaker(s): William Clark (Cornell University)
Building: East Hall
Event Type: Workshop / Seminar
Tags: Mathematics
Source: Happening @ Michigan from Department of Mathematics