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Finding an optimal search path is a NP-hard problem. Since search is one of human central activities, learning spatial search behavior from human operators is a way to solve search problems. Utilizing the submodularity of search problems, this research proposes a submodular inverse reinforcement learning (SIRL) algorithm to learn humans' search behavior. The experiments demonstrate that the performance of the learned search paths outperform that of state of the art approaches (e.g., MaxEnt IRL and DIRL).
|Title of host publication||2020 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2020|
|Editors||Lino Marques, Majid Khonji, Jorge Dias|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - 4 Nov 2020|
|Event||2020 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2020 - Abu Dhabi, United Arab Emirates|
Duration: 4 Nov 2020 → 6 Nov 2020
|Name||2020 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2020|
|Conference||2020 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2020|
|Country/Territory||United Arab Emirates|
|Period||4/11/20 → 6/11/20|
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- 2 Finished
1/08/20 → 31/07/21