| タイトル | Productive Information Foraging |
| 本文（外部サイト） | http://hdl.handle.net/2060/20160011186 |
| 著者（英） | Dille, Michael; Furlong, P. Michael |
| 著者所属（英） | NASA Ames Research Center |
| 発行日 | 2016-08-01 |
| 言語 | eng |
| 内容記述 | This paper presents a new algorithm for autonomous on-line exploration in unknown environments. The objective of the algorithm is to free robot scientists from extensive preliminary site investigation while still being able to collect meaningful data. We simulate a common form of exploration task for an autonomous robot involving sampling the environment at various locations and compare performance with a simpler existing algorithm that is also denied global information. The result of the experiment shows that the new algorithm has a statistically significant improvement in performance with a significant effect size for a range of costs for taking sampling actions. |
| NASA分類 | Cybernetics, Artificial Intelligence and Robotics |
| レポートNO | ARC-E-DAA-TN34778
NASA/TM-2016-219376 |
| 権利 | Copyright, Distribution under U.S. Government purpose rights |
