Name
Title | ||
|---|---|---|
Trajectories Prediction of the Black-Tailed Gull Using the Inverse Reinforcement Learning |
Abstract | ||
|---|---|---|
Revealing the route selection of wild animals is of fundamental importance in understanding their movements and foraging strategy. In this study, we attached GPS loggers to black-tailed gulls Larus crassirostris and recorded their movement trajectories during their foraging trips. Using inverse reinforcement learning (IRL), we analyzed the factors that affected their route selection. During the training phase, using pre-defined feature maps, we estimated a reward map that may affect the decision making of black-tailed gulls. The reward map can be used for predicting the trajectories of the gulls during the test phase. In addition, the resultant weight vector enabled us to analyze to which degree the black-tailed gulls favor each area. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/PERCOMW.2019.8730653 | 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) |
Keywords | Field | DocType |
Trajectory,Global Positioning System,Reinforcement learning,Image color analysis,Buildings,Conferences,Knowledge discovery | Computer science,Larus crassirostris,Weight,Inverse reinforcement learning,Global Positioning System,Artificial intelligence,Knowledge extraction,Machine learning,Foraging,Trajectory,Reinforcement learning,Distributed computing | Conference |
ISSN | ISBN | Citations |
2474-2503 | 978-1-5386-9151-9 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kanon Takemura | 1 | 0 | 0.34 |
| Tsubasa Hirakawa | 2 | 12 | 10.99 |
| Yuichi Mizutani | 3 | 0 | 1.01 |
| Hirokazu Suzuki | 4 | 0 | 1.01 |
| Michi Tsuruya | 5 | 0 | 0.34 |
| Ken Yoda | 6 | 0 | 3.38 |