Location
Grand Wailea, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2020 12:00 AM
End Date
10-1-2020 12:00 AM
Description
Wildlife Monitoring is very important for maintaining sustainability of environment. In this paper we pose Wildlife Monitoring as Cooperative Target Observation (CTO) problem and propose a Multi Criteria Decision Analysis (MCDA) based algorithm named MCDA-CTO, to maximize the observation of different animal species by Unmanned Aerial Vehicles (UAVs) and to effectively handle multiple target types and the multiple criteria that arise due to targets and environmental factors, during decision making. UAVs have uncertainty in observation of targets which makes it challenging to develop a high-quality monitoring strategy. We therefore develop monitoring techniques that explicitly take actions to improve belief about the true type of targets being observed. In wildlife monitoring, it is often reasonable to assume that the observers may themselves be a subject of observation by unknown adversaries (poachers). Randomizing the observer’s actions can therefore help to make the target observation strategy less predictable. We then provide experimental validation that shows that the techniques we develop provide a higher (true positive/true negative) ratio along with better randomization than state of the art approaches.
Improving Wildlife Monitoring using a Multi-criteria Cooperative Target Observation Approach
Grand Wailea, Hawaii
Wildlife Monitoring is very important for maintaining sustainability of environment. In this paper we pose Wildlife Monitoring as Cooperative Target Observation (CTO) problem and propose a Multi Criteria Decision Analysis (MCDA) based algorithm named MCDA-CTO, to maximize the observation of different animal species by Unmanned Aerial Vehicles (UAVs) and to effectively handle multiple target types and the multiple criteria that arise due to targets and environmental factors, during decision making. UAVs have uncertainty in observation of targets which makes it challenging to develop a high-quality monitoring strategy. We therefore develop monitoring techniques that explicitly take actions to improve belief about the true type of targets being observed. In wildlife monitoring, it is often reasonable to assume that the observers may themselves be a subject of observation by unknown adversaries (poachers). Randomizing the observer’s actions can therefore help to make the target observation strategy less predictable. We then provide experimental validation that shows that the techniques we develop provide a higher (true positive/true negative) ratio along with better randomization than state of the art approaches.
https://aisel.aisnet.org/hicss-53/os/ai_and_sustainability/2