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Improving Wildlife Monitoring using a Multi-criteria Cooperative Target Observation Approach

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Title:Improving Wildlife Monitoring using a Multi-criteria Cooperative Target Observation Approach
Authors:Munnangi, Sai Krishna
Paruchuri, Praveen
Keywords:AI and Sustainability: The Use of AI in Sustainability Initiatives
cooperative target observation
multi criteria decision analysis
wildlife monitoring
Date Issued:07 Jan 2020
Abstract: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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/64380
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.638
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: AI and Sustainability: The Use of AI in Sustainability Initiatives


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