Oppositional target domain estimation using grid-based simulation |
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| Authors: | Maryam Shokri Hamid R Tizhoosh Mohamed S Kamel |
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| Affiliation: | 1. Department of Systems Design Engineering, University of Waterloo, 200 University Avenue West, ON, N2L 3G1 Canada;2. Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, ON, N2L 3G1 Canada;1. Emergency Department, Canakkale State Hospital, Canakkale, Turkey;2. Emergency Department, Kars State Hospital, Kars, Turkey;3. Department of Emergency Medicine, Sisli Hamidiye Etfal Training and Research Hospital, ?stanbul, Turkey;4. Department of Emergency Medicine, Tepecik Training and Research Hospital, ?zmir, Turkey;5. Department of Emergency Medicine, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey;1. Industrial Engineering and Management Systems, Amirkabir University of Technology, Teheran, Iran;2. Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada;1. Wageningen University, Education and Learning Sciences, the Netherlands;2. Eindhoven University of Technology, Eindhoven School of Education, the Netherlands;3. Fontys University of Applied Science for Teacher Education Sittard, the Netherlands |
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| Abstract: | In this paper we address the problem of estimating the target domain for search and navigation problems. We propose oppositional target domain estimation by modeling the search and navigation environment as a grid. Typically real-world applications exhibit an environment that is extremely large, dramatically diminishing the usability of intelligent agents for search and navigation. The reduction of the size of environment, hence, can help to increase the efficiency and applicability of the agents. We address this issue by modeling the environment as a grid and estimating the target domain inside the environment. The target domain is a reduced space which includes the target. The proposed technique is specifically concerned with reducing the environment using the concept of opposition. Experimental results show significant reduction of the environment size resulting in a shorter search time. |
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