Oppositional target domain estimation using grid-based simulation

Authors:
Maryam Shokri;Hamid R. Tizhoosh;Mohamed S. Kamel
Affiliations:
Department of Systems Design Engineering, University of Waterloo, 200 University Avenue West, ON, N2L 3G1 Canada;Department of Systems Design Engineering, University of Waterloo, 200 University Avenue West, ON, N2L 3G1 Canada;Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, ON, N2L 3G1 Canada
Venue:
Applied Soft Computing
Year:
2009

Citing 4
Cited 1

Robust Monte Carlo localization for mobile robots

Artificial Intelligence
Opposition-Based Learning: A New Scheme for Machine Intelligence

CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
A novel population initialization method for accelerating evolutionary algorithms

Computers & Mathematics with Applications
Evolutionary behavior learning for action-based environment modeling by a mobile robot

Applied Soft Computing

Survey A review of opposition-based learning from 2005 to 2012

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.