Oppositional target domain estimation using grid-based simulation

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.