Modeling with Non-cooperative Agents: Destructive and Non-Destructive Search Algorithms for Randomly Located Objects

Abstract

The problem of plastic anti-personal mine detection is well known. These devices are typically 75mm in diameter and between 25mm to 50mm in thickness. Some types contain no metal, but their explosive (RDX, TNT) can be considered as a dielectric material with a dielectric constant between 3.5 and 4.0. This electromagnetic property enables a radar to identify mines. It is true that the radars operating "through the air" provide a high detection rate. However, in the case of operating "into the ground", the problems are significant and inherently reduce performance. Target identification is done in the presence of the air-ground interface, which usually produces a higher amplitude signal than an anti-personal mine Chignell (1998). This context can justify why the difficulties of detecting anti-personal mines are formidable. A family of search mobots controlled by a new algorithm proposed in this research could be a solution 1 for operating on the ground. In this Chapter we 2 address the general question of what is the best strategy to search efficiently for randomly located objects (target sites). We propose a new agent based algorithm for searching in an unpredictable environment. The originality of our work consists in applying a non-cooperative strategy, namely the distributed Goore Game model, as opposed to applying the classical collaborative and competitive strategies, or individual strategies. This research covers both the destructive search and the non-destructive search. The first occurs when the agent visits the target only one time. The latter can be performed in either of the two cases -if the target becomes temporarily inactive or if it leaves the area. The proposed algorithm has two versions: one when the agent can move with a step equal to unity and the other when the step of the agent follows a Levy flight distribution. The second version is inspired by the work of A.M. Reynolds et al.

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“…2). Although the extreme points for the two features record simultaneity in time, the discrepancy of the results obtained in the summer of 2005 is clearly highlighted through their opposition in the third year of the period analysed according to Calitoiu, Milici, 2011. Thus, there results an easy way for the analysis of sports performance in the long run and a clear outline of the potential defaults and malfunctions that may occur as a result of an accident or inadequacy of the training and coaching program according to RaŃă, Rîşneac & Milici, 2007. Another suggestive representation of the coefficients in table no.…”

Autonomous search and counter-targeting using Levy search models

“…The crossover point on Fig. 11 between the expected time for the searcher to detect the target (using (8)) and the given value of counter targeting critical duration, T lock , provides insights into the ability for the searcher to complete its mission Fig. 11.…”

“…Viswanathan et al recognized this attribute in the natural foraging habits of animals, determining that several species exhibit Levy characteristics in their search patterns [6], [7]. Calitoiu and Milici demonstrate that the Levy distribution exhibits characteristics advantageous to groups or swarms of animals foraging over large areas [8], [9]. Flenner et al prove that the use of a Levy distribution on leg lengths exhibits advantages over a continuous sweeping search while searching for moving targets [10].…”