Optimal searching for a randomly located target in a bounded known region

Abou-Gabal, Hamdy M.; El–Hadidy, Mohamed Abd Allah

doi:10.1504/ijcsm.2015.071811

Cited by 15 publications

(2 citation statements)

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“…El-Hadidy and El-Bagoury (2015) work on a search problem where a single searcher tries to detect a three-dimensional randomly located target in a known zone, with the objective of minimizing the expected value of the time required to detect the target. Gabal and El-Hadidy (2015) study a search problem where a single searcher searches for a randomly located target in a bounded known region. The authors aim to minimize the expected value of the time for detecting the target.…”

Section: Literature Reviewmentioning

confidence: 99%

Approximate Dynamic Programming for Optimal Search With an Obstacle

Göçgün¹

2019

Scitech

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In this paper, we study a class of optimal search problems where the search region includes a target and an obstacle, each of which has some shape. The search region is divided into small grid cells and the searcher examines one of those cells at each time period with the objective of finding the target with minimum expected cost. The searcher may either take an action that is quick but risky, or another one that is slow but safe, and incurs different cost for these actions. We formulate these problems as Markov Decision Processes (MDPs), but because of the intractability of the state space, we approximately solve the MDPs using an Approximate Dynamic Programming (ADP) technique and compare its performance against heuristic decision rules. Our numerical experiments reveal that the ADP technique outperforms heuristics on majority of problem instances. Specifically, the ADP technique performs better than the best heuristic policy in 17 out of 24 problem sets. The percent improvement in those 17 problem sets is on average 7.3%.

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Section: Literature Reviewmentioning

confidence: 99%

Approximate Dynamic Programming for Optimal Search With an Obstacle

Göçgün¹

2019

Scitech

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“…Furthermore, interesting search strategies that give the minimum expected value of the cost of detecting the lost target have been studied. In the case of a randomly located target, many authors, such as El-Hadidy et al [5,6,12,13,38,39] discussed this problem in the plane when the located target has symmetric and asymmetric distributions and with less information available to the searchers. In these papers, the authors desired to minimize the expected time for detecting the target.…”

Section: Introductionmentioning

confidence: 99%

On Probabilistic Cooperative Search Model to Detect a Lost Target in N-Disjoint Areas

El-Hadidy,

Fakharany

2024

Stat., optim. inf. comput.

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This paper presents a new probabilistic coordinated search technique for finding a randomly located target in n-disjoint known regions by using n-searchers. Each region contains one searcher. The searchers use advanced technology to communicate with each other. The purpose of this paper is to obtain the candidate utility function namely the expected value of the time for detecting the target. Additionally, to minimize this expected value given a restricted amount of time. We present a special case when the target has a multinomial distribution. This important for searching about a valuable target missing at sea or lost at wilderness area.

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Coordinated search for a lost target on one of 2 intersecting lines

Abou-Gabal

2018

Math Methods in App Sciences

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MSC Codes: 60K30; 90B40This paper addresses the coordinated search problem, where a hidden target is randomly located and static on one of 2 intersecting lines. Two searchers start the search for the lost target from the intersection point of the 2 lines, and each searcher aims to detect the target on his line. We study the problem of minimizing the expected time for the return of one of the searchers to the intersection point after the other searcher meets the target. We find the necessary conditions for the optimal search strategy.

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Optimal searching for a randomly located target in a bounded known region

Cited by 15 publications

References 20 publications

Approximate Dynamic Programming for Optimal Search With an Obstacle

Approximate Dynamic Programming for Optimal Search With an Obstacle

On Probabilistic Cooperative Search Model to Detect a Lost Target in N-Disjoint Areas

Coordinated search for a lost target on one of 2 intersecting lines

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