Multi-agent target searching with time constraints using game-theoretic approaches

Hazra, Tanmoy; Kumar, Suthikshn; Nene, Manisha J.

doi:10.1108/k-01-2017-0039

Cited by 4 publications

(5 citation statements)

References 40 publications

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“…We now use the known estimates for N (n). So in [Hardy 1999] an estimate of Gauss is given, showing that the number N (n) is equal to the area of the figure composed of unit squares of the lattice, for which the lower left corner lies in the circle C n . Since the greatest distance between the points of the unit square does not exceed √ 2, all unit squares that intersect the boundary of the circle C n are located in the ring…”

Section: Protection Of the Net Structurementioning

confidence: 99%

The object detection by autonomous apparatus as a solution of the Buffon needle problem

Гузев

Tsitsiashvili

Osipova

2019

Math. Mech. Compl. Sys.

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The problem of object detection by autonomous apparatus is considered. The probabilistic formulation of the problem is proposed by means of a reduction to the classical Buffon problem. The latter naturally arises when the problem is formulated in the coordinate system associated with the apparatus. The problem of detection is considered for devices moving in the open space along a circle around one body, for vehicles patrolling along the linear boundary protecting the bodies, and for devices protecting the system of bodies. The problem of object detection was shown to admit an analysis in the presence of an asymptotic parameter determined by the ratio of the local size of the apparatus scanning area to the global size of the problem under consideration. For all problems, the minimum number of apparatuses that could detect a penetrating object with probability one was calculated.

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Section: Protection Of the Net Structurementioning

confidence: 99%

The object detection by autonomous apparatus as a solution of the Buffon needle problem

Гузев

Tsitsiashvili

Osipova

2019

Math. Mech. Compl. Sys.

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“…The particular combinations of locations of base stations that yield the smallest cumulative distances are considered to be optimal (shown in Equation (3)). If more than one smallest cumulative distance exists, then the optimal combinations of locations of base stations are finally chosen based on the smallest distance among the distances from the farthest blocks to their respective nearest base stations considering all the optimal combinations (Hazra et al , 2017): …”

Section: Proposed Approachmentioning

confidence: 99%

“…So, the computational cost of determining the optimal locations of base stations increases factorially. It is a limitation of the proposed approach but this state explosion problem can be mitigated with the help of high-performance systems (Hazra et al , 2017).…”

Section: Proposed Approachmentioning

confidence: 99%

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Two-agent cooperative search model with Petri nets

Hazra

Kumar

Nene

2018

IJIUS

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Purpose The purpose of this paper is to propose a model for a two-agent multi-target-searching scenario in a two-dimensional region, where some places of the region have limited resource capacity in terms of the number of agents that can simultaneously pass through those places and few places of the region are unreachable that expand with time. The proposed cooperative search model and Petri net model facilitate the search operation considering the constraints mentioned in the paper. The Petri net model graphically illustrates different scenarios and helps the agents to validate the strategies. Design/methodology/approach In this paper, the authors have applied an optimization approach to determine the optimal locations of base stations, a cooperative search model, inclusion–exclusion principle, Cartesian product to optimize the search operation and a Petri net model to validate the search technique. Findings The proposed approach finds the optimal locations of the base stations in the region. The proposed cooperative search model allows various constraints such as resource capacity, time-dependent unreachable places/obstacles, fuel capacities of the agents, two types of targets assigned to two agents and limited sortie lengths. On the other hand, a Petri net model graphically represents whether collisions/deadlocks between the two agents are possible or not for a particular combination of paths as well as effect of time-dependent unreachable places for different combination of paths are also illustrated. Practical implications The problem addressed in this paper is similar to various real-time problems such as rescue operations during/after flood, landslide, earthquake, accident, patrolling in urban areas, international borders, forests, etc. Thus, the proposed model can benefit various organizations and departments such as rescue operation authorities, defense organizations, police departments, etc. Originality/value To the best of the authors’ knowledge, the problem addressed in this paper has not been completely explored, and the proposed cooperative search model to conduct the search operation considering the above-mentioned constraints is new. To the best of the authors’ knowledge, no paper has modeled time-dependent unreachable places with the help of Petri net.

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“…A wrong strategy will reduce the efficiency of the agent search, so the cooperative strategy between multi-agents is the key of the multi-agent collaborative target search. Hazra et al [9] introduced the Shapley function and fuzzy Shapley function to facilitate target search in a two-dimensional region with time constraints by minimizing the mission time and fuel usage. Cooper et al [10] presented a method for analyzing the upper bound for time to find a target under the potential field guidance algorithm assuming a radially expanding search area.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Collaborative Target Search Based on the Multi-Agent Deep Deterministic Policy Gradient with Emotional Intrinsic Motivation

Zhang,

Zheng,

Wang

et al. 2023

Applied Sciences

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Multi-agent collaborative target search is one of the main challenges in the multi-agent field, and deep reinforcement learning (DRL) is a good way to learn such a task. However, DRL always faces the problem of sparse reward, which to some extent reduces its efficiency in task learning. Introducing intrinsic motivation has proved to be a useful way to make the sparse reward in DRL. So, based on the multi-agent deep deterministic policy gradient (MADDPG) structure, a new MADDPG algorithm with the emotional intrinsic motivation name MADDPG-E is proposed in this paper for the multi-agent collaborative target search. In MADDPG-E, a new emotional intrinsic motivation module with three emotions, joy, sadness, and fear, is designed. The three emotions are defined by corresponding psychological knowledge to the multi-agent embodied situations in an environment. An emotional steady-state variable function H is then designed to help judge the goodness of the emotions. Based on H, an emotion-based intrinsic reward function is finally proposed. With the designed emotional intrinsic motivation module, the multi-agent system always tries to make itself joy, which means it always learns to search the target. To show the effectiveness of the proposed MADDPG-E algorithm, two kinds of simulation experiments with a determined initial position and random initial position, respectively, are carried out, and comparisons are performed with MADDPG as well as MADDPG-ICM (MADDPG with an intrinsic curiosity module). The results show that with the designed emotional intrinsic motivation module, MADDPG-E has a higher learning speed and better learning stability, and the advantage is more obvious when facing complex situations.

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Multi-agent target searching with time constraints using game-theoretic approaches

Cited by 4 publications

References 40 publications

The object detection by autonomous apparatus as a solution of the Buffon needle problem

The object detection by autonomous apparatus as a solution of the Buffon needle problem

Two-agent cooperative search model with Petri nets

Multi-Agent Collaborative Target Search Based on the Multi-Agent Deep Deterministic Policy Gradient with Emotional Intrinsic Motivation

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