• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.375-381
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• 2023

In an increasingly hyperconnected battlefield, the reliance on battlefield networks and AI-based autonomous weapons systems creates uncertainty and raises ethical concerns. This article explores the responsibility gap in operating autonomous weapons systems, using Robert Sparrow's theory. By analyzing Sparrow's arguments, we propose overcoming the responsibility gap in lethal autonomous weapon systems (LAWS). Our objective is to establish a framework of responsibility that aligns with the evolving battlefield, promoting the development and use of responsible weapon systems.

• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.47-55
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• 2020

This paper presents a real-time autonomous computation of shot numbers and aiming points against multiple soft targets on grounds by applying an unsupervised learning, k-mean clustering and Monte carlo simulation. For this computation, a 100 × 200 square meters size of virtual battlefield is created where an augmented enemy infantry platoon unit attacks, defences, and is scatted, and a virtual weapon with a lethal range of 15m is modeled. In order to determine damage types of the enemy unit: no damage, light wound, heavy wound and death, Monte carlo simulation is performed to apply the Carlton damage function for the damage effect of the soft targets. In addition, in order to achieve the damage effectiveness of the enemy units in line with the commander's intention, the optimal shot numbers and aiming point locations are calculated in less than 0.4 seconds by applying the k-mean clustering and repetitive Monte carlo simulation. It is hoped that this study will help to develop a system that reduces the decision time for 'detection-decision-shoot' process in battalion-scaled combat units operating Dronebot combat system.