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Content Provider | ACM Digital Library |
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Author | Decraene, James Anderson, Mark Low, Malcolm Yoke Hean |
Abstract | A maritime counter-piracy scenario is modeled using the agent-based simulation platform MANA. This simulation model is employed to investigate the requirements for defending a large commercial vessel, relying principally on non-lethal deterrents, against pirate hijacking. To assist this research, we utilize the data farming methodology to identify the "landscape of possibilities", i.e., data farming is employed to efficiently generate and examine a large range of simulation model variants which altogether depict a comprehensive overview of potential outcomes. Moreover, we complement this study through the evaluation of Automated Red Teaming (ART) to uncover the commercial vessel's critical vulnerabilities against pirates. ART differs from data farming by exploiting the principles of artificial evolution to automatically generate simulation model variants of interest. Both data farming and ART techniques are applied to our maritime counter-piracy simulation model in this paper. The experimental results provide complementary insights which may assist defense experts in future analyses and decision makings. |
Starting Page | 1 |
Ending Page | 8 |
Page Count | 8 |
File Format | |
ISBN | 9781450300698 |
DOI | 10.1145/1878537.1878709 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2010-04-11 |
Publisher Place | San Diego |
Access Restriction | Subscribed |
Subject Keyword | Maritime counter-piracy Automated red teaming Agent-based modelling and simulation Data farming |
Content Type | Text |
Resource Type | Article |
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