Learning System for Air Combat Decision Inspired by Cognitive Mechanisms of the Brain
Unmanned aerial vehicles (UAVs) have played an important role in recent high-tech local wars. Seizing air control rights with UAVs will undoubtedly be a popular topic in future military development. Autonomous air combat is complex, antagonistic and mutable, and consequently, the decision-making tha...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8950153/ |
| _version_ | 1819276560693198848 |
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| author | Kai Zhou Ruixuan Wei Qirui Zhang Zhuofan Xu |
| author_facet | Kai Zhou Ruixuan Wei Qirui Zhang Zhuofan Xu |
| author_sort | Kai Zhou |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) have played an important role in recent high-tech local wars. Seizing air control rights with UAVs will undoubtedly be a popular topic in future military development. Autonomous air combat is complex, antagonistic and mutable, and consequently, the decision-making that depends on unmanned systems is extremely challenging with very little research having been conducted on it. An intelligent air combat learning system inspired by the learning mechanisms of the brain is proposed in this paper. In accordance with research on learning, knowledge and memory, we constructed a cognitive mechanism model of the brain. Based on this model and the inferential abilities of humans, a long short-term hierarchical multi-line learning system is established. Then, the bio-inspired architecture and the basic learning principle of the system are clarified. Taking advantage of the conclusions of studies on information theory, the relationship between the knowledge updating cycle and the system learning performance is analysed. The updating cycle length adjustment problem is transformed into an optimization problem optimization problem, and system performance improvement is guaranteed. Experiments show that the system designed in this paper can acquire confrontation abilities through self-learning without prior rules; the parallel universe mechanism can significantly improve the system's learning speed when the number of parallels is within 40, and the performance of the system improves gradually and continuously. The system can master actions similar to classical tactical manoeuvres such as the high yo-yo and the barrel-roll-attack without prior knowledge. Compared with the Bayesian inference and moving horizon optimization (BI&MHO) method, the learning system proposed in this paper is more flexible in situation assessment and in the prediction of opponents' actions. Although it cannot be deployed quickly, it has a continuous learning ability. |
| first_indexed | 2024-12-23T23:42:10Z |
| format | Article |
| id | doaj.art-c154559f172340f590108f597e8a30c1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:42:10Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c154559f172340f590108f597e8a30c12022-12-21T17:25:36ZengIEEEIEEE Access2169-35362020-01-0188129814410.1109/ACCESS.2020.29640318950153Learning System for Air Combat Decision Inspired by Cognitive Mechanisms of the BrainKai Zhou0https://orcid.org/0000-0002-0753-5097Ruixuan Wei1Qirui Zhang2https://orcid.org/0000-0003-1680-5877Zhuofan Xu3https://orcid.org/0000-0003-2603-907XAeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaJoint Operations College, National Defence University of People’s Liberation Army, Shijiazhuang, ChinaUnmanned aerial vehicles (UAVs) have played an important role in recent high-tech local wars. Seizing air control rights with UAVs will undoubtedly be a popular topic in future military development. Autonomous air combat is complex, antagonistic and mutable, and consequently, the decision-making that depends on unmanned systems is extremely challenging with very little research having been conducted on it. An intelligent air combat learning system inspired by the learning mechanisms of the brain is proposed in this paper. In accordance with research on learning, knowledge and memory, we constructed a cognitive mechanism model of the brain. Based on this model and the inferential abilities of humans, a long short-term hierarchical multi-line learning system is established. Then, the bio-inspired architecture and the basic learning principle of the system are clarified. Taking advantage of the conclusions of studies on information theory, the relationship between the knowledge updating cycle and the system learning performance is analysed. The updating cycle length adjustment problem is transformed into an optimization problem optimization problem, and system performance improvement is guaranteed. Experiments show that the system designed in this paper can acquire confrontation abilities through self-learning without prior rules; the parallel universe mechanism can significantly improve the system's learning speed when the number of parallels is within 40, and the performance of the system improves gradually and continuously. The system can master actions similar to classical tactical manoeuvres such as the high yo-yo and the barrel-roll-attack without prior knowledge. Compared with the Bayesian inference and moving horizon optimization (BI&MHO) method, the learning system proposed in this paper is more flexible in situation assessment and in the prediction of opponents' actions. Although it cannot be deployed quickly, it has a continuous learning ability.https://ieeexplore.ieee.org/document/8950153/Autonomous air combatbio-inspiredcognitive mechanismlong short-term memorylearning systemunmanned aerial vehicles |
| spellingShingle | Kai Zhou Ruixuan Wei Qirui Zhang Zhuofan Xu Learning System for Air Combat Decision Inspired by Cognitive Mechanisms of the Brain IEEE Access Autonomous air combat bio-inspired cognitive mechanism long short-term memory learning system unmanned aerial vehicles |
| title | Learning System for Air Combat Decision Inspired by Cognitive Mechanisms of the Brain |
| title_full | Learning System for Air Combat Decision Inspired by Cognitive Mechanisms of the Brain |
| title_fullStr | Learning System for Air Combat Decision Inspired by Cognitive Mechanisms of the Brain |
| title_full_unstemmed | Learning System for Air Combat Decision Inspired by Cognitive Mechanisms of the Brain |
| title_short | Learning System for Air Combat Decision Inspired by Cognitive Mechanisms of the Brain |
| title_sort | learning system for air combat decision inspired by cognitive mechanisms of the brain |
| topic | Autonomous air combat bio-inspired cognitive mechanism long short-term memory learning system unmanned aerial vehicles |
| url | https://ieeexplore.ieee.org/document/8950153/ |
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