When architecture meets AI: A deep reinforcement learning approach for system of systems design |
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| Affiliation: | 1. Institute of Artificial Intelligence & Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, Hunan, China;2. School of Civil Engineering, University of Leeds, LS2 9JT Leeds, UK;1. Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain;2. Dpto. de Ingeniería Mecánica y Construcción, Universitat Jaume I, Castellón, Spain;3. School of Engineering Technology, Purdue University, West Lafayette, IN, USA;1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China;2. Adam Smith Business School, University of Glasgow, Glasgow, UK;3. Institute of Innovation and Circular Economy, Asia University, Taichung, Taiwan;4. Department of Medical Research, China Medical University Hospital, Taichung, Taiwan;5. School of Software, Tsinghua University, Beijing, China;1. Academy of Arts & Design, Tsinghua University, Beijing 100085, China;2. School of Politics and Law, Zhejiang Sci-tech University, Hangzhou 310018, China;3. Design Department, Politecnico di Milano, Milan 20158, Italy;1. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Hong Kong;2. Department of Disaster Mitigation for Structures, Tongji University, Shanghai 200092, China |
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| Abstract: | How to design System of Systems has been widely concerned in recent years, especially in military applications. This problem is also known as SoS architecting, which can be boiled down to two subproblems: selecting a number of systems from a set of candidates and specifying the tasks to be completed for each selected system. Essentially, such a problem can be reduced to a combinatorial optimization problem. Traditional exact solvers such as branch-bound algorithm are not efficient enough to deal with large scale cases. Heuristic algorithms are more scalable, but if input changes, these algorithms have to restart the searching process. Re-searching process may take a long time and interfere with the mission achievement of SoS in highly dynamic scenarios, e.g., in the Mosaic Warfare. In this paper, we combine artificial intelligence with SoS architecting and propose a deep reinforcement learning approach DRL-SoSDP for SoS design. Deep neural networks and actor–critic algorithms are used to find the optimal solution with constraints. Evaluation results show that the proposed approach is superior to heuristic algorithms in both solution quality and computation time, especially in large scale cases. DRL-SoSDP can find great solutions in a near real-time manner, showing great potential for cases that require an instant reply. DRL-SoSDP also shows good generalization ability and can find better results than heuristic algorithms even when the scale of SoS is much larger than that in training data. |
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| Keywords: | Deep reinforcement learning System of systems Combinatorial optimization Tradespace exploration |
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