In recent years, the rapid development of artificial intelligence and data science has given rise to the study of data driven algorithms in highly volatile systems. The scheduling of complex shop floor resources falls...
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In recent years, the rapid development of artificial intelligence and data science has given rise to the study of data driven algorithms in highly volatile systems. The scheduling of complex shop floor resources falls into such a category, which is often non-linear in nature, time varying, multi-objective, and subject to interruptions. Ergo, the machine learning-based scheduling, has become a research hotspot and attracted the attention of many scholars. In the literature, the research methods employed in solving scheduling problems are based on various perspectives, such as mathematical programming, combinatorial optimization, and heuristic rules. However, due to the inherent complexity of the problem, many issues remain to be addressed. In particular, with the availability of production data, the progress of computing power, and the breakthrough in intelligent algorithms, a novel branch of data driven algorithms present great potential, for example, the deep learning and reinforcement learning-based algorithms. To reveal the value of machine learning-based scheduling methods, bibliometric analysis was conducted to analyse the relevant articles and documents from the year 1980 to 2019. Finally, the future research trend in the domain of machine learning-based scheduling is considered and tips are provided for researchers as well as practitioners to find leading scientists for collaborations.
Recent advances in cognitive radios for electronic warfare create the potential for dynamic environmental conditions, which makes it difficult to rely upon predict-then-adapt approaches in unfamiliar environments. It ...
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ISBN:
(纸本)9781479967704
Recent advances in cognitive radios for electronic warfare create the potential for dynamic environmental conditions, which makes it difficult to rely upon predict-then-adapt approaches in unfamiliar environments. It is thus imperative that radios have increasingly intelligent capabilities in order to be effective in harsh unknown surroundings. In this paper, we explore whether an intelligent jammer can learn and adapt to its surroundings in an electronic warfare-type scenario. We address this problem from a reinforcementlearning perspective where the jammer has delayed information regarding the packets exchanged between a victim transmitter and the receiver. This is different from the traditional assumption that feedback is available instantaneously in reinforcement learning-based algorithms. A new framework, to enable delayed learning in scenarios where rewards are associated with state transitions rather than the states themselves is developed. The benefits of such a framework are shown by studying the optimal jamming strategies against an 802.11-type wireless network that uses the RTS-CTS protocol to communicate and deliver information.
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