Analogical and Case-Based Reasoning for Predicting Satellite Task Schedulability

Tinker, Pete; Fox, Jason; Green, Collin; Rome, David; Casey, Karen; Furmanski, Chris

doi:10.1007/11536406_43

Cited by 12 publications

(3 citation statements)

References 3 publications

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“…Through ground training and in-orbit application, satellites can efficiently complete autonomous task planning. Tinker [7] developed a case-learning-based method for satellite observation task planning, using historical datasets for unsupervised learning and predicting task schedulability. Liu [8] used the integrated BP neural network method to design a componentized solution architecture composed of collaborative task assignment component, task scheduling component, feature extraction component and task schedulability prediction component to predict the schedulability of observation tasks.…”

Section: Introductionmentioning

confidence: 99%

MARL-Based Multi-Satellite Intelligent Task Planning Method

Zhang,

Li,

et al. 2023

IEEE Access

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In this article, we propose a solution to multi-satellite intelligent task planning using the multiagent reinforcement learning (MARL) method. Fristly, we have developed a multi-satellite task planning model based on the Markov game framework. Furthermore, we have computationally designed a satellite state transition function to address the task planning problem and successfully solved it using the multi-agent proximal policy optimization (MAPPO) algorithm. Our experimental results demonstrate that the MARL method exhibits remarkable convergence speed and performance, delivering significant rewards in multiscale task planning scenarios. Consequently, it proves to be a highly suitable approach for multi-satellite intelligent task planning.INDEX TERMS MARL; multi-satellite intelligent task planning; Markov game; MAPPO

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Section: Introductionmentioning

confidence: 99%

MARL-Based Multi-Satellite Intelligent Task Planning Method

Zhang,

Li,

et al. 2023

IEEE Access

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“…At present, the research on the schedulability prediction of imaging satellite tasks is still in its infancy. Thinker et al [3] first proposed a method for predicting the schedulability of satellite tasks based on case learning, using historical satellite planning data sets for unsupervised learning, and comparing with known examples to complete satellite task prediction. Li et al [4] used the classic machine learning method decision tree and support vector machine to achieve task schedulability prediction, which proved the effectiveness of the method.…”

Section: Introductionmentioning

confidence: 99%

Research on the Prediction Problem of Satellite Mission Schedulability Based on Bi-LSTM Model

Zhang

Wang

et al. 2022

Aerospace

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The realization of microsatellite intelligent mission planning is the current research focus in the field of satellite planning, and mission schedulability prediction is the basis of this research. Aiming at the influence of the sequence tasks before and after the task sequence to be predicted, we propose an online schedulability prediction model of satellite tasks based on bidirectional long short-term memory (Bi-LSTM) on the basis of describing and establishing the satellite task planning and solving model. The model is trained using satellite offline mission planning data as learning samples. In the experiment, the prediction effect of the model is excellent, with a recall rate of 93.17% and a precision rate of 92.59%, which proves that the model can be effectively applied to predict the schedulability of satellite tasks.

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“…However, its methods of working with structures are computationally expensive and poorly take semantics into account (see also Section 1.2 and 1.3). Also, analogy‐based reasoning has not yet reached the maturity of case‐based reasoning usage, although moving in this direction (e.g., Forbus 2001; Bjornestad 2003; Markman et al 2003; Tinker et al 2005; Forbus and Hinrichs 2006; Forbus, Lockwood, and Sharma 2009; Wetzel and Forbus 2009).…”

Section: Introductionmentioning

confidence: 99%

Similarity‐based Retrieval With Structure‐sensitive Sparse Binary Distributed Representations

Rachkovskij¹,

Slipchenko²

2012

Computational Intelligence

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We present an approach to similarity-based retrieval from knowledge bases that takes into account both the structure and semantics of knowledge base fragments. Those fragments, or analogues, are represented as sparse binary vectors that allow a computationally efficient estimation of structural and semantic similarity by the vector dot product. We present the representation scheme and experimental results for the knowledge base that was previously used for testing of leading analogical retrieval models MAC/FAC and ARCS. The experiments show that the proposed single-stage approach provides results compatible with or better than the results of two-stage models MAC/FAC and ARCS in terms of recall and precision. We argue that the proposed representation scheme is useful for large-scale knowledge bases and free-structured database applications.

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Analogical and Case-Based Reasoning for Predicting Satellite Task Schedulability

Cited by 12 publications

References 3 publications

MARL-Based Multi-Satellite Intelligent Task Planning Method

MARL-Based Multi-Satellite Intelligent Task Planning Method

Research on the Prediction Problem of Satellite Mission Schedulability Based on Bi-LSTM Model

Similarity‐based Retrieval With Structure‐sensitive Sparse Binary Distributed Representations

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