因次模函數的各式各樣應用(如物件搜尋、三維地圖重建)，其已經引起人工智慧社群的注意， 搜尋受害者是搜救行動的關鍵技術，但是找到最佳搜尋路徑是NP困難問題。 因空間搜尋的目標函數具次模性，貪婪演算法可產生近似最佳解。 然而，因N個集合的次模函數輸出數量是$2^N$，導致學習次模函數是一大挑戰。 最先進的方法是以壓縮感測技術，在頻域學習次模函數，再於空間域還原。 然而，傅立葉基底的數量和集合感測重疊區域的數量相關。 為了能克服此問題，本研究提出次模深度壓縮感測(SDCS)方法來學習次模函數， 此演算法包含學習自編碼器與傅立葉係數，學習後的網路可以用於預測次模函數的$2^N$數值， 實驗證明此演算法比基準方案更有效率。;The AI community has been paying attention to submodular functions due to their various applications (e.g., target search and 3D mapping). Searching for the victim is the key to search and rescue operations but finding an optimal search path is an NP-hard problem. Since the objective function of the spatial search is submodular, greedy algorithms can generate near-optimal solutions. However, learning submodular functions is a challenge since the number of a function′s outcomes of N sets is $2^N$. The state-of-the-art approach is based on compressed sensing techniques, which are to learn submodular functions in the Fourier domain and then recover the submodular functions in the spatial domain. However, the number of Fourier bases is relevant to the number of sets′ sensing overlapping. To overcome this issue, this research proposed a submodular deep compressed sensing (SDCS) approach to learning submodular functions. The algorithm consists of learning autoencoder networks and Fourier coefficients. The learned networks can be applied to predict $2^N$ values of submodular functions. Experiments conducted with this approach demonstrate that the algorithm is more efficient than the benchmark approach.
