Title: Dimensionality Reduction for State-action Pair Prediction based on Tendency of State and Action

Issue Number: Vol. 7, No. 1
Year of Publication: Jan - 2017
Page Numbers: 18-28
Authors: Masashi Sugimoto, Naoya Iwamoto, Robert W. Johnston, Keizo Kanazawa, Yukinori Misaki, Hiroyuki Inoue,Manabu Kato, Hitoshi Sori, Shiro Urushihara, Kazunori Hosotani,Hitoshi Yoshimura, Kentarou Kurashige
Journal Name: International Journal of New Computer Architectures and their Applications (IJNCAA)
- Hong Kong
DOI:  http://dx.doi.org/10.17781/P002307


This study investigates the effectiveness of reduction of training sets and kernel space for action decision using future prediction. For future prediction in a real environment, it is necessary to know the properties of the state and disturbance resulting from the outside environment, such as a ground surface or water surface. However, obtaining the properties of the disturbance depends on the specifications of the target processor,especially its sensor resolution or processing ability. Therefore, sampling-rate settings are limited by hardware specifications. In contrast, in the case of future prediction using machine learning, prediction is based on the tendency obtained from past training or learning. In such a situation, the learning time is proportional to training data. At worst, the prediction algorithm will be difficult to implement in real time because of time complexity. Here, we consider the possibility of carefully analyzing the algorithm and applying dimensionality reduction technique st accelerate the algorithm. In particular, to reduce the training sets and kernel space based on the recent tendency of disturbance or state, we focus on the use of the fast Fourier transform (FFT) and pattern matching. From this standpoint, we propose a method for dynamically reducing the dimensionality based on the tendency of disturbance. As a future application, an algorithm for operating unmanned agricultural support machines will be planned to implement the proposed method in a real environment.