異種冗長なセンサー情報に基づく自律的状態抽象化法

概要

論文の詳細を見る
Autonomous state abstraction, i.e., the process to acquire the general information from the primitive sensor inputs is a key issue in the research area of intelligent autonomous agents, because the sensor spaces for these agents are generally vast and it is almost impossible to treat each sensor state individually. At the same time, fusion of heterogeneous and redundant sensor information is indispensable for those agents to deal with the uncertainty in the real world. This paper proposes a new abstraction method based on Bayesian classifier, which enables the agents to integrate heterogeneous and redundant sensor information in a flexible manner. Simulation results have shown that this method increases the robustness of the agents' behavior against the disturbance such as sensor noise and fault.
社団法人人工知能学会の論文
1999-07-01