Abstract
If machines are capable of understanding natural language, communications between humans and machines will be very efficient. However, natural language is often very vague or inaccurate, and it has been very difficult to construct such capable machines. When an instruction is given from a human to a system, the conventional way has been to utilize a priori knowledge to understand it. Where the instruction is expressed linguistically, it is very hard for the system to acquire a priori knowledge. This paper presents a system which is able to understand linguistic instructions given by a human operator and to extract intention from the linguistic instructions using a Fuzzy Classifier System(FCS). By extracting the intension, the system can generalize the instructions for different conditions. By applying the proposed system to an obstacle avoidance problem, simulations are done to show that the system can extract intentions, and the extracted rules have generality.
