Incorporating awareness in expert systems - learning from expert's selective attention and perception

摘要

We collect environmental information through our sensory organs, perceive them suitably to execute the task in hand. Examples of such tasks are abundant, like driving, operating machines, playing games, hunting, or mushroom picking on mountains. While executing such tasks, there is a marked difference in efficiency and accuracy between an expert and a novice. An expert is attentive to only what is important, still makes fewer errors. She is tacitly aware when or where to focus attention. The operation is efficient because an expert has less information to process, and do not attend to what is irrelevant. An expert correctly perceives from sensory information when to be alert, and therefore she is efficient. At times, the expert is able to explicitly describe her knowledge in a set of rules, but not always. It is not uncommon that the expert herself is unaware how the right decision is taken, and can not express his expertise in explicit rules. This is tacit knowledge acquired through long experience, and lack of which makes a novice ponder to accomplish the task correctly. What is perceived by an expert is different from that of a novice, though the available information through vision, audio and other senses are the same. We can design efficient machines, if the expert's selective attention and perception could be learned and incorporated in machine learning. The motivation of this work is to propose a framework to design machines which will be able to learn the tacit knowledge of an expert. When something important is perceived (like an alarming situation warranting immediate action), it is reflected in bio-signals like increased pulse rate or decrease in GSR. These bio-signals are used as cues to collect labeled data for supervised learning of the tacit knowledge. The system will be efficient by avoiding irrelevant information.