Studies on learning by detecting impasse and by resulting it for building large scale knowledge base for autonomous plant

The acquisition of knowledge from human experts in an exhaustive way is extremely difficult, and even if it were possible, the maintenance of such a large knowledge base for realtime operation is not an easy task. The autonomous system having just incomplete knowledge would face with so many problems that contradicts with the system's current beliefs and/or are novel or unknown to the system. Experienced humans can manage to do with such novelty due to their generalizing ability and analogical inference based on the repertoire of precedents, even if they with new problems. Moreover, through experiencing such breakdowns and impasse, they can acquire some novel knowledge by their proactive attempts to interpret a provided problem as well as by updating their beliefs and contents and organization of their prior knowledge. We call such a style of learning as impasse-driven learning, meaning that learning dose occur being motivated by facing with contradiction and impasse. The related studies concerning with such a style of leaning have been studied within a field of machine learning of artificial intelligence so far as well as within a cognitive science field. In this paper, we at first summarize an outline of machine learning methodologies, and then, we detail about the impasse-driven learning. We discuss that from two different perspective of learning, one is from deductive and analogical learning and the other one is from inductive conceptual learning (i.e., concept formation or generalization-based memory). The former mainly discuss about how the learning system updates its prior beliefs and knowledge so that it can explain away the current contradiction using some meta-cognition heuristics. The latter attempts to assimilate a contradicting problem into its prior memory structure by dynamically reorganizing a collection of the precedents. We present those methodologies, and finally we introduce a case study of concept formation for plant anomalies and its usage for an intelligent decision support system. (J.P.N.)