Goal-Oriented Conceptualization of Procedural Knowledge Martin Moˇ zina, Matej Guid, Aleksander Sadikov, Vida Groznik, Ivan Bratko Faculty of Computer and Information Science, University of Ljubljana, Slovenia {martin.mozina,matej.guid,aleksander.sadikov,vida.groznik,ivan.bratko} @fri.uni-lj.si Abstract. Conceptualizing procedural knowledge is one of the most challenging tasks of building systems for intelligent tutoring. We present an algorithm that enables teachers to accomplish this task semi auto- matically. We used the algorithm on a difficult king, bishop, and knight versus the lone king (KBNK) chess endgame, and obtained concepts that could serve as textbook instructions. A pilot experiment with students and a separate evaluation of the instructions by experienced chess train- ers were deemed very positive. Keywords: domain conceptualization, procedural knowledge, goal-oriented rule learning, argument-based machine learning, chess 1 Introduction Domain conceptualization lies at the very core of building an intelligent tutoring system (ITS) [7],[10]. This involves the structuring of the domain and creating a vocabulary or ontology of key concepts. Domain conceptualization consists of declarative knowledge and procedural knowledge, which generally speaking is the knowledge exercised in the performance at some task. Procedural knowledge is usually implicit and not easily articulated by the individual. Due to its tacit nature this kind of knowledge is often very hard to conceptualize. In this paper, we will consider symbolic problem solving domains where prob- lem solving is based on reasoning with symbolic descriptions (like in physics, mathematics, or games like chess). A particular domain is defined with a ba- sic domain theory (e.g., the rules of chess) and a solution to be achieved (e.g., checkmate the opponent in chess). The task is to find a sequence of steps that bring us from the starting state of the problem to the goal state. The basic domain theory (or basic declarative knowledge of the domain) is usually simple and easy to remember. It is, in principle, sufficient for solving problems (e.g., knowing rules of chess could in theory enable optimal play). However, finding a solution using only declarative knowledge would require far too extensive searching for a human. A human student is incapable of searching very deeply, therefore we need to teach him also the procedural knowledge – how to solve problems. The “complete” procedural knowledge would be a function