This paper presents a mixed-initiative assistant that supports a subject matter expert to extend the ontology of a learning agent, in order to express the subtle distinctions he makes in an application domain. This assistant performs a comparative analysis of the correct examples and the exceptions accumulated by the problem solving rules and guides the expert in improving its knowledge base. This assistant is integrated into the Disciple-RKF system, and has been evaluated in two knowledge acquisition experiments performed at the US Army War College. The object ontology plays a critical role in the learning and teaching process of the Disciple agent, being used as the generalization hierarchy for learning. However, this ontology is generally incomplete, causing situations in which there is no represented knowledge to distinguish between correct and incorrect problem solving examples. In such cases, trying to specialize a rule in order to uncover the incorrect examples will result in uncovering some of the correct examples as well. Therefore, in the context of the current ontology, the incorrect examples that could not be distinguished from the positive examples are kept as negative exceptions of the rule. In this paper we present a mixed-initiative assistant for learning new object features that extend the object ontology, allowing the elimination of the rule's exceptions and improving the agent's problem solving efficiency. To present the assistant we will consider an example from a didactic domain, the PhD advising domain, which was used in a knowledge-base development experiment performed as part of the "IT 803 Instructable Agents" course at George Mason University in Spring 2002. During this course, the students developed a Disciple agent for assessing whether a faculty member would be an appropriate advisor for a specific student, considering the professor's area of expertise versus the student's research interests. The agent's goal was to identify the strengths and weaknesses of potential advisors with respect to several characteristics, thus being able to assist a person in choosing a PhD advisor. The students, acting as subject matter experts, taught personal Disciple agents to analyze potential PhD thesis advisors with respect to characteristics such as professional reputation, responsiveness to students, expected learning experience, financial support offered to students, general personality and work compatibility, and the expected quality of student's work. The problem solving paradigm of Disciple is task reduction, which was used by the experts to express their reasoning, by decomposing an initial task into simpler tasks, guided by questions and answers, until a solution was found. In this teaching process, the experts formulated first in natural language specific problem solving examples, based on which Disciple learned general task reduction rules, through a mixed-initiative interaction.
['Cristina Boicu', 'Gheorghe Tecuci']
Mixed-Initiative Ontology Learning