Agent Shell for the Development of Tutoring Systems for Expert Problem Solving Knowledge

Abstract

This paper introduces the concept of learning and tutoring agent shell as a general and powerful tool for rapid development of a new type of intelligent assistants that can learn complex problem solving expertise directly from human experts, can support human experts in problem solving and decision making, and can teach their problem solving expertise to non-experts. This shell synergistically integrates general problem solving, learning and tutoring engines and has been used to build a complex cognitive assistant for intelligence analysts. This assistant has been successfully used and evaluated in courses at US Army War College and George Mason University. The goal of this paper is to provide an intuitive overview of the tutoring-related capabilities of this shell which rely heavily on its problem solving and learning capabilities. They include the capability to rapidly acquire the basic abstract problem solving strategies of the application domain, directly from a subject matter expert. They allow an instructional designer to rapidly design lessons for teaching these abstract problem solving strategies, without the need of defining examples because they are automatically generated by the system from the domain knowledge base. They also allow rapid learning of test questions to assess students’ problem solving knowledge. The proposed type of cognitive assistant, capable of learning, problem solving and tutoring, as well as the learning and tutoring agent shell used to build it, represent a very promising and expected evolution for the knowledge-based agents for “ill-defined” domains.KeywordsITS-building toolITS authoringagent-based tutoring systemsknowledge acquisitionmachine learning in ITSintelligent agentsill-defined domainsintelligence analysisteaching problem solving expertiselesson design and generationtest learning and generation