Rule-Based Systems Combining AI Techniques into a Legal Agent-based Intelligent Tutoring System

Computer based learning gets more and more important in higher education. Particularly, in Legal domain, students have little chance to deal with realistic situations. One way to alleviate this problem is to provide Law students with real cases, rules and different viewpoints of which a given body of knowledge is often recognized as important to their successful learning. We propose a novel approach to ITS applied to Legal domain in order to address each of the above concerns. Then, we define an agent-based architecture to support multiple views of domain knowledge, aiming to improve the quality of student-ITS interactions and the learning success of the students. Each tutoring agent from the system contains a knowledge-based system that combines case-based reasoning and rule-based system. In addition, each agent adopts the reinforcement learning Algorithm aiming to identifying the best pedagogical strategy by considering the student profile. This paper focuses on both architecture and the mentioned Artificial Intelligence techniques into a Legal System. Finally, an example scenario is shown to demonstrate the feasibility of our approach.