Resumen de An architecture for knowledge management in intelligent tutoring system
Simone Riccucci, Antonella Carbonaro, Giorgio Casadei
This paper discusses a general framework for knowledge acquisition and management in an intelligent tutoring system. This system is based on "Learning by performance errors" theory stating that in a given domain knowledge there is a set of constraints that must be satisfied in order to provide the correct solution to the problem. This paper addresses the issues of representing complex and generic information that applies to multiple domains. The proposed solution provides guidelines for both the system knowledge acquisition and management based on the natural language processing platform GATE (General Architecture for Text Engineering), inductive logic programming and constraint based paradigm.
