A Web-Based System for Learning Qualitative Constraint Networks with Preferences

Pablo Echavarria, Malek Mouhoub

Abstract: We present a new web-based platform crafted to represent and learn Qualitative Constraint Networks (QCNs) with preferences, focusing specifically on temporal data. The system uses a learning algorithm that extracts qualitative temporal constraints through user-guided membership queries. The learning process is enhanced with transitive closure (Path Consistency) to infer new relations and reduce the number of queries. Path consistency relies on the Allen’s interval algebra composition table. During the learning phase, the user can add their preferences. The latter will be represented by a conditional preference network (CP-net).