Gurukulam: Reasoning based Learning System using Extended Description Logics

Knowledge representation and reasoning aims at designing computer systems that reason about a machine­interpretable representation of the world, similar to human reasoning. Reasoning is a mechanism which helps in retracting the previously inferred facts or changing the confidence factors when conditions change while more complete information is received. This paper presents the design and implementation of Gurukulam – non­monotonic reasoning based learning system which involves extended description logics for knowledge representation. The knowledgebase is constructed by adapting the fundamental classification of world knowledge concepts as per Nyaya Sastra, the famous Indian Philosophy. The system simulates 5 student entities which inputs queries from the user interface and identifies the knowledge units which are grouped into a suitable structure to be fed to the reasoning services engine. Inferences are made from the submitted input and are later updated to their respective knowledge base. Any knowledge base conflicts arising at this juncture is raised as doubts to the teaching entity for further clarification. Upon user response, the system alters false beliefs which created conflicts during previous inferences, thus demonstrating learning by non­monotonic reasoning.