Qualitative Knowledge Driven Approach to Inductive Logic Programming

Automating the learning of context specific robust rules from an evolving scene has been a research challenge in the area of cognitive vision systems. A research has been conducted to develop a system that learns context specific rules from an evolving scene by abstracting a model of human visual learning. Our system treats a set of symbolic data generated from an evolving real world scene and background knowledge as input to the system and inductive logic programming techniques are used to learn rules of the scene. The observed visual scene is represented in terms of qualitative spatial and temporal relations and these learnt relations are considered as input examples for inductive learning. A prototype has been developed for learning from various scenes of setting dinner tables. Currently the system is being tested to incorporate learning from different real world scenes thus improving the generalization power as well as combine more spatial and temporal representation and reasoning mechanisms to further enhance human like learning. This work can be adopted in automating a robot learning of object manipulation based on a visual scene