Knowledge representation in a blackboard system for sensor data interpretation

A prototype system has been developed for automated defect classification and characterisation of automotive or other components employing two separate inspection sensors, vision and electromagnetic. The development work for the electromagnetic sensor sub-system was fraught with difficulties. In particular, there was the basic problem of encoding human expertise. The reasoning carried out by the human inspectors is more complex than the experts themselves may suppose and was not easily encapsulated.A blackboard architecture was used to integrate the different areas of expertise required for each sensor to interpret the results of the inspections. One issue here discussed is the effective use of the blackboard architecture for intelligent data fusion at all levels to improve interpretation.