Validation of blackboard architecture for dynamic intent inferencing

The authors propose the concept of an operator´s associate to serve as an intelligent aid to the human operator. They note that a necessary prerequisite for a useful and effective `intelligent´ automated aid is the ability to understand, or infer the intentions of, the human operator. This ability in turn relies on a sound model of operator performance goals in the control of the system. A good model of operator performance defines the structure of operator intentions or goals with respect to current system state. In the authors´ system, this intentional structure is implemented as a blackboard architecture called ACTIN (Action Interpreter). Beyond implementation, however, the intent inferencing process must be validated. The authors describe two studies that experimentally evaluated the system´s ability to infer operator intentions involving (1) post hoc analysis by a human domain expert and (2) concurrent verbal protocols by the actual human operator. Finally, the authors offer suggestions toward a general validation methodology