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.<
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