Achieving Flexibility, Efficiency, and Generality in Blackboard Architectures

Achieving flexibility and efficiency in blackboardbased AI applications are often conflicting goals. Flexibility, the ability to easily change the blackboard representation and retrieval machinery, can be achieved by using a general purpose blackboard database implementation, at the cost of efficient performance for a particular application. Conversely, a customized blackboard database implementation, while efficient, leads to strong interdependencies between the application code (knowledge sources) and the blackboard database implementation. Both flexibility and efficiency can be achieved by maintaining a sufficient level of data abstraction between the application code and the blackboard implementation. The abstraction techniques we present are a crucial aspect of the generic blackboard development system GBB. Applied in concert, these techniques simultaneously provide flexibility, efficiency, and sufficient generality to make GBB an appropriate blackboard development tool for a wide range of applications.