Achieving Flexibility, Efficiency, and Generality in Blackboard
Daniel D. Corkill, Kevin Q. Gallagher, Philip M. Johnson · 1987
ACHIEVING FLEXIBILITY AND EFFICIENCY IN BLACKBOARD-BASED 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 EFFI- CIENT PERFORMANCE FOR A PARTICULAR APPLICATION. CONVERSELY, A CUSTOMIZED BLACKBOARD DATABASE IMPLEMENTATION, WHILE EFFICIENT, LEADS TO STRONG INTER- DEPENDENCIES 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.