The Evolution of Blackboard Control Architectures

This paper examines the issues that arise in the control of blackboard systems for applications with large and complicated search spaces by analyzing the evolution of blackboard control architectures We feel that the issues addressed here apply more generally to AI application domains involving complex multi dimensional search in which control knowledge is as important to successful problem solving as domain knowledge Evolution is viewed largely from the context of the Hearsay II HSII speech understanding system The appeal of the blackboard model is that it provides great exibility in structuring problem solving On the other hand many of the features that are responsible for this exibility make e ective control di cult because they complicate the process of estimating the expected value of potential actions Among the key themes in the evolution of blackboard control is the development of mechanisms that support more sophisticated goal directed reasoning In the basic control mechanism of HSII control decisions could consider only the local and immediate e ects of possible actions Thus the value of potential actions in meeting the system goals could be evaluated in only a limited manner The development of appropriate abstractions of the intermediate state of problem solving can be used to evaluate the non local e ect of actions relative to the overall problem solving goals In addition blackboard systems went from the implicit representation of goals in HSII to explicit representation of the goals that must be satis ed in order to meet the overall goals of the system This allowed the implementation of various styles of goal directed reasoning e g subgoaling and planning that were not supported in the basic HSII control mechanism Other architectural mechanisms were concerned with e ciency issues We will examine a number of di erent blackboard control architectures that have evolved from the basic model of HSII HASP SIAP s event based control CRYSALIS hierarchical control the DVMT s goal directed architecture the control blackboard architecture BB model based incremental planning for the DVMT the channelized parameterized control loop version of the DVMT ATOME s hybrid multistage control CASSANDRA s distributed control and the RESUN interpretation framework This is a revised and expanded version of a paper that will appear in Expert Systems with Applications special issue on The Blackboard Paradigm and Its Applications The major di erences between the two versions include Section was signi cantly rewritten Sections and were added and there are several additional gures in Section