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. The autho rs feel that the issues addressed here apply more generally to AI appl ication domains involving complex multidimensional search, in which co ntrol knowledge is as important to successful problem solving as is do main knowledge. Evolution is viewed largely from the context of the He arsay-II (HSII) speech understanding system. The appeal of the blackbo ard model is that it provides great flexibility in structuring problem solving. On the other hand, many of the features that are responsible for this flexibility make effective control difficult because they co mplicate the process of estimating the expected value of potential act ions. Among the key themes in the evolution of blackboard control is t he development of mechanisms that support more sophisticated goal-dire cted reasoning. In the basic control mechanism of HSII, control decisi ons could consider only the local and immediate effects of possible ac tions. Thus, the value of potential actions in meeting the system goal s could be evaluated in only a limited manner. The development of appr opriate abstractions of the intermediate state of problem solving can be used to evaluate the non-local effect of actions relative to the ov erall problem-solving goals. In addition, blackboard systems went from the implicit representation of goals in HSII to explicit representati on of the goals that must be satisfied in order to meet the overall go als of the system. This allowed the implementation of various styles o f goal-directed reasoning (e.g., subgoaling and planning) that were no t supported in the basic HSII control mechanism. Other architectural m echanisms were concerned with efficiency issues. This article examines a number of different blackboard control architectures that have evol ved from the basic model of HSII: HASP/SIAP's event-based control, CRY SALIS' hierarchical control, the DVMT's goal-directed architecture, th e control blackboard architecture (BB1), model-based incremental plann ing for the DVMT, and the RESUN interpretation framework. A longer ver sion of this paper is available as a technical report. It also include s analyses of the channelized, parameterized control loop version of t he DVMT (Decker, Humphrey, & Lesser, 1989), ATOME's hybrid multistage control (Laasri, & Maitre. 1989) and CASSANDRA's distributed control ( Craig, 1989).