The functionally accurate, cooperative (FA/C) paradigm provides a model for task decomposition and agent interaction in a distributed problemsolving system. In this model, agents need not have all the necessary information locally to solve their subproblems, and agents interact through the asynchronous, co-routine exchange of partial results. This model leads to the possibility that agents may behave in an uncoordinated manner. The author traces the development of a series of increasingly sophisticated cooperative control mechanisms for coordinating agents. They include integrating data- and goal-directed control, using static metalevel information specified by an organizational structure, and using dynamic metalevel information developed in partial global planning. The framework of distributed search motivates these developments. Major themes are the importance of sophisticated local control, the interplay between local control and cooperative control, and the use of satisficing cooperative control. Ongoing and new directions for research in FA/C systems are presented. >

A retrospective view of FA/C distributed problem solving

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 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 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 effects 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 effect 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 satisfied 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 efficiency issues. We will examine a number of different 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 (BB1), 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.

The Evolution of Blackboard Control Architectures

Model construction operators

This paper describes some recent research on architectures for situated (embedded) systems that need to deliberate and reason in real time One of the most difficult problems in the design of such architectures is how to manage the reasoning performed by such a system while still meeting the real-time constraints of the problem domain We present an architecture, based on the Procedural Reasoning System (PRS), that provides mechanisms for the management and control of deliberation and reasoning in real-time domains In particular, we show how deliberation and reasoning strategies can be represented in the form of metalevel plans, and describe an interpreter that selects and executes these in a way that retains bounded reaction time In addition , this approach allows us to represent different types of situated systems by varying the meta-level deliberation strategies Finally, we provide some statistical measures of performance for one such type of situated system applied to a complex real-time application

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Managing Deliberation and Reasoning in Real-Time AI Systems

A specific blackboard expert system to simulate and automate the design of high recirculation airlift reactors

Goal relationships resulting from the initial data and subsequent processing can he used to dynamically construct a partial topology of the solution space based on what appear to be feasible solutions. This structure can be used to make control decisions that significantly reduce the amount of search required to solve a problem in a complex domain. We examine the utility of this approach in the context of a multi-level, cooperative knowledge source model of problem solving. We present a taxonomy of goal relationships for constructing partial topologies of the solution space and show that mechanisms using this information can be built as natural extensions of an integrated data-directed and goal directed archi tecture. Examples and performance results demonstrating how these additions improve the system's ability to evaluate potential activities are provided.

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Joseph A. Hernandez

Papers

Focus of control through goal relationships