A blackboard system for real-time control of approximate processing

Approximate processing is an approach to real-time AI problem solving in domains in which compromise is possible between the resources required to generate a solution and the quality of that solution. It is a satisficing approach in which the goal is to produce acceptable solutions within the available time and computational resource constraints. This paper describes four components for achieving this goal in an approximate processing blackboard system. A parametrized low-level control loop allows predictable knowledge source execution, multiple execution channels allow dynamic control over the computation involved in each task, a meta-controller allows a representation of the set of current and future tasks and their estimated durations and results, and a real-time blackboard scheduler monitors and modifies tasks during execution so that deadlines are met. An example is given that illustrates how these components work together to construct a satisficing solution to a time-constrained problem in the Distributed Vehicle Monitoring Testbed