This paper discusses the use of writing as a teaching approach for an upper level computer science course. In describing my experiences, I hope to encourage those schools/teachers who are considering incorporating writing into such a course. Many different kinds of writing are compared and contrasted. Attention is paid to practicalities of the writing process and of the demands made of faculty in these writing-based courses.

This paper investigates the effects of parallelism on blackboard system scheduling heuristics. A parallel blackboard system is described that allows multiple knowledge source instantiations (KSIs) to execute in parallel using a shared-memory blackboard approach. New classes of control knowledge are defined that order the agenda by using information about the relationships between the goals of the KSIs. This control knowledge is implemented and tested in the DVMT application on a Sequent multiprocessor using BB1-style control heuristics. The usefulness of the heuristics is examined by comparing the effectiveness of problem-solving with and without the heuristics (as a group and individually). Problem solving with the new control knowledge results in improved system performance.

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. Previous work has shown how to integrate approximate processing knowledge sources within the blackboard architecture. However, in order to solve real-time problems with hard deadlines using a blackboard system, we need to have: (1) a predictable blackboard execution loop, (2) a representation of the set of current and future tasks and their estimated durations, and (3) a model of how to modify those tasks when their deadlines are projected to be missed, and how the modifications will affect the task durations and results. This paper describes four components for achieving this goal in an approximate processing blackboard system. A parameterized 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 (DVMT).

Design-to-time is an approach to problem solving in resource-constrained domains where multiple solution methods are available for tasks. Those solution methods make tradeoffs in solution quality within the available time. The design-to-time approach is defined in detail, and contrasted to the anytime algorithm approach. A heuristic algorithm for design-to-time real-time scheduling is presented. The authors' blackboard architecture that implements the design-to-time approach is discussed, and an example problem and solution from the Distributed Vehicle Monitoring Testbed (DVMT) is described in detail. Experimental results, generated using simulation, show the effects of various parameters on scheduler performance. Future research goals and plans are discussed.>