We propose an approach for meeting real-time constraints in AI systems that views (1) time as a resource that should be considered when making control decisions, (2) plans as ways of expressing control decisions, and (3) approximate processing as a way of satisfying time constraints that cannot be achieved through normal processing. In this approach, a real-time problem solver estimates the time required to generate solutions and their quality. This estimate permits the system to anticipate whether the current objectives will be met in time. The system can then take corrective actions and form lower-quality solutions within the time constraints. These actions can involve modifying existing plans or forming radically different plans that utilize only rough data characteristics and approximate knowledge to achieve a desired speedup. A decision about how to change processing should be situation dependent, based on the current state of processing and the domain-dependent solution criteria. We present preliminary experiments that show how approximate processing helps a vehicle-monitoring problem solver meet deadlines and outline a framework for flexibly meeting real-time constraints.

In COLING-88, pp. 71–76

In Non- Monotonic Reasoning: 2nd International Workshop Proceedings, pp. 74–99.

Proceedings of the National Conference on Artificial Intelligence, 245-250

In Proceedings of the Fifth International Conference on Machine Learning, pages 54-64, Ann Arbor, Michigan.

Proc. AAAI-88, Vol. 2, pp. 607–611

In Mora, T. (Ed.), Applied Algebra, Algebraic Algorithms and Error-Correcting Codes, pp. 99–110. Springer-Verlag.

In Marcus, S., (Ed.), Automating Knowledge Acquisition for Knowledge Based Systems, chapter 8, pages 120-146. Boston: Kluwer Academic Publishers

In R. Engelmore and T. Morgan (Eds.), Blackboard systems. Wokingham, England: Addison-Wesley

In IEEE International Conference on Neural Networks, pp. 325–333.