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. The system can then take corrective actions and form lower-quality solutions within the time constraints. 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.
Lesser, Victor R., Corkill, Daniel G.
Cooperative distributed problem solving networks are distributed networks of semi-autonomous processing nodes that work together to solve a single problem. The Distributed Vehicle Monitoring Testbed is a flexible and fully-instrumented research tool for empirically evaluating alternative designs for these networks. The testbed simulates a class of a distributed knowledge-based problem solving systems operating on an abstracted version of a vehicle monitoring task. it implements a novel generic architecture for distributed problems solving networks that exploits the use of sophisticated local node control and meta-level control to improve global coherence in network problem solving; (2.)
Lesser, Victor R.
Our program in AI is part of the larger departmental focal area of cybernetics which integrates both AI and brain theory (BT). Our research also draws upon a new and expanding interdepartmental program in cognitive science that brings together researchers in cybernetics, linguistics, philosophy, and psychology. This interdisciplinary approach to AI has already led to a number of fruitful collaborations in the areas of cooperative computation, learning, natural language parsing, and vision.