GREAT: a model of progressive reasoning for real-time systems

The problem of producing timely responses when faced with deadlines is an important issue for real-time systems. To deal with this problem different kinds of scheduling algorithms have been developed within the real-time community. Unfortunately, these approaches fail when applied to knowledge based systems, mainly because AI techniques rely on time-consuming algorithms with unpredictable, or highly variable performances. To face this problem researchers in AI have introduced deliberative techniques that enable to adapt the way a task is working in function of the available time. For this purpose approximation algorithms of two kinds have been developed: iterative refinement and multiple methods. The model we propose in this paper, belongs to iterative refinement methods. The main advantage of our approach is the capability of designing iterative refinement methods using a general rule language.<>