Representing temporal spatial, and causal knowledge for monitoring and control in an Intelligent Simulation Training System

The objective of the Intelligent Simulation Training System (ISTS) is to train students in monitoring and controlling physical objects in time and space. The ISTS consists of a graphic computer simulation, an expert system, and a user interface. The expert system must possess a high-level model for reasoning about simulation events. Quantitative simulation data and elicited domain expertise are used to generate an abstract, qualitative model for reasoning purposes. The qualitative model is generated by a subsystem known as ELICIT (Expertise Learner and Intelligent Causal Inference Tool). This paper describes the representations used for generating such a model. ELICIT is implemented in Common LISP and Joshua (a knowledge representation language developed by Symbolics Inc.) on a Symbolics 3630 LISP machine.<
>