CASE SIMULATION TO ASSESS LEARNING-SYSTEMS

Learning forms an essential part of artificial intelligence applicatio ns. Databases of example ''cases'' are essential to the development an d assessment of learning systems. insufficient examples make it diffic ult or impossible to compare variations of the learning method. Suffic ient examples are rarely available to assess a learning system thoroug hly. Simulation represents a means of producing data based upon a defi ned system. In this paper, a simulation for assessing the characterist ics of learning systems is described. The simulation aims to generate data as an actual knowledge-based system (KBS) observes and stores dat a. A notional model is developed to mirror what is known of part of th e actual target domain of a particular KBS. Significant results materi alizing from simulated data include a quantitative comparison of learn ing and testing on the same and disjoint data sets. Simulated data is used to show that the use of the same data for learning and testing fr equently reduces diagnostic accuracy when learnt knowledge is applied to new data. Copyright (C) 1996 Elsevier Science Ltd