Using an integrated model learning system to construct the model base of a decision support system

✍ Scribed by Robert T. H. Chi

• Publisher: John Wiley and Sons
• Year: 1992
• Tongue: English
• Weight: 807 KB
• Volume: 7
• Category: Article
• ISSN: 0884-8173
• DOI: 10.1002/int.4550070406

✦ Synopsis

The Model Base Management System (MBMS) in a Decision Support System (DSS) has become increasingly important in handling complicated decision problems. Traditionally, models are retrieved from the human experts. This approach has the disadvantages of low productivity and being subjective by different individuals. Although recent research projects include how to apply Artificial Intelligence approaches to automatic model retrieval, none of them provides an efficient and effective way to produce large-scale complicated models. In this article, an Integrated Model Learning System is introduced to learn new models from model instances. This system basically uses the Multiple Instance Explanation Based Generalization (miEBG) approach which is a modification of the Multiple Explanation Based Generalization (mEBG) approach to explain and generalize model instances by the Domain Theory. Different from other machine learning approaches, this approach takes advantages of using the Domain Theory to learn multiple explanation trees from instances, and then combines all trees to form a complete explanation tree which will be generalized into a useful model. In general, this approach provides the Model Base Management System an automatic way to retrieve new and more complete models from related instances in a self-learning fashion.