Knowledge-Base Evolution for Product and Production Planning

Issue title: Machine Learning

Article type: Research Article

Authors: Hinkelmann, Knut^; | Meyer, Manfred | Schmalhofer, Franz

Affiliations: DFKI (German Research Center for Artificial Intelligence), Postfach 2080, 67608 Kaiserslautern, Germany, Email: {hinkelma,meyer,schmalho}@dfki.uni-kl.de

Note: [] This research was supported by grant 413-5839-ITW9304/3 from the BMFT.

Abstract: Knowledge-base evolution techniques are shown to be of critical importance for the successful application of knowledge-based systems in complex domains. By conceptualizing knowledge-base evolution as theory revision, we can take advantage of the basic findings from different research communities. Results from Inductive Logic Programming (ILP) and Explanation-Based Learning (EBL) provide a set of techniques that can be used as a foundation for obtaining new knowledge (knowledge-base exploration). Techniques from deductive database research might be used for testing the correctness of a knowledge base (knowledge base verification). By an interactive application of these exploration and verification techniques, domain experts and other users may similarly improve the effectiveness of the knowledge base (knowledge validation). The application of such selected techniques is then discussed with respect to the specific problem of improving production parameters.

DOI: 10.3233/AIC-1994-7203

Journal: AI Communications, vol. 7, no. 2, pp. 98-113, 1994