Abstract
Case-based reasoning (CBR) and expert systems have a long tradition in artificial intelligence: CBR since the late 1970s and expert systems since the late 1960s. While expert systems are based on expertise and expert reasoning capabilities for a specific area of responsibility, CBR is an approach for problem solving and learning of humans and computers. Starting from different research activities, CBR and expert systems have become overlapping research fields. In this talk the relationships between CBR and expert systems are analyzed from different perspectives like problem solving, learning, competence development, and knowledge types. As human case-based reasoners are quite successful in integrating problem-solving and learning, combining different problem solving strategies, utilizing different kinds of knowledge, and becoming experts for specific areas of responsibility, computer based expert systems do not have the reputation to be successful at these tasks. Based on this, the potential of CBR succeeding as future expert systems is discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Althoff, KD. (2012). Case-Based Reasoning and Expert Systems. In: Agudo, B.D., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2012. Lecture Notes in Computer Science(), vol 7466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32986-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-32986-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32985-2
Online ISBN: 978-3-642-32986-9
eBook Packages: Computer ScienceComputer Science (R0)