Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Knowledge Acquisition without Analysis
Proceedings of the 7th European Workshop on Knowledge Acquisition for Knowledge-Based Systems
A Maintenance Approach to Case-Based Reasoning
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
Collecting Experience on the Systematic Development of CBR Applications Using the INRECA Methodology
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Techniques and Knowledge Used for Adaptation During Case-Based Problem Solving
IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
Acquiring Adaptation Knowledge for CBR with MIKAS
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Building a case-based diet recommendation system without a knowledge engineer
Artificial Intelligence in Medicine
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This paper introduces a new approach to building complex adaptation functions for case-based reasoning systems. We present an incremental method which allows a domain expert to refine the existing adaptation function during use of the system. We lend ideas from Ripple-Down Rules, a proven method for the very effective and efficient acquisition of classification knowledge during the use of a knowledge-based system. In our approach the expert is only required to provide explanations of why, for a given problem, a certain adaptation step should be taken. Incrementally a complex adaptation function as a systematic composition of many simple adaptation functions is developed. This approach is effective with respect to both, the development of highly tailored and complex adaptation functions for CBR as well as the provision of an intuitive and feasible approach for the expert. The approach has been implemented in our CBR system MIKAS, for the design of menus according to dietary requirements. While our approach showed very good results in MIKAS, it represents also a promising technique for many other CBR applications.