Incremental acquisition of search knowledge
International Journal of Human-Computer Studies
Common KADS Library for Expertise Modelling
Common KADS Library for Expertise Modelling
Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology
Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology
Acquiring Adaptation Knowledge for CBR with MIKAS
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
A Maintenance Approach to Case-Based Reasoning
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
Case-Based Design for Tablet Formulation
EWCBR '98 Proceedings of the 4th 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
A New Apporach for the Incremental Development of Adaptation Functions for CBR
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
A Support System Based on CBR for the Design of Rubber Compounds in Motor Racing
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
The roles of adaptation in case-based design
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Ripple down rules: Turning knowledge acquisition into knowledge maintenance
Artificial Intelligence in Medicine
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Context-aware personal diet suggestion system
ICOST'10 Proceedings of the Aging friendly technology for health and independence, and 8th international conference on Smart homes and health telematics
An ontology-based fuzzy decision support system for multiple sclerosis
Engineering Applications of Artificial Intelligence
An evolutionary divide and conquer method for long-term dietary menu planning
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
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We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients have special needs in regards to their medical conditions, cultural backgrounds, or special levels of nutrient requirements for better recovery from diseases or surgery, etc. Existing computer support for this task is insufficient-many diets are not specifically tailored for the client's needs or require substantial time of a dietitian to be manually developed. Our approach is based on case-based reasoning, an artificial intelligence technique that finds increasing entry into industrial practice. Our approach goes beyond the traditional case-based reasoning (CBR) approach by allowing an incremental improvement of the system's competency during routine use of the system. The improvement of the system takes place through a direct expert user-system interaction while the expert is accomplishing their tasks of constructing a diet for a given client. Whenever the system performs unsatisfactorily, the expert will need to modify the system-produced diet 'manually', i.e. by entering the desired modifications into the system. Our implemented system, menu construction using an incremental knowledge acquisition system (MIKAS), asks the expert for simple explanations for each of the manual actions he/she takes and incorporates the explanations automatically into its knowledge base (KB) so that the system will perform these manually conducted actions automatically at the next occasion. We present MIKAS and discuss the results of our case study. While still being a prototype, the senior clinical dietitian involved in our evaluation studies judges the approach to have considerable potential to improve the daily routine of hospital dietitians as well as to improve the average quality of the dietary advice given to patients within the limited available time for dietary consultations. Our approach opens up a new avenue towards building highly specialised CBR systems in a more cost-effective way. Hence, our approach promises to allow a significantly more widespread development and practical deployment of CBR systems in a large variety of application domains including many medical applications.