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DM: Call for Participation for IJCAI-99 Workshop on Automating the

From: Sarabjot S. Anand
Date: Wed, 19 May 1999 16:10:42 -0400 (EDT)
Construction of Case Based Reasoners
Date: Wed, 19 May 1999 00:33:14 -0500

IJCAI-99 Workshop on Automating the Construction of Case Based 
Venue: City Conference Center, Stockholm, Sweden
2nd August, 1999

Note: Registeration with organising commitee required by 31st of May,


Case based reasoning (CBR) was presented to the field of knowledge-based systems as a solution to the knowledge acquisition bottleneck and brittleness, ails from which rule-based systems were known to suffer. However, CBR systems also require substantial knowledge acquisition effort (e.g. acquiring cases, case vocabulary, retrieval knowledge, adaptation knowledge). Acquiring this knowledge for CBR systems has traditionally been heavily dependent on the availability of a domain expert. Today most organisations have large operational data sets that, to varying degrees, model various real-world processes. The question arises: Can knowledge implicitly contained within these databases be harnessed using data mining techniques to reduce the domain expert dependence present in case base development?

Data mining is considered to be one of the ten most important technologies (Gartner Group Inc, January 1998), in terms of the potential impact on industry and wide-ranging applications. While CBR and data mining make similar assumptions of regularity, typicality and consistency, they are largely complementary technologies. Data mining focuses on the process of discovering knowledge while CBR focuses on the management and application of knowledge through representation, retrieval, re-use, revision and retention of case knowledge. With major strides being made in knowledge integration within CBR systems, CBR may be viewed as a general knowledge management and problem solving tool. Knowledge management has, to date, not been central to data mining research. Keeping this in mind, this workshop focuses on how CBR and data mining can support each other. In particular, how data mining can aid the construction on case based reasoners.

Data mining may be used to support the acquisition of knowledge required to construct the case-base as well as perform the four CBR functions of retrieve, reuse, revise and retain in a number of ways. However, to do so, a number of research issues must be addressed. These issues will form the primary focus of the workshop and include (not exhaustively)

- A database is not necessarily a case base; what characterises a case base?
- How do you identify a case?
- To what extent can recent developments within data mining contribute to automated case base construction (authoring)?
- How can case extraction (from, for example, documents, structured logs, the World-Wide Web) be supported?
- How is similarity measured between two cases?
- What is the best structure for individual cases and the case base as a whole?
- Can suitable indexing regimes be elicited from data about the domain?
- What are the sources for adaptation knowledge?
- Can the discovery of useful adaptation knowledge be automated?
- How can feedback from the application of solved cases be used to provide useful knowledge for future applications of the CBR system?
- How can this feedback be used in future applications of the CBR system?
- How do we keep case knowledge up-to-date i.e. learning phase in CBR?
Workshop Participation

The workshop will be kept small, with a maximum of 40 participants. Preference will be given to active participants selected on the basis of their submitted papers. If you are interested in attending the workshop please e-mail us at before the 31st of May, 1999 to guarantee a place.

According to IJCAI rules, all workshop attendees must register for the main conference.

Invited Speakers
Developing Industrial Case-Based Reasoning Applications Using the INRECA Methodology
Speaker: Dr. Ralph Bergmann, University of Kaiserslautern, Germany
Constructing Competent Case-Based Reasoners: Theories, Tools and Techniques
Speaker: Dr. Barry Smyth, University College Dublin, Ireland
Accepted Papers
Arijit Sengupta, David C. Wilson, David Leake: On Constructing the Right Sort of CBR Implementation
Margaret Richardson: A Systematic Machine Learning approach to case matching in the development of case-based reasoning systems
Ture Friese: Utilisation of Bayesian Belief Networks for Explanation-Driven CBR Norway
Ole M. Winnem, Hans Inge Myrhaug, Nils Moe: Applying CBR and Neural Networks to Noise Planning
D. McSherry: Relaxing the Similarity Criteria in Adaptation Knowledge Discovery
David Riano, Juan Corchado: Descriptive rules model for learning and pruning the memory of CBR systems
Paulo Gomes and Carlos Bento: Converting Programs into Cases for Software Reuse
Helge Langseth, A. Aamodt, Ole M. Winnem: Learning Retrieval Knowledge from Data
Forde Sormo, A. Aamodt: Knowledge Elicitation for Improved CBR
Jacek Jarmulak and Susan Craw: Genetic Algorithms for feature Subset selection and Weighting
C. Giraud-Carrier and I. Dattani: Case-based Management through Induction
Organising Committee
Sarabjot S. Anand, University of Ulster, N. Ireland
Agnar Aamodt, Norwegian University of Science and Technology, Norway
David Aha, Navy Center for Applied Research in AI, USA

Program Committee
Klaus-Dieter Althoff,  Fraunhofer Institute for Experimental Software
Engineering, Germany
Karl Branting, University of Wyoming, USA
Werner Dubitzky, University of Ulster, Northern Ireland
Mark Keane, Trinity College, Dublin, Republic of Ireland
Hiroaki Kitano, Sony Computer Science Laboratory Inc, Japan
David Leake Indiana University, USA
David Patterson, University of Ulster, Northern Ireland
Enric Plaza, Spanish Scientific Research Council, Barcelona, Spain

Sarabjot S. Anand
School of Information and Software Engineering
University of Ulster,
County Antrim BT37 0QB
Northern Ireland
Tel: 01232 366671
Fax: 01232 366068

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