Course content, general
This is an advanced course in case-based reasoning, with an emphasis on a deeper understanding of fundamental issues related to the CBR cycle, such as case representation, case base organization, similarity assessment, case retrieval, adaptation, and learning. Integrating purely case-based methods with generalization-based methods is also witin the scope of the course. A selected set of book chapters and papers will be discussed. The concrete focus will
to some extent depend on the project and interests of the students taking the
course.
This doctoral course runs as a self-study course with guidance.
We will have five meetings, and each time we will have a particular topic up for discussion, to be presented and introduced
by the course students. The presentations should focus on the motivation for the research described in the corresponding article, the research goal(s), the research method, the results, and an evaluation of the results.
The students are assumed to work with the topics of the respective papers in between the
meetings, and to browse the Internet or use other sources to fill in missing
details. The students are also encouraged to arrange group meetings between the
scheduled meetings. All students have to read the
papers addressed and prepare for discussions during the course meetings.
Background assumed, from earlier courses. Read these (again) before the first meeting!
A. Aamodt and E. Plaza, 1994: Case-based reasoning; Foundational issues, methodological variations, and system approaches. AI Communications, 7(1):3959.
D. Aha, 1991: Case-based learning algorithms. Proceedings of the 1991 DARPA Case-Based Reasoning Workshop. Morgan Kaufmann.
Padraig Cunningham, 2008: A Taxonomy of Similarity Mechanisms for Case-Based Reasoning. University College Dublin, Technical Report UCD-CSI-2008-01.
Overview level knowledge of Cunningham's paper is assumed. This paper will in the course be analyzed in much more depth than before, and incuded in the course curriculum.
Course material:
Papers are available at Mendeley CBR archive.
Papers 1-4 will be discussed in the seminar on March 12th!
Papers for first seminar (12/3)
[1] A. Tversky: Features of Similartity. In: Preference , Belief , and Similarity (book), 1st chapter.
[2] M.M. Richter: Similarity.
[3] U. Hahn and N. Chater: Understanding similarity: a joint project for psychology, case-based reasoning, and law.
[4] E.L. Rissland: AI and Similarity.
Papers for second seminar
[5] A. Stahl: Defining similarity measures: Top-Down vs. bottom-up.
[6] A. Stahl: Learning similarity measures; A formal view based on a generalized CBR model.
[7] A. Aamodt: Knowledge acquisition and learning by experience; The role of case-specific knowledge.
[8] T. Gabel and A. Stahl: Exploiting background knowledge when learning similarity measures.
Papers for third seminar
[9] P. Cunningham: A taxonomy of similarity mechanisms for case-based reasoning.
[10, 11, 12] (To be decided)
NTNU-IDI, January 2012
Agnar Aamodt.