Fall semester 2018
Module organizer: Professor Agnar Aamodt and Associate Professor Kerstin Bach
Course content and start-up
A selected set of papers (including book chapters) related to case-based reasoning combined with general domain knowledge and other method components will be discussed.
The actual focus will to some extent depend on the project and interests of the students taking the course.
The course will be run as a set of seminar meetings in which selected papers are summarized by the students and discussed in the group.
The first seminar meeting will take place on Wednesday September 12th at 12:00, in IDI building room 242
In the start-up meeting a general overview of the course will be given, the course material will be presented and discussed, and a scheme for dividing the presentations between the students will be initialized. For this meeting, we expect that the participants have read papers 1.-3. (usually as part of earlier courses) as well as the two papers listed in Seminar 1.
The text for these seminars will be based on the textbook Case-Based Reasoning, by Michael Richter and Rosina Weber (Springer 2013), together with additional papers. The textbook is downloadable for free if you are on the NTNU network, at Springer Link. Several chapter from this book was included in the regular ML+CBR course TDT4173.
It is assumed that the students have a background in CBR corresponding to the CBR part of the AI-2 course (TDT4171) and the ML+CBR course (TDT4173). This means that all participants should - at least - make themselves familiar with the following set of papers before the course starts:
- A. Aamodt and E. Plaza, 1994: Case-based reasoning; Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), pgs. 39-59.
- Chapters 2, 3, 6, and 8 in Richter/Weber's Case-Based Reasoning Textbook
- A. Aamodt: Knowledge-intensive case-based reasoning in Creek. ECCBR 2004. LNAI 3155, Spinger, 2004. pgs. 1-16.
We will have four meetings to discuss the set of course papers. The papers will constitute the course "pensum".
As students of this module you 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.
You are also encouraged to arrange group meetings among yourselves between the scheduled seminar meetings. In the seminar meetings each paper will be presented for discussion by one or two students, who will summarize it, point out particular scientific issues of interest, research evaluation method applied (if any), and strong and weak points of the papers.
You all are expected to read the papers in question before each meeting and prepare for the discussions.
Seminars including the list of papers
The seminar plan and a first set of papers will be decided in the first meeting, and the detailed content will be determined as the course develops.
Seminar 1 - The start-up meeting
Time and place: Wednesday, September 12, 12-14, room 242 IT West
For the first meeting you should read the following two, rather light-weighted, papers, which we will discuss in plenum. Together they provide a broader context for CBR, in general as well as with respect to the important issue of similarity.
- David Aha: The omniprescence of case-based reasoning in science and application. Proceedings of the Seventeenth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence, 1998. pp 261-273.
- Edwina Rissland: AI and Similarity. IEEE Intelligent Systems, May/June 2006. pp 39-49.
The continued content of the seminar will be rather similar to last year's, although with some changes. Below, for your information, you will find the seminar scheme and content of TDT55 in the fall 2017:
Seminar 2 - Adaptation
Time and place: Wednesday, October 3, 12-14, room 242 IT West
- Susan Craw et al.: Learning adaptation knowledge to improve case-based reasoning. Artificial Intelligence Journal, 170(2006), May/June 2006. pp 39-49.
- Chapter 9 in Richter/Weber's Case-Based Reasoning Textbook
Seminar 3 - Learning in CBR
Time and place: Wednesday, October 17, 12-14, room 242 IT West
- Chapter 10 in Richter/Weber's Case-Based Reasoning Textbook (focus on learning aspects)
- Reinaldo Bianchi et al.: Transferring knowledge as heuristics in reinforcement learning: A case-based approach. Artificial Intelligence Journal, 226(2015), pp 102-121.
- Armin Stahl: Learning Similarity Measures: A Formal View Based on a Generalized CBR Model. Case-Based Reasoning Research and Development. ICCBR 2005 pp 507-521.
Seminar 4 - CBR system maintanenance
Time and place: Wednesday, November 7, 12-14, room 242 IT West
- Barry Smyth, Mark Keane: Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems. IJCAI'95 Proceedings, Volume 1, pages 377-382. pp 39-49.
- David Leake: Abduction, Experience, and Goals: A Model of Everyday Abductive Explanation. The Journal of Experimental and Theoretical Artificial Intelligence, Volume 7, Issue 4, Jan 1995, pp 407-428
- David Leake, Matthew Whitehead Case Provenance: The Value of Remembering Case Sources In: Weber R.O., Richter M.M. (eds) Case-Based Reasoning Research and Development. ICCBR 2007. Lecture Notes in Computer Science, vol 4626. Springer, Berlin, Heidelberg
Seminar 5 - Course Summary
Time and place: Wednesday, November 14, 12-14, room 242 IT West
- Summary of the course
- Preparation for the exam
Time and place: November 29, 9 - 18, room 322 IT West (Agnar's office)
- At the oral exam you will get 2-3 questions/topics to talk about (appx. 10 mins. each), from the set of book chapters and papers.
- Schedule for the oral exam (updated Nov 23)
Schedule (planned - updates are possible)
|# 2||Susan Craw et al.: Learning adaptation knowledge to improve case-based reasoning.||Rasmus (p) / Sondre R|
|# 2||Chapter 9 in Richter/Weber's Case-Based Reasoning Textbook||Morten (p) / Thomas (p)|
|17.10.||Learning in CBR|
|# 3||Chapter 10 in Richter/Weber's Case-Based Reasoning Textbook||Martin (p)/ Andreas (p) [pptx]|
|# 3||Reinaldo Bianchi et al.: Transferring knowledge as heuristics in reinforcement learning: A case-based approach.||Johannes (p) / Isak (p)|
|# 3||Armin Stahl: Learning Similarity Measures: A Formal View Based on a Generalized CBR Model. (Part I)||Tina (p) / Vidar|
|7.11.||CBR system maintanenance|
|# 4||Barry Smyth, Mark Keane: Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems.||Sander (p) / Ole B.|
|# 4||David Leake: Abduction, Experience, and Goals: A Model of Everyday Abductive Explanation||Sondre (p) / Piraveen|
|# 4||David Leake, Matthew Whitehead: Case Provenance: The Value of Remembering Case Sources||Ivar (p) / Ole A.|
(p) indicates the presenter(s) of the paper