Case-Based Reasoning

Using Similar Situations from the Past to Solve a Present Problem

"At the highest level of generality, a general CBR cycle may be described by the following four processes:

  1. RETRIEVE the most similar case or cases
  2. REUSE the information and knowledge in that case to solve the problem
  3. REVISE the proposed solution
  4. RETAIN the parts of this experience likely to be useful for future problem solving "

"Analogy-based reasoning: This term is sometimes used, as a synonym to case-based reasoning, to describe the typical case-based approach... However, it is also often used to characterize methods that solve new problems based on past cases from a different domain, while typical case-based methods focus on indexing and matching strategies for single-domain cases."

 - Agnar Aamodt & Enric Plaza

Representation & Reasoning

Contents

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Good Starting Places

Link

* CBR in Context: The Present and Future

By David B. Leake. This chapter from Case-Based Reasoning: Experiences, Lessons, and Future Directions, David B. Leake, editor (AAAI Press / MIT Press, 1996) is available online and "provides an introduction to case-based reasoning, discusses motivations for CBR, and describes the central steps in the CBR process. It examines the relationship of CBR to other approaches, and discusses major research areas, open issues, and promising opportunities for CBR. It surveys and relates numerous approaches within CBR and provides more than 150 references to international CBR research."

Source

1996
Link

ai-cbr

[Note: Although this site is no longer being maintained, there's still plenty of basic information and leads to additional resources.] There's something for everyone at this site, such as a page about applied CBR, a page offering actual case bases you can download, a searchable bibliography, and even a virtual library.

Source

By Watson, Ian & Farhi Marir, 1994
overview
Link

Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches.

By A. Aamodt and E. Plaza. (1994) Artificial Intelligence Communications, IOS Press, Vol. 7:1, pp. 39-59. Very thorough and very clearly written. "A very important feature of case-based reasoning is its coupling to learning. The driving force behind case-based methods has to a large extent come from the machine learning community, and case-based reasoning is also regarded a subfield of machine learning."

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General Readings

Link

AI and Similarity

By Edwina L. Rissland. IEEE Intelligent Systems (May/June 2006) 21(3): 39-49. "For AI to become truly robust, we must further our understanding of similarity-driven reasoning, analogy, learning, and explanation. Here are some suggested research directions." This article is part of a special issue on the Future of AI.

Source

2006
Link

Tenth Anniversary of the Plastics Color Formulation Tool

By William Cheetham. Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-04). "Since 1994 GE Plastics has employed a case-based reasoning tool that determines color formulas which match requested colors. This tool, called FormTool, has saved GE millions of dollars in productivity and material (i.e. colorant) costs. The technology developed in FormTool has been used to create an on-line color selection tool for our customers called ColorXpress Select. A customer innovation center has been developed around the FormTool software."

Source

2004
Link

The General Motors Variation-Reduction Adviser: Deployment Issues for an AI Application

By Alexander P. Morgan, John A. Cafeo, Kurt Godden, Ronald M. Lesperance, Andrea M. Simon, Deborah L. McGuinness, and James L. Benedict. Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-04). "The General Motors Variation-Reduction Adviser is a knowledge system built on case-based reasoning principles that is currently in use in a dozen General Motors Assembly Centers. This paper reviews the overall characteristics of the system and then focuses on various AI elements critical to support its deployment to a production system. A key AI enabler is ontology-guided search using domain-specific ontologies."

Source

2004
Publication

A Computational Model of Reasoning from the Clinical Literature

Source

By Rennels ,G., E. H. Shortliffe, F. E. Stockdale, P. L. Miller, 1987
classic
Publication

Derivational analogy: a theory of reconstructive problem solving and expertise acquisition

Derivational analogy, a method of solving problems based on the transfer of past experience to new probiem situations, is discussed in the context of other general approaches to problem solving. The experience transfer process consists of recreating lines of reasoning, including decision sequences and accompanying justifications, that proved effective in solving particular problems requiring similar initial analysis. The role of derivational analogy in case-based reasoning and in automated expertise acquisition is discussed.

Source

By Carbonell, J. G. , 1986
classic, transfer learning
Link

A Distributed Case-Based Reasoning Application for Engineering Sales Support

By Ian Watson and Dan Gardingen. In Proceedings 16th International Joint Conference on Artificial Intelligence (IJCAI-99), Vol. 1: pp. 600-605. Morgan Kaufmann Publishers. This paper received the IJCAI-99 Distinguished Paper Award.

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Organizations

Link

Case-Based Reasoning Group

At the University of Massachusetts at Amherst, Department of Computer Science. "Current research projects include projects to investigate the use of multiple case representation and indexing schemes in precedent-based CBR, the effect of high level reasoning goals on supporting CBR tasks and vice versa in a mixed paradigm blackboard-based architecture, the use of CBR for generation of retrieval strategies in the context of information retrieval, and the automatic selection of parameters for dynamic scheduling problems."

Source

Educational Resources

Link

Case-Based Reasoning Wiki


In this Wiki site for Case-Based Reasoning, you will find information on the following topics:

Source

2012
bibliography, collection
Link

Case-Based Reasoning courses.

Course materials for several CBR courses around the world.

Source

2012
courses, syllabi
Link

Case-Based Reasoning Resources

Collection of Applications | Bibliographies | Books | Commercial | Conferences | Courses | Home Pages | Information | Journals | Mailing Lists | Medicine | Projects | Research Groups and Projects | Research Software | Talks and Tutorials

Valuable set of resources, although no longer maintained.

Source

By Aha, David W.
CBR, bibliography, collection, resources

Hardware and Software

Link

CmapTools knowledge construction program

From the Institute for Human and Machine Cognition (IHMC). "TheCmapTools program empowers users to construct, navigate, share and criticize knowledge models represented as concept maps. It allows users to, among many other features, construct their Cmaps in their personal computer, share them on servers (CmapServers) anywhere on the Internet, link their Cmaps to other Cmaps on servers, automatically create web pages of their concept maps on servers, edit their maps synchronously (at the same time) with other users on the Internet, and search the web for information relevant to a concept map. The CmapTools client is free for use by anybody, whether its use is commercial or non-commercial. In particular, schools and universities are encouraged to download it and install it in as many computers as desired, and students and teachers may make copies of it and install it at home."

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Journals and Proceedings

Link

The Third International Conference on Case-Based Reasoning

By Klaus-Dieter Althoff, Ralph Bergmann, and Karl Branting. AI Magazine 22(1): Spring 2001, 116-118. "Case-based reasoning (CBR) is a problem-solving paradigm that uses exemplars or previous solutions to solve new problems (Aamodt and Plaza 1994; Kolodner 1993). Three characteristics of CBR account for its growing popularity. First, CBR can reduce search.... The second characteristic is that CBR permits problem solving even when the underlying domain theory is incomplete.... Finally, CBR can facilitate knowledge acquisition.... The conference began with an Industry Day, chaired by Brigitte BartschSporl (BSR Consulting Munich) and Wolfgang Wilke (tecinno GmbH, Kaiserslautern). The Industry Day presentations illustrated a number of successful commercial applications of CBR in the United States and Europe...."

Source

2001
Link

International Conference on Case-Based Reasoning

Includes a CBR Wiki and archives back to 2001.

Source

Link

Proceedings of the International Florida Artificial Intelligence Research Society Conference (FLAIRS)

"The Florida Artificial Intelligence Research Society Conference (FLAIRS) was founded in 1987 to promote and advance artificial intelligence research within the state of Florida, fostering interaction between researchers at colleges, universities, and industry.

From its inception, one major goal of the society has been to promote the advancement of AI through a yearly conference. During the first two years of the conference, participation was limited to Florida residents. Since 1990, the conference has been open to all and has logged a dramatic increase in participation from around the world, due in part to the warm climate offered by this conference location, but also due to the increase in research quality."

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Videos

Video

CBR Noir

CBR Noir is a high-level, tongue-in-cheek introduction to Case-Based Reasoning (CBR). It is presented in the style of a classic Film Noir murder mystery. For the connoisseur, the dialogue incorporates the names of many early CBR systems; see if you can count them all before the credits roll.

Source

3 min, 2008, AAAI

Classic Articles & Books

Publication

A Computational Model of Reasoning from the Clinical Literature

Source

By Rennels ,G., E. H. Shortliffe, F. E. Stockdale, P. L. Miller, 1987
classic
Publication

Toward memory-based reasoning

Available for a fee.

Source

By Stanfill, C. and Waltz, D. , 1986
classic, overview
Publication

Derivational analogy: a theory of reconstructive problem solving and expertise acquisition

Derivational analogy, a method of solving problems based on the transfer of past experience to new probiem situations, is discussed in the context of other general approaches to problem solving. The experience transfer process consists of recreating lines of reasoning, including decision sequences and accompanying justifications, that proved effective in solving particular problems requiring similar initial analysis. The role of derivational analogy in case-based reasoning and in automated expertise acquisition is discussed.

Source

By Carbonell, J. G. , 1986
classic, transfer learning

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