Case-Based Reasoning

(a subtopic of Reasoning)

Contents Good Places to Start Readings Online Related Web Sites Related Pages More Readings

"At the highest level of generality, a general CBR cycle may be described by the following four processes: RETRIEVE the most similar case or cases REUSE the information and knowledge in that case to solve the problem REVISE the proposed solution RETAIN the parts of this experience likely to be useful for future problem solving - Agnar Aamodt & Enric Plaza

Good Places to Start

CBR in Context: The Present and Future. By David B. Leake. This chapter from Case-Based Reasoning: Experiences, Lesons, 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." [Here's an illustration from this chapter portraying the dynamics of CBR.]

• Resources available from Professor Leake's pages at Indiana University include:
  • Overview of Case-Based Reasoning (CBR) at Indiana University: "Case-based problem-solving solves problems by retrieving and applying solutions to previous problems. CBR research at Indiana investigates CBR for design support, planning and explanation. Our projects focus especially on issues in case-base maintenance, the use of introspective reasoning to refine indexing and adaptation, integration of CBR with other information tools in a larger task context, case-based knowledge management, and case-based components for scientific computing."
  • Using AI to Guide Users through the Data Maze, a Data and Search Institute project headed by Professor Leake: "One of the great challenges when dealing with large-scale data is providing accurate ways to collect the data and associated metadata when the source is human response or gathered from on-line instrument streams. Intelligent interfaces that employ artificial intelligence methods such as Conversational Case-Based Reasoning (CCBR) can ease the burden on the ingest clients (including humans) by technology that engages the client in a guided conversation, with questions selected strategically, based on existing data and the developing description of the encounter."
  • Collection of selected publications that are available online.
  • Links to past projects such as the SWALE project which "explores case-based reasoning (CBR) as a basis for creativity."

Case-based Reasoning Research - learning through experience, at AIAI, the Artificial Intelligence Applications Institute at the University of Edinburgh's School of Informatics. "Case-based Reasoning is one of the most successful applied AI technologies of recent years. Commercial and industrial applications can be developed rapidly, and existing corporate databases can be used as knowledge sources. Helpdesks and diagnostic systems are the most common applications. Case-based Reasoning (CBR) is based on the intuition that new problems are often similar to previously encountered problems and, therefore, that past solutions may be of use in the current situation."

• See their collection of CBR Projects that includes fraud detection, information retireval, and more.

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."

• See our Machine Learning page.

Some examples of CBR at work from the Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-04):

• Deployed Application Papers:
  • Tenth Anniversary of the Plastics Color Formulation Tool. By William Cheetham. "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."
  • 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. "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."
• Emerging Application Papers
  • CaBMA: Case-Based Project Management Assistant. By Ke Xu and Héctor Muñoz-Avila. "We are going to present an implementation of an AI system, CaBMA, built on top of a commercial project management tool, MS Project. Project management is a business process for successfully delivering one-of-a kind products and services under real-world time and resource constraints. CaBMA (for: Case-Based Project Management Assistant) provides the following functionalities: (1) It captures cases from project plans. (2) It reuses captured cases to refine project plans and generate project plans from the scratch. (3) It maintains consistency of pieces of a project plan obtained by case reuse. (4) It refines the case base to cope with inconsistencies resulting from capturing cases over a period of time. CaBMA adds a knowledge layer on top of MS Project to assist the user with his project management tasks."

Also see: Analogy: "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." -A. Aamodt and E. Plaza

Readings Online

A Discourse on Law and Artificial Intelligence. By Michael Aikenhead (1996). 5 Law Technology Journal 1. "[T]he dichotomy between rule based systems and cased based reasoning systems in AI and law research reflects an underlying jurisprudential debate that has raged for the last century. ... Instead of implying that legal reasoning is primarily a process of deduction or a process of analogising the theory of law as discourse requires a richer view of the process of legal reasoning."

• Also see our Law page.

The Third International Conference on Case-Based Reasoning (ICCBR '99). 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. ..."

FLAIRS. See the Proceedings of the International Florida Artificial Intelligence Research Society Conference (AAAI Press) for papers collected for the Case-Based Reasoning Special Track (2004, 2005) and for CBR generally (2001, 2002, 2003).

Applying Case-Based Reasoning to Manufacturing. By David Hinkle and Christopher Toomey. AI Magazine 16(1): Spring 1995, 65-73. "CLAVIER is a case-based reasoning (CBR) system that assists in determining efficient loads of composite material parts to be cured in an autoclave. CLAVIER's central purpose is to find the most appropriate groupings and configurations of parts (or loads) to maximize autoclave throughput yet ensure that parts are properly cured. CLAVIER uses CBR to match a list of parts that need to be cured against a library of previously successful loads and suggest the most appropriate next load. clavier also uses a heuristic scheduler to generate a sequence of loads that best meets production goals and satisfies operational constraints. The system is being used daily on the shop floor and has virtually eliminated the production of low-quality parts that must be scrapped, saving thousands of dollars each month. As one of the first fielded CBR systems, CLAVIER demonstrates that CBR is a practical technology that can be used successfully in domains where more traditional approaches are difficult to apply."

Case-Based Reasoning. By Dr. Bonnie Morris, West Virginia University. A succinct and understandable explanation.

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.

Textual case-based reasoning. By Rosina O. Weber, Kevin D. Ashley and Stefanie Brüninghaus. Knowledge Engineering Review. "What is textual case-based reasoning? Case-based reasoning (CBR) consists of comparing a new problem to previously solved cases in order to draw inferences about the problem and to guide decision making. Textual case-based reasoning (TCBR) is a subfield of CBR concerned with research and implementation on case- based reasoners where some or all of the knowledge sources are available in textual format. It aims to use these textual knowledge sources in an automated or semi-automated way for supporting problem solving through case comparison."

Related Web Sites

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.

• Start with a visit to the classroom where amongst the many wonderful materials, is the suggested introductory article Case-Based Reasoning: A Review, by Ian Watson & Farhi Marir.
• CBR research projects in areas such as design, estimating, planning, decision support, finance, and legal reasoning.
• CBR commercial applications

Automated Case Based Reasoning (CBR) at NRC-IIT, Canada's National Research Council's (NRC) Institute for Information Technology (IIT)

CBR Resources. From David W. Aha. Topics covered: Applications | Bibliographies | Books | Commercial | Conferences | Courses | Home Pages | Information | Journals | Mailing Lists | Medicine | Projects | Research Groups and Projects | Research Software | Talks and Tutorials.

Case Based Reasoning in Cardiovascular Disease. "Learning diagnostic expertise from experience." A project from theClinical Decision Making Group at the MIT Laboratory for Computer Science.

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."

Case-Based Reasoning Research Group at the University of Pittsburgh. Check out their projects, and don't miss their papers about Intelligent Tutoring, Textual Case-Based Reasoning, and Ethical Reasoning.

• Also see:
  • this position paper for AAAI 2006 Fellows Symposim by group member, Kevin Ashley: "I have been interested in usingAI computational models of case-based reasoning empirically to investigate semantic relationships betweenabstract normative principles and fact-specific cases. Moral and legal philosophers have long observed adialectical relationship between them: the abstract principles inform the decisions of specific cases, but thedecisions, in turn, elaborate the principles’ meaning. I thank my dissertation adviser, Edwina Rissland, for 'turning me on' to this insight a long time ago (See Ashley & Rissland, 2003, p. 31)."
  • the publications with abstracts collected by group member, Stefanie Bruninghaus. Many of the articles can be accessed online.

Case-Based Reasoning Resources. Maintained by David Leake.

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."

GAIA - the Group for Artificial Intelligence Applications: "a group of professors and graduate students, interested in basic and applied AI research, working in the Dpt. of Software Engineering and Artificial Intelligence at Complutense University of Madrid.Regarding basic research our aim is to advance the state of the art in AI research related to Case-Based Reasoning (CBR), Knowledge Acquisition and Machine Learning. Our main focus is on cost-effective solutions to inject knowledge into CBR systems, empowering CBR knowledge-light approaches with off-the-shelf knowledge components and knowledge mined from readily available data."

International Conference on Case-Based Reasoning.

Knowledge Acquisition and Projection Lab, one of the Pervasive Technology Labs at Indiana University, seeks to "develop new insights about how knowledge is created, managed and used within organizations, and then based on this knowledge create advanced information technology systems that will create new possibilities for management, delivery, and use of institutional knowledge.

• Also see: Knowledge Acquisition and Projection Lab completes Navy project. Indiana University press release (December 21, 2005). "Researchers in Indiana University's Knowledge Acquisition and Projection Lab -- part of Pervasive Technology Labs -- along with computer scientists from the IU School of Informatics, have completed a project for the U.S. Navy in which they developed key components of the Navy's maintenance Knowledge Projection System. ... This system uses an artificial intelligence technique known as 'case-based reasoning,' which draws upon solutions to previous, similar problem scenarios to help engineers and crew diagnose and solve new problems. The system also serves as a 'recording system' for engineering expertise within the Navy's widely distributed maintenance organization, capturing engineering expertise as it is expressed during problem-solving sessions."

Related AI Topics Pages

• Expert Systems
• Induction
• Law
• Machine Learning
• Reasoning

More Readings

Bergmann, R., Althoff, K.-D., Breen, S., Göker, M., Manago, M., Traphöner, R., and Wess, S. 2003. Developing Industrial Case-Based Reasoning ApplicationsThe INRECA Methodology (2nd Edition). Lecture Notes in Artificial Intelligence, Vol. 1612. Springer - Buchreihe.

Leake, David, ed. 1993. Case-Based Reasoning: Papers from the AAAI Workshop. Technical Report WS-93-01. American Association for Artificial Intelligence, Menlo Park, California. "The AAAI-93 workshop on case-based reasoning brought together investigators from theoretical and applied viewpoints to exchange results and discuss their ramifications. Topics covered included progress in content theories and knowledge representation; theoretical and applied results in indexing and case retrieval; methods for similarity assessment; theory and practice of case adaptation; hybrid systems; and strengths, limitations, and open issues of implemented CBR systems."

Nagel, Rebecca Thompson. June/July 1998. HAL, Esq. - Will computers someday replace attorneys in the delivery of legal services? We profile one woman whose work with artificial intelligence could forecast the future of the profession. Law Office Computing (subscription req'd.). "A computer that can think like an attorney? Artificial intelligence in a real-life application? Science fiction, right? Well, a system like the one described above is not yet available...commercially. But it does exist in the laboratory of University of Massachusetts, Amherst professor Dr. Edwina Rissland. ... The key to these programs is case-based reasoning (CBR) -- a subsection of AI that uses examples and analogy, as opposed to rules or logic, to solve problems."

Rissland, Edwina L. AI and Similarity. 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."