Memory-Based Learning

Definition of the Area [from Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. By A. Aamodt and E. Plaza. (1994)]
"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 "
Machine Learning

Contents

Vertical Tabs

Good Starting Places

Publication

CBR in Context: The Present and Future

In Case-based reasoning (CBR), the primary knowledge source is not generalized rules but a memory of stored cases recording specic prior episodes. In CBR, new solutions are generated not by chaining, but by retrieving the most relevant cases from memory and adapting them to to new situations. Thus in CBR, reasoning is based on remembering.

Source

By Leake, David, 1996
overview, text book
Publication

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

Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

Source

By Aamodt, Agnar and Enric Plaza, 1994
overview

General Readings

Publication

Applying Case-Based Reasoning to Manufacturing

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.

Source

By Hinkle, David and Christopher Toomey, 1995
CBR, configuration, shop floor

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 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

Classic Articles & Books

Publication

Experiments with the adaptive graph traverser

"The main topic of the present paper is the proposal that general-purpose search procedures should be able to use accumulating experience of a given problem to improve search efficiency at run time. One of the mechanisms to be described involves optimization of numerical parameters used to control the search, reminiscent of the 'learning by generalization' used in Samuel's (1959) checkers-playing program."

Source

By Michie, D., and Ross, R. , 1969, Machine Intelligence 5
classic

Vertical Tabs