Overview of Case-Based Reasoning, Applications and Implementations

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Overview of Case-Based Reasoning

Case-based reasoning is a method of finding appropriate solutions to similar problems by referring to past problem-solving experience and case studies. The following is an overview of case-based reasoning and its advantages.

First, the steps of case-based reasoning are as follows

Collecting cases: Cases or examples related to the problem to be solved are collected in a database. This includes the history of previous problems, failures, and solutions, as well as relevant data and features. Cases typically contain information on problem descriptions, characteristics, and solutions.
Similarity Calculation: When a new problem arises, the similarity between the problem and previous cases is calculated. Similarity is evaluated based on matching of problem and case features and calculation of distances.
Solution Application: After finding a similar problem or case, the relevant solution is applied to the case. This allows for the reuse of previous successful solutions for new problems.
Modify and learn solutions: Evaluate the effectiveness and results of solutions applied to new problems and modify or learn as needed. Because case-based reasoning is based on experience, it allows for continuous learning and improvement in new problems and cases.

Advantages of case-based reasoning include

Reuse of experience: By reusing past problem-solving experience, solutions to new problems can be found quickly. This allows for efficient problem solving.
Adaptability and flexibility: Case-based reasoning can flexibly modify and extend the case base to address new problems. The system itself can grow and evolve through modification and learning of solutions.
Interpretability: Case-based reasoning makes it easier to interpret the results and reasons for inferences because each case and its solution is explicitly presented. This makes it easier for users to understand and accept the system’s results.

Case-based reasoning has been widely applied in a variety of domains. Some specific applications are described below.

Case Studies in the Application of Case-Based Reasoning

Failure Diagnostics: Case-based reasoning is used in machine and system failure diagnostics to find solutions to similar failure cases based on past failures and solutions. Case-based reasoning is recognized as an effective failure diagnostic because it uses empirical reasoning in real-world problems.
Customer Support: Case-based reasoning is used in customer support and help desks to provide appropriate solutions to customer inquiries and trouble tickets by referring to similar problems and past cases. This enables fast and efficient customer support.
Medical Diagnosis: Case-based reasoning is also used in medical diagnosis, allowing for the creation of diagnoses and treatment plans based on new patient symptoms and characteristics, using past patient cases and diagnostic results as a reference. Case-based reasoning complements the experience and knowledge of physicians and specialists to help support accurate diagnosis.
Product Design and Improvement: Case-based reasoning is used in the process of product design and improvement, allowing the user to refer to examples from previous product development and improvement projects to find solutions to similar requirements or problems. This allows for efficient decision-making and knowledge reuse in the design of new products and improvement of existing products.

Next, we discuss the algorithms used for case-based reasoning.

On the algorithms used for case-based reasoning

The following algorithms are used in case-based reasoning

k-nearest neighbors (k-NN): The nearest neighbor (k-NN) method finds the most similar previous case to a new problem and applies the relevant solution to that case. A distance function (e.g., Euclidean distance or cosine similarity) is used to calculate similarity, and k-NN may consider k nearest neighbors cases for majority voting and weighting.
Case-based Reasoning (CBR): Case-based reasoning is a method that refers to the entire past case base to find the most appropriate solution to a new problem. Based on the similarity of the cases, solutions from similar cases are applied. It can also improve the system through case modification and learning.
Instance-based Learning: An argumented model is a type of case-based reasoning that retains previous cases and allows the most similar cases to be applied to new problems. Using this method, the similarity between cases can be calculated based on case characteristics and attributes, and parameters such as the number of cases to be applied and weighting can also be set.

Machine learning techniques may also be combined with case-based reasoning. This would be like using machine learning algorithms (e.g., support vector machines or random forests) to model case features and similarities and use them for inference.

Libraries and platforms that can be used for case-based reasoning

There are several libraries and platforms available to implement case-based reasoning. The following is a representative list of some of them.

Python’s scikit-learn: scikit-learn is a machine learning library in Python that can be used for case-based inference, including nearest neighbor (k-NN) methods. scikit-learn includes classes implementing nearest neighbor methods ( KNeighborsClassifier and RadiusNeighborsClassifier) that implement nearest neighbor methods.
jCOLIBRI in Java: jCOLIBRI is a framework for implementing case-based reasoning in Java. It supports various algorithms for case-based reasoning (e.g., nearest neighbor methods, case-based reasoning, etc.).
CBR-Works: CBR-Works is a framework for case-based reasoning. It is implemented in Java and provides the functionality required for case-based reasoning (case management, similarity computation, solution application, etc.).
MyCBR: MyCBR will be a software tool for case-based reasoning, implemented in Java and providing the functionality needed to create cases, calculate similarities, apply solutions, etc.

In order to address the challenges described next, case-based reasoning has recently become a mainstream approach, not only using these libraries and platforms, but also using general machine learning libraries (such as Python’s TensorFlow and PyTorch, Java’s Weka, etc.).

Case-Based Reasoning Challenges

The following describes the challenges of case-based reasoning.

Evaluation of similarity: Case-based reasoning searches for similar past cases to solve a new problem. However, proper similarity assessment is important, and requires appropriate selection of which features and attributes influence similarity judgments, and consideration of the varying importance of different attributes in different problems.
Problem representation: It is important to represent the problem in a format suitable for case-based reasoning; an inappropriate representation of the problem will prevent proper similarity evaluation and retrieval. Care should be taken in the choice of problem representation and how the problem is characterized.
Case storage and management: Case-based reasoning requires storing and managing large numbers of past cases, and appropriate case storage and indexing methods must be selected to enable efficient retrieval. It also needs to be able to handle the addition of new cases and updates to existing cases.
Past dependence of knowledge: In case-based reasoning, problems are solved based on past experiences and cases. However, relying only on past cases makes it difficult to respond to new situations and changes. Therefore, it is necessary to consider adding and updating new information and knowledge.
Incompleteness of cases: Past cases may not be complete and may contain missing information or errors. This may lead to inferences based on incomplete information, which may make it difficult to obtain accurate results in such cases.

To address these issues, it is necessary to select appropriate cases and representation methods, add and update knowledge, and improve case management methods. Therefore, improvements using AI technology and other techniques are needed. These are described below.

Improvements using AI techniques in case-based reasoning

Feature Selection and Weighting: The selection and weighting of features and attributes of a problem can be improved by using machine learning methods to improve the evaluation of similarity. For example, supervised learning algorithms can be used to learn the relationship between past cases and the feature vector of the problem and automatically extract important features.
Improved case representation: Improved problem and case representation methods can enable more effective similarity assessment. Natural language processing techniques can be used to appropriately vectorize problem statements and cases to capture semantic relationships, and graph-based representations can be used to consider relationships among cases.
Automatically extract and manage cases: cases can be automatically extracted and organized using data mining and natural language processing techniques; appropriate cases can be collected from a large number of sources using web scraping and text analysis; and knowledge bases and ontologies can be built to classification and indexing efficiently will also be important.
Complementing and updating cases: In case-based reasoning, past cases may not be able to cope with new situations. Therefore, it is necessary to incorporate new information and knowledge, and to use automatic information extraction and self-learning methods to add new cases and update existing ones.

Combined, these improvements are expected to improve the accuracy and flexibility of case-based reasoning.

The next section describes the implementation procedure for case-based reasoning in general cases.

Implementation Procedure for Case-Based Reasoning

The implementation procedure for case-based reasoning consists of the following steps

Gather cases: Gather cases relevant to the problem domain. This means collecting data from existing sources, such as past cases and problem-solving history.
Representation of the cases: The collected cases should be represented appropriately. Case representation methods vary by problem and domain, but the characteristics, attributes, and solutions of the cases need to be appropriately represented.
Similarity Calculation: Calculate the similarity between cases in order to find cases that are similar to the new problem. A common approach would be to calculate the distance or similarity between cases using a distance function (e.g., Euclidean distance, Cosine similarity).
Apply the solution: take the solution from the similar cases and apply it to the new problem. Solutions from similar cases may be combined (e.g. majority voting, weighting).
Evaluate the results: Evaluate the results of the applied solution. This allows us to measure the quality and usefulness of the solutions and to obtain feedback to improve the inference system.
Modify and learn the system: modify and learn the case and similarity models in response to feedback in order to improve the case-based reasoning system. Here, new cases may be added, or cases may be modified or deleted.

A specific implementation example using these procedures is described below.

Case-based reasoning implementation in python

An example implementation of case-based reasoning in Python is described below.

Import the required libraries: First, import the required Python libraries. This is, for example, the scikit-learn library.

from sklearn.neighbors import KNeighborsClassifier

Case collection and representation: Cases relevant to the problem domain are collected and represented appropriately. This is represented, for example, as a data set consisting of values of features and attributes and corresponding target variables (solutions).

# Creating a data set of cases
X_train = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] # Feature Value
y_train = [0, 1, 0] # Target Variable (Solution)

Creating a model for the nearest neighbor (k-NN) method: Use scikit-learn’s KNeighborsClassifier class to create a model for the nearest neighbor method. Here, set the appropriate hyperparameters (e.g., k values).

# Creation of k-NN model
k = 3 # Number of Neighborhoods
knn_model = KNeighborsClassifier(n_neighbors=k)

Model Learning: In case-based inference, model learning is synonymous with the formation of a case data set. Models are learned based on case data.

# Model Learning
knn_model.fit(X_train, y_train)

Reasoning about a new problem: Use the learned model to reason about a new problem.

# New issue data
X_test = [[3, 4, 5]]

# inference
predicted_solution = knn_model.predict(X_test)

Result evaluation and feedback: Evaluate inference results and obtain feedback to improve the system as needed.

CBR libraries are mainly built in Java, and an environment running on a JVM such as Clojure is advantageous to use them.

Implementation using jCOLIBRI and Clojure for case-based reasoning

<About jCOLIBRI>

jCOLIBRI (Java Case-Based Reasoning Library) is a Java-based library used for developing and researching CBR applications. jCOLIBRI provides various functions and algorithms related to case-based reasoning, including Case-based reasoning: jCOLIBRI provides a variety of functions and algorithms related to case-based reasoning, including

Case base creation and management: jCOLIBRI provides the ability to create case bases and add, edit, and delete cases. The case base is an important component for retaining past problem-solving results and domain knowledge.
Query and Inference: jCOLIBRI provides the ability for users to create queries to define new problems. The query specifies attributes and constraints and sets criteria for retrieving similar cases, while inference is the process of retrieving similar cases and applying their solutions to the new problem.
Similarity computation: jCOLIBRI offers a variety of methods for computing similarity between cases. Similarity calculations are used to evaluate the agreement or closeness of case features and to search for the most similar cases.
Retrievers and Retainers: jCOLIBRI provides two main components used to retrieve and select cases: retrievers and retainers. The Retriever searches for similar cases in the case base, while the Retainer is responsible for selecting the most appropriate case from the retrieved cases.
Visualization and Evaluation: jCOLIBRI includes case base and inference result visualization tools to assist in understanding and evaluating the case base and inference process.

Clojure, described in “Clojure and Functional Programming” is a LISP that runs on the JVM and allows direct use of Java code while also taking advantage of functional programming. Next, we describe the implementation using Clojure.

<Implementation in Clojure>

The following is an example implementation of case-based inference combining jCOLIBRI and Clojure.

Setup of jCOLIBRI: Download and setup the jCOLIBRI library. See the jCOLIBRI documentation for detailed setup procedures.
Clojure project setup: add jCOLIBRI to the project dependencies, using Clojure’s project management tools (Leiningen or deps.edn).
Use jCOLIBRI’s API: Use jCOLIBRI’s API from Clojure to perform case-based reasoning. The following is a simple example.

;; Import jCOLIBRI classes
(import '[jcolibri.cbrcore CaseBase]) ; case based
(import '[jcolibri.method.retrieve NNScoringMethod]) ; nearest neighbor algorithm

;; Creating a case base
(def case-base (CaseBase.))

;; Add a case
(.addCase case-base (create-case ...)) ; Creating and Adding Cases

;; Nearest neighbor law setting
(def nn-scoring (NNScoringMethod. case-base))
(.setWeight nn-scoring ...) ; Setting Weights

;; inference
(def solution (.compute nn-scoring (create-query ...))) ; Query creation and inference

;; Obtaining a Solution
(def predicted-solution (.getCases solution)) ; Obtain solutions from inference results

In the above example, the jCOLIBRI class is imported into Clojure and used. The above example includes the general steps of creating a case base, adding cases, setting up the nearest neighbor method, and performing inference.

Implementation using CBR-Works and Clojure for case-based reasoning

<CBR-Works>

CBR-Works is a framework for Case-Based Reasoning (CBR) and is implemented in Java.

Case base creation and management: CBR-Works allows users to create case bases and add, edit, and delete cases. The case base is an important element for retaining past problem-solving results and domain knowledge.
Query and Inference: CBR-Works provides the ability for users to create queries to define new problems. Queries specify attributes and constraints and set criteria for searching similar cases, while inference is the process of searching for similar cases and applying their solutions to the new problem.
Similarity Calculation: CBR-Works offers a variety of methods for calculating similarity between cases. Similarity calculations are used to evaluate the agreement or closeness of case characteristics and to search for the most similar cases.
Retrievers and Retainers: CBR-Works provides two main components used to search and select cases: retrievers and retainers. Retrievers search for similar cases in the case base, while retainers are responsible for selecting the most appropriate case from the retrieved cases.
Visualization and Evaluation: CBR-Works includes case base and inference result visualization tools to assist in understanding and evaluating the case base and inference process.

CBR-Works is a powerful tool for the effective use of case bases and automation of problem solving in case-based reasoning research and practice, and the documentation and sample code in CBR-Works will be a useful reference for developing and studying CBR applications.

<Implementation in Clojure>

When CBR-Works is used in combination with Clojure, it is possible to call CBR-Works from Clojure according to the Java integration method. The following describes the procedure for implementing case-based reasoning by combining CBR-Works and Clojure.

Setup of CBR-Works: Download and setup the CBR-Works framework. For detailed setup instructions, please refer to the CBR-Works documentation.
Clojure project setup: add CBR-Works to your project dependencies using Clojure’s project management tools (Leiningen or deps.edn).
Java and Clojure integration: follow the Java and Clojure integration method to be able to call CBR-Works Java classes and methods from Clojure; import CBR-Works classes using Clojure’s import and create and manipulate Java objects to be created and manipulated.

;; Import CBR-Works classes
(import '[cbrworks.CBREngine CBRCaseBase CBRQuery CBRResult])
(import '[cbrworks.core.Attribute AttributeType])

;; Creation of CBR engine
(def engine (CBREngine.))

;; Creating a case base
(def case-base (CBRCaseBase.))
(.setCaseBase engine case-base)

;; Attribute Definitions
(def attribute (Attribute. "attributeName" AttributeType/NUMERIC))
(.addAttribute engine attribute)

;; Add a case
(.addCase case-base (create-case ...)) ; ケースの作成と追加

;; Creating Queries
(def query (CBRQuery.))
(.addQueryAttribute query attribute (.valueOf 10))

;; inference
(def result (.retrieveCases engine query))
(def predicted-solution (.getSolution (.getCase result 0)))

The above example includes the general steps of importing CBR-Works classes into Clojure, creating a CBR engine, creating a case base, defining attributes, adding cases, creating queries, and retrieving inference results.

Implementation using MyCBR and Clojure for case-based reasoning

<About MyCBR>

MyCBR (My Case-Based Reasoning) is a framework for Case-Based Reasoning (CBR), implemented in Java, which provides functions for creating and managing case bases, searching and reasoning about cases, and calculating similarity. It is implemented in Java and provides functions for creating and managing case bases, searching and reasoning about cases, and calculating similarity. MyCBR also provides GUI tools that allow users to intuitively manipulate case bases and queries.

The main features of MyCBR are as follows

Case base creation and management: MyCBR provides the ability to create case bases and add, edit, and delete cases. The case base is an important element for retaining past problem-solving results and domain knowledge.
Query and Inference: MyCBR provides the ability for users to create queries to define new problems. The query specifies attributes and constraints and sets criteria for searching similar cases, while inference is the process of searching for similar cases and applying their solutions to the new problem.
Similarity Calculation: MyCBR offers a variety of methods for calculating case similarity. The similarity calculations are used to evaluate the agreement or closeness of features between cases and to search for the most similar cases.
GUI Tools: MyCBR comes with intuitive GUI tools that allow users to create and manage case bases, set up queries, and view inference results.

<Implementation in Clojure>

When MyCBR is used in combination with Clojure, MyCBR is called from Clojure according to the Java integration method. The following is a general procedure for implementing case-based reasoning using MyCBR in combination with Clojure.

MyCBR setup: Download and setup the MyCBR tool. See the MyCBR documentation for detailed setup instructions.
Clojure project setup: add MyCBR to the project dependencies using Clojure’s project management tools (Leiningen or deps.edn).
Java and Clojure integration: Java classes and methods of MyCBR can be called from Clojure according to the Java and Clojure integration method; import MyCBR classes using Clojure’s import, generate Java objects and Manipulation.

;; Import MyCBR classes
(import '[org.mycbr.core CaseBase CBRQuery CBRResult CBRCase])
(import '[org.mycbr.core.similarity.ISimilarity])

;; Creating a case base
(def case-base (CaseBase.))
(.loadCSV case-base "path/to/casebase.csv") ; Read case base from CSV file

;; Creating Queries
(def query (CBRQuery.))
(.setDescription query "query description")

;; Query Attribute Settings
(.setAttForDesc query "attributeName" "attributeValue")

;; Query Execution
(def result (.startReasoning case-base query))
(def predicted-solution (.getSolution (.getCases result)))

In the above example, the classes in MyCBR are imported into Clojure, and the general steps of creating a case base, reading the case base from a CSV file, creating a query and setting its attributes, and retrieving inference results are performed.

reference book (work)

“Case-Based Reasoning: A Textbook“、

“Applying Case-Based Reasoning: Techniques for Enterprise Systems“、

“Successful Case-based Reasoning Applications“等がある。