A Review of the Development and Future Challenges of Case-Based Reasoning

Abstract

Case-based reasoning (CBR), which is based on the cognitive assumption that similar problems have similar solutions, is an important problem-solving and learning method in the field of artificial intelligence (AI). In this article, the development of CBR is reviewed, and the major challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework and concepts of CBR are introduced. Then, the developed technology and innovative work that were designed to solve problems by CBR are summarized. Then, the application fields of CBR are summarized. Finally, according to the idea of deep learning and interpretable AI, the main challenges for the future development of CBR are proposed.

Full text

Citation: Yan, A.; Cheng, Z. A Review
of the Development and Future
Challenges of Case-Based Reasoning.
Appl. Sci. 2024,14, 7130. https://
doi.org/10.3390/app14167130
Academic Editor: Tobias Meisen
Received: 21 May 2024
Revised: 31 July 2024
Accepted: 12 August 2024
Published: 14 August 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
applied
sciences
Review
A Review of the Development and Future Challenges of
Case-Based Reasoning
Aijun Yan 1,2,3,* and Zijun Cheng 1,2
1School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China;
chengzijun@emails.bjut.edu.cn
2Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China
3Beijing Laboratory for Urban Mass Transit, Beijing 100124, China
*Correspondence: yanaijun@bjut.edu.cn
Abstract: Case-based reasoning (CBR), which is based on the cognitive assumption that similar
problems have similar solutions, is an important problem-solving and learning method in the field
of artificial intelligence (AI). In this article, the development of CBR is reviewed, and the major
challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework
and concepts of CBR are introduced. Then, the developed technology and innovative work that
were designed to solve problems by CBR are summarized. Then, the application fields of CBR are
summarized. Finally, according to the idea of deep learning and interpretable AI, the main challenges
for the future development of CBR are proposed.
Keywords: case-based reasoning; problem solving; similarity measure; case retrieval; case adaptation;
case base maintenance; deep learning
1. Introduction
Case-based reasoning (CBR), which belongs to a branch of knowledge-based systems,
is an important research direction in the field of artificial intelligence (AI) [
1
]. It is a
new reasoning method developed along with the research of cognitive science. CBR
simulates the cognitive process of human beings. The core idea is to use the solutions of
similar problems from past cases to reason and solve new problems based on a cognitive
hypothesis: similar problems have similar solutions [
2
]. Since CBR was proposed, it has
gradually formed a developed reasoning model framework and has become an effective
and practical AI technology that is widely used in industrial control [
3
], emergency decision
making [
4
], planning and design [
5
], medical diagnosis [
6
], and other fields. We select
the EI database to explore the growth trend of CBR literature, which is the most extensive
and complete engineering literature database in the world, covering many fields and
subjects. Search for ‘case-based reasoning (or case based reasoning)’ in the option column
of ‘Subject/Title/Abstract’, and count the literature number related to CBR every 5 years.
The changing trend from 1992 to 2020 is shown in Figure 1. It can be seen that the scale and
quantity of literature on CBR research and application are gradually increasing.
CBR first appeared in the description of ‘Dynamic Memory’ by Schank and Abelson [
7
]
of Yale University, which laid a theoretical foundation for the generation of CBR. Then,
Riesbeck and Schank [
8
] described the classic definition of CBR: CBR solves problems by
using or adjusting solutions to old problems. It is a problem-solving paradigm that is
fundamentally different from other major AI approaches [
9
]. Instead of relying solely on
the general knowledge of a problem domain or making associations based on generalized
relationships between problem descriptors and conclusions, CBR can use the specific
knowledge of specific problem situations (cases) experienced in the past to solve similar,
new problems. A second important difference is that CBR is an incremental, continuous
learning method that retains new experiences in solving each problem and then applies it to
Appl. Sci. 2024,14, 7130. https://doi.org/10.3390/app14167130 https://www.mdpi.com/journal/applsci

Appl. Sci. 2024,14, 7130 2 of 22
solve new problems in the future. Aamodt and Plaza [
9
] summarized the reasoning process
of CBR into the following four steps: Retrieve, Re-use, Revise, and Retain; this process is
called the ‘4R’ cycle, which provides a basic reasoning framework for CBR research. In 1999,
Watson [
10
] proposed that CBR is a methodology rather than a technology, which means
that CBR can use various technologies to achieve the solution to a problem, interpretation,
and learning process. Thus, CBR can continue to develop as researchers are faced with
the challenge of applying it to various technologies. After Watson, Finnie and Sun [
11
]
integrated the case representation into ‘4R’ and proposed the ‘5R’ model of CBR, which
improved the reasoning framework of CBR.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 2 of 23
learning method that retains new experiences in solving each problem and then applies it
to solve new problems in the future. Aamodt and Plaza [9] summarized the reasoning
process of CBR into the following four steps: Retrieve, Re-use, Revise, and Retain; this
process is called the ‘4R’ cycle, which provides a basic reasoning framework for CBR re-
search. In 1999, Watson [10] proposed that CBR is a methodology rather than a technology,
which means that CBR can use various technologies to achieve the solution to a problem,
interpretation, and learning process. Thus, CBR can continue to develop as researchers are
faced with the challenge of applying it to various technologies. After Watson, Finnie and
Sun [11] integrated the case representation into ‘4R’ and proposed the ‘5R’ model of CBR,
which improved the reasoning framework of CBR.
Figure 1. The changing trend of quantity of literature around CBR.
As a mature research field, CBR has many specific methods in each step, wide appli-
cation fields, and broad development prospects; however, this also makes it difficult to
have a comprehensive understanding of this field. Therefore, this paper provides a de-
tailed analysis and summary of various aspects of CBR. First, it classifies and summarizes
the concepts, frameworks, and key technologies of each step of CBR, providing a compar-
ison of the advantages and disadvantages of CBR methods, and summarizing the research
direction of algorithm development. Second, it summarizes the specific applications of
CBR in different fields and provides relevant literature, showing the current application
status of CBR in each field. Finally, according to the research status of CBR and the current
development frontier of AI technology, we propose the two possible development direc-
tions of CBR, looking forward to the future research trends of CBR. It is hoped that this
paper can provide useful help for beginners, practitioners, and researchers in this field.
The rest of this paper is organized as follows: in Section 2, the basic framework and
basic concepts of CBR are introduced; in Section 3, the development of key technologies
of CBR is reviewed; in Section 4, the application fields of CBR are discussed; finally, in
Section 5, several challenges regarding the future development of CBR are presented.
2. Basic Framework and Concept of CBR
The CBR framework [9] is shown in Figure 2. The main functions of each step are as
follows:
Figure 1. The changing trend of quantity of literature around CBR.
As a mature research field, CBR has many specific methods in each step, wide applica-
tion fields, and broad development prospects; however, this also makes it difficult to have a
comprehensive understanding of this field. Therefore, this paper provides a detailed analy-
sis and summary of various aspects of CBR. First, it classifies and summarizes the concepts,
frameworks, and key technologies of each step of CBR, providing a comparison of the
advantages and disadvantages of CBR methods, and summarizing the research direction of
algorithm development. Second, it summarizes the specific applications of CBR in different
fields and provides relevant literature, showing the current application status of CBR in
each field. Finally, according to the research status of CBR and the current development
frontier of AI technology, we propose the two possible development directions of CBR,
looking forward to the future research trends of CBR. It is hoped that this paper can provide
useful help for beginners, practitioners, and researchers in this field.
The rest of this paper is organized as follows: in Section 2, the basic framework and
basic concepts of CBR are introduced; in Section 3, the development of key technologies
of CBR is reviewed; in Section 4, the application fields of CBR are discussed; finally, in
Section 5, several challenges regarding the future development of CBR are presented.
2. Basic Framework and Concept of CBR
The CBR framework [
9
] is shown in Figure 2. The main functions of each step are
as follows:
(1)
Case retrieval: One or more source cases most similar to the new case are retrieved
from the case base.
(2)
Case re-use: Information and knowledge from similar cases are re-used to establish
solutions adapted to new case.
(3)
Case revision: The proposed solution is evaluated, and the solution is adjusted if it
does not meet the requirements.

Appl. Sci. 2024,14, 7130 3 of 22
(4)
Case retention: The parts of this experience that may be useful for solving problems
in the future are retained.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 3 of 23
Case base
Proble m
(New case)
Similar
cases
Soluti ons t ha t a dap t
to new case
Repaired
new case
Retrie ve
Reuse
Revise
Retain
Figure 2. CBR cyclic structure.
(1) Case retrieval: One or more source cases most similar to the new case are retrieved
from the case base.
(2) Case re-use: Information and knowledge from similar cases are re-used to establish
solutions adapted to new case.
(3) Case revision: The proposed solution is evaluated, and the solution is adjusted if it
does not meet the requirements.
(4) Case retention: The parts of this experience that may be useful for solving problems
in the future are retained.
From the problem-solving process of CBR, it can be seen that the initial description
of the problem will be defined as a new case. Then, the similarity measurement between
the new case and the source case in the case base is carried out to achieve case retrieval.
Therefore, the case is the basis of CBR, and the representation form of the case plays an
important role in CBR. The solutions for similar cases are then directly and appropriately
adapted as solutions for new cases through the retrieval and re-use steps, and new solu-
tions are evaluated to determine whether revision is needed. Finally, whether a new case
and solution need to be retained for the solution of subsequent problems is considered.
Several key links affecting the reasoning performance are case representation, case re-
trieval, case re-use, case retention, and case base maintenance.
It can be seen from the recent collected papers from the International Conference on
Case-Based Reasoning (ICCBR) that articles on similarity measurement, retrieval, adapta-
tion, and other methods have increased, which indicates that research on CBR key tech-
nologies is still very important to the CBR community. According to the basic concept of
CBR and the principle of problem solving, we retrieve some keywords in the theme, title
and abstract of the literature based on CBR in the EI database, which includes ‘case repre-
sentation’, ‘similarity’, ‘case retrieval’ or ‘retrieve’, ‘case adaptation’ and ‘case revision’,
and ‘case base maintenance’ or ‘case maintenance’. Literature retrieval was carried out
from these dimensions. The number of relevant literatures is shown in Figure 3. Figure 3
shows that case representation, similarity measurement, case retrieval, case adaptation,
and case base maintenance have received sufficient aention. The following will review
and summarize these dimensions.
Figure 2. CBR cyclic structure.
From the problem-solving process of CBR, it can be seen that the initial description
of the problem will be defined as a new case. Then, the similarity measurement between
the new case and the source case in the case base is carried out to achieve case retrieval.
Therefore, the case is the basis of CBR, and the representation form of the case plays an
important role in CBR. The solutions for similar cases are then directly and appropriately
adapted as solutions for new cases through the retrieval and re-use steps, and new solutions
are evaluated to determine whether revision is needed. Finally, whether a new case and
solution need to be retained for the solution of subsequent problems is considered. Several
key links affecting the reasoning performance are case representation, case retrieval, case
re-use, case retention, and case base maintenance.
It can be seen from the recent collected papers from the International Conference on
Case-Based Reasoning (ICCBR) that articles on similarity measurement, retrieval, adap-
tation, and other methods have increased, which indicates that research on CBR key
technologies is still very important to the CBR community. According to the basic concept
of CBR and the principle of problem solving, we retrieve some keywords in the theme, title
and abstract of the literature based on CBR in the EI database, which includes ‘case rep-
resentation’, ‘similarity’, ‘case retrieval’ or ‘retrieve’, ‘case adaptation’ and ‘case revision’,
and ‘case base maintenance’ or ‘case maintenance’. Literature retrieval was carried out
from these dimensions. The number of relevant literatures is shown in Figure 3. Figure 3
shows that case representation, similarity measurement, case retrieval, case adaptation,
and case base maintenance have received sufficient attention. The following will review
and summarize these dimensions.

Appl. Sci. 2024,14, 7130 4 of 22
Appl. Sci. 2024, 14, x FOR PEER REVIEW 4 of 23
Figure 3. The quantity of CBR key technology research literature from 1992 to 2022. In the EI data-
base, keywords are searched in the subject, title, and abstract based on CBR literature: case repre-
sentation, similarity, retrieval, case adaptation, case revision, and case base maintenance or case
maintenance.
3. Development of CBR Key Technologies
Research on the key technologies of CBR promotes the rapid development of CBR.
The development of these key technologies is inseparable from the assistance of other in-
telligent algorithms, as shown in Figure 4. In this section, the research status and devel-
opment trends of key CBR technologies, including case representation, similarity meas-
urement, case retrieval, case adaptation, and case base maintenance, are discussed.
Case-Based
Reasoning
Machine Learning
Deep Learning
Knowledge
Representation
Information
Retrieval
Data Mining
Databases
Reinforcement
Learning
SVM/SVR
Intelligent Optimization
(Heuristic) Algorithm
Figure 4. CBR and other AI technologies.
3.1. Case Representation
The case is a knowledge representation of experience [12], including the content of
past lessons learned and the context in which these lessons can be used. Bergmann et al.
[13] mentioned the case as a contextualized piece of knowledge representing an experi-
ence that teaches a lesson fundamental to achieving the goals of the reasoner. The case
representation focuses on what is stored in the case base and how to build the case to
describe its contents [9]. Generally, it can be expressed as a binary group:
{,}case P S= (1)
0
2000
4000
6000
8000
representation similarity retrieval adaptation revise maintenance
Figure 3. The quantity of CBR key technology research literature from 1992 to 2022. In the EI database,
keywords are searched in the subject, title, and abstract based on CBR literature: case representation,
similarity, retrieval, case adaptation, case revision, and case base maintenance or case maintenance.
3. Development of CBR Key Technologies
Research on the key technologies of CBR promotes the rapid development of CBR. The
development of these key technologies is inseparable from the assistance of other intelligent
algorithms, as shown in Figure 4. In this section, the research status and development
trends of key CBR technologies, including case representation, similarity measurement,
case retrieval, case adaptation, and case base maintenance, are discussed.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 4 of 23
Figure 3. The quantity of CBR key technology research literature from 1992 to 2022. In the EI data-
base, keywords are searched in the subject, title, and abstract based on CBR literature: case repre-
sentation, similarity, retrieval, case adaptation, case revision, and case base maintenance or case
maintenance.
3. Development of CBR Key Technologies
Research on the key technologies of CBR promotes the rapid development of CBR.
The development of these key technologies is inseparable from the assistance of other in-
telligent algorithms, as shown in Figure 4. In this section, the research status and devel-
opment trends of key CBR technologies, including case representation, similarity meas-
urement, case retrieval, case adaptation, and case base maintenance, are discussed.
Case-Based
Reasoning
Machine Learning
Deep Learning
Knowledge
Representation
Information
Retrieval
Data Mining
Databases
Reinforcement
Learning
SVM/SVR
Intelligent Optimization
(Heuristic) Algorithm
Figure 4. CBR and other AI technologies.
3.1. Case Representation
The case is a knowledge representation of experience [12], including the content of
past lessons learned and the context in which these lessons can be used. Bergmann et al.
[13] mentioned the case as a contextualized piece of knowledge representing an experi-
ence that teaches a lesson fundamental to achieving the goals of the reasoner. The case
representation focuses on what is stored in the case base and how to build the case to
describe its contents [9]. Generally, it can be expressed as a binary group:
{,}case P S= (1)
0
2000
4000
6000
8000
representation similarity retrieval adaptation revise maintenance
Figure 4. CBR and other AI technologies.
3.1. Case Representation
The case is a knowledge representation of experience [
12
], including the content of past
lessons learned and the context in which these lessons can be used. Bergmann et al. [
13
]
mentioned the case as a contextualized piece of knowledge representing an experience that
teaches a lesson fundamental to achieving the goals of the reasoner. The case representa-
tion focuses on what is stored in the case base and how to build the case to describe its
contents [9]. Generally, it can be expressed as a binary group:
case ={P,S}(1)

Appl. Sci. 2024,14, 7130 5 of 22
where Pand Srefer to a set of features describing the problem and a set of features
describing the solution, respectively. In addition, if it is necessary to know the result of the
solution, the case is represented as a triplet [14]:
case ={P,S,O}(2)
where Orepresents a set of features describing the result.
Problem descriptions describe the relevant information about a case in the form of
attributes or features and in the form of images and sequences, etc. [
15
]. Case representation
methods mainly include feature vector [
16
], frame representation [
17
], object-oriented case
representation [
18
], predicate-based representation [
19
], semantic nets [
20
], and production
rule representation [
21
], etc. Generally, the appropriate representation method is selected
according to the different application fields. The specific description is shown in Table 1.
Table 1. Case representation methods.
Method Element Representation Limitation
Frame slot: facet: value
<Frame name>
Low reasoning efficiency;
Hard to track and control.
slot 1: facet 11value 111 , value 112,· · ·
.
.
.
.
.
.facet 1mvalue 1m1, value 1m2 ,· · ·
.
.
.
slot n: facet n1value n11, value n12,· · ·
.
.
.
facet nmvalue nm1, value nm2 ,· · ·
constraint:
constraint condition
Object-
Oriented
CLASS::=<ID, DS, MS, MI>
ID: Identifier
DS: Data Structure
MS: Method Set
MI: Message Interface
class <name>[:<Superclass>]
[<Class variable name>]
Structure
<Static structure description of an object>
Method
<Definition of an operation on an object>
Restraint
<Restricted condition>
END
Production
Rule
<production>::=
<precondition>
<conclusion>
P→Q
IF P THEN Q (CF = [0, 1]) CF: Certainty Factor
Low efficiency;
Unable to express
structured knowledge
Semantic Nets (Node1, Arc, Node2)
Semantic Relation
Appl. Sci. 2024, 14, x FOR PEER REVIEW 6 of 23
Semantic
Nets
(Node1, Arc, Node2)
Semantic Relation
A B
C
D
AKO Have
Have
eg:
AKO: A-Kind-Of
Non-rigidity;
Low reasoning efficiency;
Knowledge access complexity
Predicate-
Based
Predicate (Constant/Vari-
ate/Function)
Conjunctions
Quantifier
Predicate Formula
Cannot represent uncertain
knowledge;
Combinatorial explosion;
Low efficiency
On the one hand, based on the existing case representation method, new improve-
ment strategies have been proposed to achieve the complex representation of cases in
combination with other AI methods according to the actual application. For example, a
framework case representation method integrating multiple information enhances the
representability of the case [25]. A multi-strategy ontology mapping method can be ap-
plied to realize the semantic expression between the process knowledge graph and the
entity model [26]. An association representation between cases is added to case represen-
tation [27]. However, the problem is that improved methods are often aimed at a specific
application field, which requires domain-specific knowledge in order to build a model.
This method is complex and only applicable in a small range of cases. Second, it increases
the workload of the case representation process. For ordinary users, it is often difficult to
manipulate the model. Finally, there are many improvement methods, but explanations
of their advantages and disadvantages are lacking. Therefore, choosing different AI meth-
ods is a problem that must be considered.
On the other hand, the CBR community is actively providing new representations of
cases through deep learning (DL) technology and other forms of vectorization. For exam-
ple, a pre-trained word2vec model and a one-dimensional convolutional neural network
were used to generate appropriate case representations [28], and a deep autoencoder and
Siamese neural network (SNN) were used to generate text case representations [29]. This
type of exploration provides corresponding processing methods for complex elements
such as text and images in each case, but supervised learning based on DL has problems
such as data labeling, hyperparameter optimization, network structure, and parameter
design. Furthermore, how to express the rich structured cases of CBR in a network context
is another key issue [30]. Finally, determining whether each new case generation and
preservation need to update the network also requires relevant discussion and research.
3.2. Similarity Measure and Case Retrieval
An important step in CBR is to retrieve similar cases. Case retrieval depends on an
accurate assessment of its similarity to the target problem. This process involves the fol-
lowing two key factors: (1) Similarity measure, where the similarity between cases is an
important basis for retrieving similar cases; (2) Retrieval method, where choosing the ap-
propriate retrieval method can improve retrieval speed and accuracy.
3.2.1. Similarity Measure
In CBR, similarity is a bridge between case representation and retrieval. Aamodt and
Plaza [9] thoroughly introduced similarity evaluations, which are mainly divided into sur-
face similarity, structural similarity, and a similarity framework. This method is mostly
selected according to the case representation method; for example, feature representation
often uses surface similarity calculation, and object-oriented case representation can use
structural similarity calculation [31]. There are two mathematical methods for represent-
ing similarity: relationship description and function representation. Table 2 provides the
details regarding similarity.
AKO: A-Kind-Of
Non-rigidity;
Low reasoning efficiency;
Knowledge access
complexity
Predicate-
Based
Predicate (Con-
stant/Variate/Function)
Conjunctions
Quantifier
Predicate Formula
Cannot represent
uncertain knowledge;
Combinatorial explosion;
Low efficiency
In recent years, with the continuous expansion of the application field of CBR, the
complexity of many cases under specific problems is deepening, resulting in the diversifica-
tion and increased complexity of case representation. Examples include implicit, empirical,
and unstructured design knowledge in complex product design [
22
] as well as case text

Appl. Sci. 2024,14, 7130 6 of 22
representation in medical information [
23
]. At the same time, in the era of big data, the
massive flood of information has led to a surge in the number of cases; consequently, the
description of case information is messy, and the scale of the case base is expanding [
24
]. In
the face of this situation, these mature case representation methods are more applicable to
cases with simple structures, single representation methods, and fewer parameters, and
cannot be applied well to complex and changeable cases. Therefore, many scholars have
begun to study new methods to solve the above problems.
On the one hand, based on the existing case representation method, new improvement
strategies have been proposed to achieve the complex representation of cases in combination
with other AI methods according to the actual application. For example, a framework case
representation method integrating multiple information enhances the representability of
the case [
25
]. A multi-strategy ontology mapping method can be applied to realize the
semantic expression between the process knowledge graph and the entity model [
26
]. An
association representation between cases is added to case representation [
27
]. However,
the problem is that improved methods are often aimed at a specific application field, which
requires domain-specific knowledge in order to build a model. This method is complex
and only applicable in a small range of cases. Second, it increases the workload of the case
representation process. For ordinary users, it is often difficult to manipulate the model.
Finally, there are many improvement methods, but explanations of their advantages and
disadvantages are lacking. Therefore, choosing different AI methods is a problem that must
be considered.
On the other hand, the CBR community is actively providing new representations of
cases through deep learning (DL) technology and other forms of vectorization. For example,
a pre-trained word2vec model and a one-dimensional convolutional neural network were
used to generate appropriate case representations [
28
], and a deep autoencoder and Siamese
neural network (SNN) were used to generate text case representations [
29
]. This type of
exploration provides corresponding processing methods for complex elements such as
text and images in each case, but supervised learning based on DL has problems such
as data labeling, hyperparameter optimization, network structure, and parameter design.
Furthermore, how to express the rich structured cases of CBR in a network context is another
key issue [
30
]. Finally, determining whether each new case generation and preservation
need to update the network also requires relevant discussion and research.
3.2. Similarity Measure and Case Retrieval
An important step in CBR is to retrieve similar cases. Case retrieval depends on
an accurate assessment of its similarity to the target problem. This process involves the
following two key factors: (1) Similarity measure, where the similarity between cases is
an important basis for retrieving similar cases; (2) Retrieval method, where choosing the
appropriate retrieval method can improve retrieval speed and accuracy.
3.2.1. Similarity Measure
In CBR, similarity is a bridge between case representation and retrieval. Aamodt and
Plaza [
9
] thoroughly introduced similarity evaluations, which are mainly divided into
surface similarity, structural similarity, and a similarity framework. This method is mostly
selected according to the case representation method; for example, feature representation
often uses surface similarity calculation, and object-oriented case representation can use
structural similarity calculation [
31
]. There are two mathematical methods for representing
similarity: relationship description and function representation. Table 2provides the details
regarding similarity.

Appl. Sci. 2024,14, 7130 7 of 22
Table 2. The relation representation and function representation of similarity.
Relation Function
SI M(x,y)⇔xand yare similar SI M(x,y) = 1⇔xand yare exactly similar
DSI M(x,y)⇔xand yare dissimilar SI M(x,y) = 0⇔xand yare exactly dissimilar
R(x,y,z)⇔xis at least as similar to yas xto z0<SI M(x,y)<1⇔xand yare partly similar
The most commonly used method is to calculate the similarity between cases xand y
by a distance metric function (taking Euclidean distance as an example):
SI M(x,y) = 1−D IST(x,y) = 1−r∑
i
w2
idist2(xi,yi)(3)
where x
i
and y
i
represent the same feature of the two cases, w
i
is the corresponding weight,
and SI M(x,y)∈[0, 1].
With the continuous expansion of CBR applications, there have been difficulties in
obtaining similarity measurements based on complex or mixed data such as time series,
images (or graph structure), or text in complex CBR tasks. However, since the metric
function calculation is mainly applicable to the case of numerical attribute descriptions, it
cannot be directly applied to cases containing multiple complex representations; therefore,
improving retrieval performance by developing more effective similarity measurement
methods has become the focus of research in the field of CBR, such as [
32
–
34
]. At present,
many scholars have improved the measurement algorithm in the following three directions:
(1)
An improved method based on a hybrid similarity measure [
32
,
35
–
37
] mainly im-
proves the calculation accuracy of similarity by processing attribute features, such as
adding other information and setting multiple attribute value formats. This hybrid
measurement method solves the similarity measurement problem of multi-attribute
representation cases, but it often needs to combine the relevant knowledge of specific
applications, which is a highly professional task and computationally complex.
(2)
Based on the weighted similarity measure of feature weight optimization [
38
–
42
], the
measurement calculation is improved through the reasonable distribution of feature
weight (i.e.,
wi
in Formula (3)). The emphasis is on the selection, optimization, and im-
provement of the weight distribution method. This kind of method has been studied
for the longest amount of time, and the achievements are more fruitful. It combines
information entropy, genetic algorithm, neural network, and other optimization algo-
rithms. However, due to the different evaluation criteria in each article, how to choose
the appropriate optimization algorithm in practical applications is a difficult problem.
(3) Based on the (deep) metric learning algorithm [
33
,
43
,
44
], the learning of the similarity
measure is achieved by training a (deep) neural network. This method has a shorter
research time in CBR compared with that of other case similarity measurement algo-
rithms. Its advantage is that it realizes similarity calculations in the form of a neural
network, solves the nonlinear problem, and reduces the computational complexity of
the similarity measurement process. In dealing with cases represented by text and
images, etc., the network structure has better representation advantages in case data
processing; however, problems such as the neural network design caused by this
method also need to be considered and studied.
In addition, there are some other intelligent methods combined with similarity mea-
surement methods to improve the retrieval performance [
45
–
48
]. However, due to the
large number of AI methods, this article only lists several articles that the author believes
have been more valuable in recent years for reference. Verma et al. [
45
] proposed a data-
driven method to model the local similarity measure of numerical and class attributes.
Lenz et al. [
46
] studied an ontology-based semantic similarity measure in the application of
argumentation schemes. Zeyen et al. [
47
] focused on the similarity measures of semantic
label graphs and proposed a combination of A* search and knowledge-intensive local simi-

Appl. Sci. 2024,14, 7130 8 of 22
larity measures, which not only outputs the similarity but also provides the corresponding
mapping that can be used for interpretation and adjustment.
3.2.2. Case Retrieval
As an important step of case-based reasoning, obtaining the correct case retrieval
results determines the overall performance of CBR systems. Effective retrieval means not
only finding similar cases but also finding useful similar cases. To date, many mature
retrieval methods have been formed, including the nearest neighbor algorithm (NN) [
49
],
knowledge-guided approach [
50
], template retrieval [
51
], two-level retrieval [
52
], and
index-based retrieval [
53
], etc. The efficiency of these retrieval methods depends to a large
extent on the following:
•The representation of the objects.
•The case base structure.
•The similarity measure.
•The accuracy of the intended answer or solution.
In case retrieval, similarity-based retrieval methods [
35
,
36
,
54
] are the most interesting
to scholars and have been widely used. The classic representative is NN (KNN), which is the
most commonly used method in case retrieval because its principle is easy to understand
and simple to calculate. However, for complex large-scale data, the simplicity of the
method also limits its retrieval in complex cases; therefore, NN is often combined with other
algorithms to improve retrieval accuracy and efficiency [
55
–
57
]. Although purely similarity-
based retrieval is still the most widely used technology, the limitations of similarity are
gradually exposed with the development of CBR; for example, the results obtained by
retrieval are singular and cannot provide users with more novel and valuable references.
There may be no similar cases or only a few cases with similar features in the case base;
this leads to a no-retrieval result. Alternatively, the retrieval results cannot provide support
for subsequent re-use stages; therefore, while similarity continues to play a role in retrieval,
it also gradually combines with other standards to guide the retrieval process.
(1) Retrieval based on diversity is often used in recommendation systems to provide users
with more diversified solutions to avoid similar recommended single and limited
cases [58,59].
(2)
Retrieval oriented to adaptation guidance [
60
] is based on the assumption that the
most similar situation may not be adapted and can perform preliminary adaptation
work during the retrieval process, which can significantly reduce adaptation failure
and adaptation costs [61,62].
(3)
Interpretation-oriented retrieval, which explains CBR and justifies recommendations
or solutions, is often important, especially in the field of medical decision making,
in which explaining the cause and correctness of the search results can provide
compelling support [63–65].
In addition, selecting different retrieval methods according to case representation
is also a key research direction for many scholars; for example, text language retrieval
combined with natural language processing (NLP) technology [
66
,
67
], retrieval based on
non-symbolic types such as images [
68
], or case retrieval with missing case information [
69
].
These methods mainly aim at the analysis of the features of the case data itself, combining
feature selection and measurement algorithms to design the case retrieval method.
3.3. Case Adaptation
The re-use phase needs to use the experience and knowledge of old cases in new
situations and obtain the solution to the new problem by adapting the retrieval results.
Figure 5shows the general re-use principle of a selected case. The general process of this
phase is shown in Figure 5. If the new problem is the same as the old case retrieved, then
the re-use phase only needs to copy the old case solution; however, in reality, the situation
with new problems is often not exactly the same as that of old cases, and the retrieved

Appl. Sci. 2024,14, 7130 9 of 22
solution can only be regarded as an initial reference solution. Any difference between the
new problem and the retrieved case may require appropriate adaptation of the solution to
adapt to the new problem. Especially when CBR is used for constructive problems, such
as design tasks, planning tasks, and decision-making tasks, adaptation is often essential.
Because such tasks have difficulty finding every solution in the case base, the adaptation
process is essential.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 9 of 23
phase is shown in Figure 5. If the new problem is the same as the old case retrieved, then
the re-use phase only needs to copy the old case solution; however, in reality, the situation
with new problems is often not exactly the same as that of old cases, and the retrieved
solution can only be regarded as an initial reference solution. Any difference between the
new problem and the retrieved case may require appropriate adaptation of the solution
to adapt to the new problem. Especially when CBR is used for constructive problems, such
as design tasks, planning tasks, and decision-making tasks, adaptation is often essential.
Because such tasks have difficulty finding every solution in the case base, the adaptation
process is essential.
New problem p
New solution s
Retrieval
Adaptation Old solution s
i
Old Problem p
i
Figure 5. Re-use principle.
The complexity of the adaptation method is reflected in two dimensions: what is
changed in the retrieved solution and how the change is achieved [15]. The basic adapta-
tion type is shown in Figure 6, of which the following three types truly need adaptation
operations:
 Substitutions: They replace some part of the retrieved solution by another or by sev-
eral others.
 Structural transformations: They alter the structure of the solution and re-organize
the solution by adding, deleting, or replacing parts of the proposed solution [70].
 Generative adaptations: They replay the method of deriving the retrieved solution
on the new problem. This is the most complex form of adaptation.
Start
Null
Adaptation
Transformation
based adaptation
Generative
adaptation
Substitutional Structual
Figure 6. Basic adaptation types.
The most important part of the adaptation process is the learning of adaptation
knowledge, which is also a classic problem in CBR. In the early stage, CBR invested much
energy in developing case adaptation knowledge, but the difficulty of knowledge gener-
ation seriously hindered the development of CBR. It can also be seen from Figure 3 that
there is lile previous literature on adaptation. Later in the research process, the potential
of learning methods to acquire case adaptation knowledge was gradually recognized, in-
cluding generating rules through decision tree learning [71,72] and support vector regres-
sion (SVR) [51,73], and by applying CBR to the adaptation process itself [74,75]. The case-
difference heuristic (CDH) first proposed by Hanney and Keane [76] has become one of
the most commonly used methods for learning adaptation knowledge. Equation (4) de-
scribes the difference between case x and case y:
Figure 5. Re-use principle.
The complexity of the adaptation method is reflected in two dimensions: what is changed
in the retrieved solution and how the change is achieved [
15
]. The basic adaptation type is
shown in Figure 6, of which the following three types truly need adaptation operations:
•
Substitutions: They replace some part of the retrieved solution by another or by
several others.
•
Structural transformations: They alter the structure of the solution and re-organize the
solution by adding, deleting, or replacing parts of the proposed solution [70].
•
Generative adaptations: They replay the method of deriving the retrieved solution on
the new problem. This is the most complex form of adaptation.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 9 of 23
phase is shown in Figure 5. If the new problem is the same as the old case retrieved, then
the re-use phase only needs to copy the old case solution; however, in reality, the situation
with new problems is often not exactly the same as that of old cases, and the retrieved
solution can only be regarded as an initial reference solution. Any difference between the
new problem and the retrieved case may require appropriate adaptation of the solution
to adapt to the new problem. Especially when CBR is used for constructive problems, such
as design tasks, planning tasks, and decision-making tasks, adaptation is often essential.
Because such tasks have difficulty finding every solution in the case base, the adaptation
process is essential.
New problem p
New solution s
Retrieval
Adaptation Old solution s
i
Old Problem p
i
Figure 5. Re-use principle.
The complexity of the adaptation method is reflected in two dimensions: what is
changed in the retrieved solution and how the change is achieved [15]. The basic adapta-
tion type is shown in Figure 6, of which the following three types truly need adaptation
operations:
 Substitutions: They replace some part of the retrieved solution by another or by sev-
eral others.
 Structural transformations: They alter the structure of the solution and re-organize
the solution by adding, deleting, or replacing parts of the proposed solution [70].
 Generative adaptations: They replay the method of deriving the retrieved solution
on the new problem. This is the most complex form of adaptation.
Start
Null
Adaptation
Transformation
based adaptation
Generative
adaptation
Substitutional Structual
Figure 6. Basic adaptation types.
The most important part of the adaptation process is the learning of adaptation
knowledge, which is also a classic problem in CBR. In the early stage, CBR invested much
energy in developing case adaptation knowledge, but the difficulty of knowledge gener-
ation seriously hindered the development of CBR. It can also be seen from Figure 3 that
there is lile previous literature on adaptation. Later in the research process, the potential
of learning methods to acquire case adaptation knowledge was gradually recognized, in-
cluding generating rules through decision tree learning [71,72] and support vector regres-
sion (SVR) [51,73], and by applying CBR to the adaptation process itself [74,75]. The case-
difference heuristic (CDH) first proposed by Hanney and Keane [76] has become one of
the most commonly used methods for learning adaptation knowledge. Equation (4) de-
scribes the difference between case x and case y:
Figure 6. Basic adaptation types.
The most important part of the adaptation process is the learning of adaptation knowl-
edge, which is also a classic problem in CBR. In the early stage, CBR invested much energy
in developing case adaptation knowledge, but the difficulty of knowledge generation
seriously hindered the development of CBR. It can also be seen from Figure 3that there
is little previous literature on adaptation. Later in the research process, the potential of
learning methods to acquire case adaptation knowledge was gradually recognized, includ-
ing generating rules through decision tree learning [
71
,
72
] and support vector regression
(SVR) [
51
,
73
], and by applying CBR to the adaptation process itself [
74
,
75
]. The case-
difference heuristic (CDH) first proposed by Hanney and Keane [
76
] has become one of the
most commonly used methods for learning adaptation knowledge. Equation (4) describes
the difference between case xand case y:
△(x,y) = (x1−y1, . . . , xk−yk,sx−sy) = (△f1, . . . , △fk,△s)(4)
where srepresents the solution for the case,
△fi
represents the i-th feature difference, and
△s
represents the scheme difference. CDH is used to attribute the difference in the solution

Appl. Sci. 2024,14, 7130 10 of 22
description to the difference in its problem description, transforming the difference into
rules, thus achieving the case adaptation process.
At present, based on the complexity of case knowledge, methods of learning and
adapting knowledge are mainly studied from the following directions:
(1)
Improvement of the CDH method [
61
,
77
–
79
]. Although the CDH adaptation method
reduces the burden of knowledge engineering, it also brings the problem of defining
the difference function. Recent studies have used implicit calculation in ML technol-
ogy to replace the traditional CDH difference calculation, but there is still a lack of
theoretical proof.
(2) Adaptation method based on knowledge and rules [
80
–
82
]. Lieber et al. [
81
] proposed
using positive examples to adapt rules, while using negative examples to filter out
some of the rules to avoid the problem of incorrect schemes in CBR systems during the
adaptation process. These methods focus on the extraction efficiency of knowledge
and rules to enhance adaptation performance.
(3) Adaptation methods based on machine learning (ML) or DL [
51
,
83
,
84
]. Long et al. [
84
]
proposed a feature re-use case adaptation (FR-CA) method based on an SVR machine,
which can automatically and intelligently solve product experience features and
achieve the integration of expert comprehensive decisions with the least expert partici-
pation. These methods simplify the compilation and extraction of adapted knowledge
through an end-to-end learning process, but the design of network structures often
affects the learning effect.
These learning-based case adaptation methods often use different evaluation criteria
to evaluate the performance of the adaptation model in different tasks. According to task
requirements such as classification and diagnosis tasks, accuracy, F1 score, and recall rate
are often used as evaluation criteria for the model. In tasks such as timing or demand
forecasting, various error formulas and R
2
are used as evaluation criteria. In addition, the
next step in case adaptation in CBR is case revise, i.e., a further evaluation of the adaptation
results; however, this step often needs to be implemented after the model is actually applied
to practice.
3.4. Case Base Maintenance (CBM)
As CBR systems were developed and deployed for real-world application scenarios,
the potential pitfalls of long-term case learning became apparent, especially in relation
to the impact of case-based growth on retrieval costs [
15
]. In the process of case studies,
cases are continuously saved to the case base (CB). On the one hand, the increase in the
number of cases helps to improve the problem-solving ability of CBR systems. However,
with the increase in the CB scale, redundant, repetitive, or even wrong cases increase the
time complexity of the systems and reduce the overall performance of the CBR system.
Especially in the current era of information flooding, many case bases are already very
large premises. Case base maintenance (CBM) has also begun to become more important,
and related research is gradually increasing.
CBM ensures the performance of CBR systems by deleting, adding, updating, and
modifying cases and related data in the case base. Leake and Wilson [
85
] defined CBM
as the method used in CBR to operate and organize the content of its case base in order
to improve or maintain the ability of CBR technology. We summarize the general CBM
maintenance process in Figure 7. The basic requirement of CBM is that CBR systems need
to maintain the existing case base without affecting its problem-solving capability [
86
].
Smiti et al. [
87
] first proposed dividing CBM into the following two strategies: case base
division and case base optimization. In recent years, Nakhjiri et al. [
88
] proposed dividing
CBM into the following steps:
(1)
Direct models [
89
–
91
]: They do not consider the relationship between cases and do
not otherwise retrieve information from individual cases.
(2)
Hybrid model [
87
,
92
]: They describe an association between cases by integrating
different AI methods.

Appl. Sci. 2024,14, 7130 11 of 22
(3)
Case property model [
93
,
94
]: They integrate additional information into the case
to better illustrate the features of the case in the case base to achieve the mainte-
nance function.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 11 of 23
division and case base optimization. In recent years, Nakhjiri et al. [88] proposed dividing
CBM into the following steps:
Figure 7. CBM maintenance process.
(1) Direct models [89–91]: They do not consider the relationship between cases and do
not otherwise retrieve information from individual cases.
(2) Hybrid model [87,92]: They describe an association between cases by integrating dif-
ferent AI methods.
(3) Case property model [93,94]: They integrate additional information into the case to
beer illustrate the features of the case in the case base to achieve the maintenance
function.
Although the direct model is simple, the effect is relatively poor. The case aribute
model is maintained by adding additional information, but the selection and addition of
additional information are limited by the case base itself, which needs to be analyzed on
a case-by-case basis. Therefore, the hybrid model is currently the most popular research
method. For example, Nakhjiri et al. [88] proposed a CBM model based on reputation
values, calculating a case aribute called reputation for each member of the case base,
whose value reflects the capability of the relevant case. Based on this case aribute, several
removal strategies and maintenance methods are designed. Each strategy focuses on dif-
ferent aspects of case-based maintenance. Chebli et al. [95] proposed a CBM strategy based
on active semi-supervised maintenance (ASSM), using ML technology to solve the prob-
lem of scarcity of marked cases. Smiti et al. [96] provided a new alternating technique to
correctly detect noise and redundancy in cases or features. It is a method of dynamically
maintaining the case base and can maintain the vocabulary knowledge carrier of CB and
CBR systems at the same time.
4. Application Fields of CBR
Since the 1990s, CBR has been increasingly applied, including in diagnosis, decision-
making, design and so on. To summarize the practical application of CBR, based on some
keywords in the title and abstract of CBR literature, the author counted the number of
literatures on several common applications. Specifically, it includes classification, diagno-
sis or diagnose, prediction or predict, design or plan, recommendation, decision making
or decision support, and knowledge management. Among them, the literature search on
planning and design was limited to the title. The trend of the number of application doc-
uments in different fields is shown in Figure 8. CBR applications are increasing and oc-
cupy an important position in the field of AI. In this chapter, some typical applications are
mainly introduced from the following aspects: diagnosis, prediction, design and planning,
Figure 7. CBM maintenance process.
Although the direct model is simple, the effect is relatively poor. The case attribute
model is maintained by adding additional information, but the selection and addition of
additional information are limited by the case base itself, which needs to be analyzed on
a case-by-case basis. Therefore, the hybrid model is currently the most popular research
method. For example, Nakhjiri et al. [
88
] proposed a CBM model based on reputation
values, calculating a case attribute called reputation for each member of the case base,
whose value reflects the capability of the relevant case. Based on this case attribute, several
removal strategies and maintenance methods are designed. Each strategy focuses on
different aspects of case-based maintenance. Chebli et al. [
95
] proposed a CBM strategy
based on active semi-supervised maintenance (ASSM), using ML technology to solve the
problem of scarcity of marked cases. Smiti et al. [
96
] provided a new alternating technique
to correctly detect noise and redundancy in cases or features. It is a method of dynamically
maintaining the case base and can maintain the vocabulary knowledge carrier of CB and
CBR systems at the same time.
4. Application Fields of CBR
Since the 1990s, CBR has been increasingly applied, including in diagnosis, decision-
making, design and so on. To summarize the practical application of CBR, based on some
keywords in the title and abstract of CBR literature, the author counted the number of
literatures on several common applications. Specifically, it includes classification, diagnosis
or diagnose, prediction or predict, design or plan, recommendation, decision making or
decision support, and knowledge management. Among them, the literature search on
planning and design was limited to the title. The trend of the number of application
documents in different fields is shown in Figure 8. CBR applications are increasing and
occupy an important position in the field of AI. In this chapter, some typical applications
are mainly introduced from the following aspects: diagnosis, prediction, design and
planning, decision support and recommendation systems. Moreover, other applications are
briefly described.

Appl. Sci. 2024,14, 7130 12 of 22
Appl. Sci. 2024, 14, x FOR PEER REVIEW 12 of 23
decision support and recommendation systems. Moreover, other applications are briefly
described.
Figure 8. Variation trend of the number of literatures on different applications of CBR.
4.1. Diagnosis
Diagnostic tasks usually include technical fault diagnosis [97,98] in electronics, engi-
neering and other fields and medical health case diagnosis [39,99,100], which can be re-
garded as a variant of classification tasks. Many AI methods can simply diagnose prob-
lems. However, CBR is different from these methods. The main advantage of using CBR
to achieve the diagnosis task is not only to determine the diagnosis itself but also to pro-
vide a reference solution through CBR. The specific process is shown in Figure 9.
Information
collec tion
Generating
diagnosis
Suggesting the
remedy or steps
Figure 9. Diagnostic process.
With the development of technology, the function and structure of modern mechan-
ical equipment or electronic systems have become increasingly complex, which makes the
fault mechanism staggered and changeable. In addition, fault diagnoses of complex sys-
tems have become very difficult. Therefore, the fault diagnosis method based on CBR is
mostly combined with other methods to improve the diagnosis efficiency. Chen et al. [98]
developed an aero-engine fault system based on CBR. They designed a tree structure
based on a semantic graph to quantify the similarity between semantic aributes and de-
fined the relationship between fault components and fault modes to accurately match
them.
Because the decision-making of the human medical health process itself is highly de-
pendent on historical experience therapy and literature [39], medical pathology diagnosis
based on CBR has always been a popular research topic in the medical field. Benamina et
al. [99] established a fuzzy CBR application system on the JColibri platform to diagnose
diabetes. They used symbolic learning to build a fuzzy decision tree on the Fispro plat-
form, extract fuzzy rules, and import them into the JColibri platform to establish a fuzzy
case base. The case index of the diabetes surveillance plan was selected by the retrieval
method based on the fuzzy reasoning mechanism. This method not only optimizes the
time but also reduces the complexity of the similarity calculation between individuals.
4.2. Prediction
In CBR, the application of prediction is very common, and the form of the case be-
comes:
{, }case hiistory predctioin
= (5)
0
500
1000
1500
2000
2500
3000
3500
1992 1997 2002 2007 2012 2017 2022
classification
diagnosis
prediction
planing or design
recommendation
decision
knowledge management
Figure 8. Variation trend of the number of literatures on different applications of CBR.
4.1. Diagnosis
Diagnostic tasks usually include technical fault diagnosis [
97
,
98
] in electronics, en-
gineering and other fields and medical health case diagnosis [
39
,
99
,
100
], which can be
regarded as a variant of classification tasks. Many AI methods can simply diagnose prob-
lems. However, CBR is different from these methods. The main advantage of using CBR to
achieve the diagnosis task is not only to determine the diagnosis itself but also to provide a
reference solution through CBR. The specific process is shown in Figure 9.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 12 of 23
decision support and recommendation systems. Moreover, other applications are briey
described.
Figure 8. Variation trend of the number of literatures on dierent applications of CBR.
4.1. Diagnosis
Diagnostic tasks usually include technical fault diagnosis [97,98] in electronics, engi-
neering and other elds and medical health case diagnosis [39,99,100], which can be re-
garded as a variant of classication tasks. Many AI methods can simply diagnose prob-
lems. However, CBR is dierent from these methods. The main advantage of using CBR
to achieve the diagnosis task is not only to determine the diagnosis itself but also to pro-
vide a reference solution through CBR. The specic process is shown in Figure 9.
Information
collection Generating
diagnosis Suggesting the
remedy or steps
Figure 9. Diagnostic process.
With the development of technology, the function and structure of modern mechan-
ical equipment or electronic systems have become increasingly complex, which makes the
fault mechanism staggered and changeable. In addition, fault diagnoses of complex sys-
tems have become very dicult. Therefore, the fault diagnosis method based on CBR is
mostly combined with other methods to improve the diagnosis eciency. Chen et al. [98]
developed an aero-engine fault system based on CBR. They designed a tree structure
based on a semantic graph to quantify the similarity between semantic aributes and de-
ned the relationship between fault components and fault modes to accurately match
them.
Because the decision-making of the human medical health process itself is highly de-
pendent on historical experience therapy and literature [39], medical pathology diagnosis
based on CBR has always been a popular research topic in the medical eld. Benamina et
al. [99] established a fuzzy CBR application system on the JColibri platform to diagnose
diabetes. They used symbolic learning to build a fuzzy decision tree on the Fispro plat-
form, extract fuzzy rules, and import them into the JColibri platform to establish a fuzzy
case base. The case index of the diabetes surveillance plan was selected by the retrieval
method based on the fuzzy reasoning mechanism. This method not only optimizes the
time but also reduces the complexity of the similarity calculation between individuals.
4.2. Prediction
In CBR, the application of prediction is very common, and the form of the case be-
comes:
{ , }case hiistory predctioin
(5)
0
500
1000
1500
2000
2500
3000
3500
1992 1997 2002 2007 2012 2017 2022
classification
diagnosis
prediction
planing or design
recommendation
decision
knowledge management
Figure 9. Diagnostic process.
With the development of technology, the function and structure of modern mechanical
equipment or electronic systems have become increasingly complex, which makes the fault
mechanism staggered and changeable. In addition, fault diagnoses of complex systems
have become very difficult. Therefore, the fault diagnosis method based on CBR is mostly
combined with other methods to improve the diagnosis efficiency. Chen et al. [
98
] devel-
oped an aero-engine fault system based on CBR. They designed a tree structure based on
a semantic graph to quantify the similarity between semantic attributes and defined the
relationship between fault components and fault modes to accurately match them.
Because the decision-making of the human medical health process itself is highly de-
pendent on historical experience therapy and literature [39], medical pathology diagnosis
based on CBR has always been a popular research topic in the medical field. Benam-
ina et al. [
99
] established a fuzzy CBR application system on the JColibri platform to
diagnose diabetes. They used symbolic learning to build a fuzzy decision tree on the Fispro
platform, extract fuzzy rules, and import them into the JColibri platform to establish a fuzzy
case base. The case index of the diabetes surveillance plan was selected by the retrieval
method based on the fuzzy reasoning mechanism. This method not only optimizes the
time but also reduces the complexity of the similarity calculation between individuals.
4.2. Prediction
In CBR, the application of prediction is very common, and the form of the case becomes:
case ={history,predctioin}(5)
It is based on the fact that the history in the same class is similar in some sense, so
the predicted results are similar. CBR is widely used in prediction tasks. In the industrial
control process, the prediction of key parameters by CBR can improve the efficiency of
industrial production, realize the stable control operation of the working condition process,

Appl. Sci. 2024,14, 7130 13 of 22
and reduce the safety risk, such as [
101
,
102
]. In financial forecasting, the analysis and
prediction of financial data such as stocks can avoid risks and reduce the economic losses of
shareholders or companies, such as [
103
]. In disaster relief emergency, CBR can realize the
prediction of material demand with the help of previous disaster cases, which is the basic
condition of disaster relief operation and the premise of optimal allocation of emergency
resources, such as [
104
]. In healthcare, the prediction of body data by CBR can help people
improve their physical fitness and reduce the risk of illness or injury, such as [
105
]. In
agriculture, the prediction of yield by CBR can help farmers improve and optimize crop
planting and increase yield, thus promoting the economic development of agricultural
products, such as [106].
4.3. Design and Planning
Case-based design (CBD) [
107
–
109
] is a CBR-based application that develops new
design solutions by adjusting previous solutions [
30
], allowing designs to be re-used and
adapted to create new designs. For example, Grace et al. [
107
] proposed a dual-cycle CBR
model in the domain of recipe generation. The model combines the strengths of deep
learning and similarity-based retrieval. The model learns the latent structure of the domain
from the case base to generate a new object that deliberately flouts that structure and then
reintegrates the new object with known cases through adaptation to generate novel and
valuable recipes. Ke et al. [
109
] proposed an intelligent design for remanufacture (DFREM)
method based on a vector space model (VSM) and CBR, which can extract the features of
customer requirement data from massive customer requirement data and quickly generate
a design scheme meeting customer requirements.
The planning problem relies on the experience of decision-makers to propose a
reasonable planning scheme. As one of the important applications of CBR, planning
tasks [5,110–112]
, which are similar to design tasks, are not planned from scratch. For
example, Abdelwahed et al. [
112
] proposed using learning experience to solve motion
planning problems and CBR technology to provide state samples associated with the prob-
lem to achieve path building and find the shortest path. Chen et al. [
5
] proposed a CBR
framework for assembly sequence planning (ASP). By establishing the overall architecture
of CBR, the extraction, aggregation and re-use of assembly information of existing solutions
are achieved, which can analyze the collection information in heterogeneous multimedia
and re-use the existing assembly information scattered in multimedia solutions to solve
assembly sequence planning.
4.4. Decision Support
The decision support system (DSS), based on CBR, uses data, knowledge, and rea-
soning technology to assist decision-makers, which can improve the level and quality
of decision making, such as [
113
,
114
]. The decision-making process is roughly shown
in
Figure 10
. The CBR system can provide reference and help for decision-makers in
emergency responses, management, clinical situations, and other fields, with the help of
historical cases that have occurred in the past.
In emergency, CBR can design emergency-management programs or auxiliary strate-
gies to help managers make correct decisions in emergency affairs, in a timely manner, to
avoid greater losses [
4
,
113
]. In management, CBR often provides managers or decision-
makers with past management experience and helps them choose or specify the appropriate
strategic guidance through the analysis of the management system, long-term development
direction, and other aspects [
115
,
116
]. In clinical contexts, CBR provides reference infor-
mation for experts through medical cases and helps experts analyze the patient’s physical
condition to realize the expert’s determination of the patient’s treatment plan [117,118].

Appl. Sci. 2024,14, 7130 14 of 22
Appl. Sci. 2024, 14, x FOR PEER REVIEW 14 of 23
Abstract
description
Decision-making
reference scheme
Case match ing and
ranking
Generated data
Scheme fi nding and
adaptation
Decisions generate
and evaluatio ns
Int erpreta tion and
visualization
Human-computer
Interaction Interface
Decision
makers
Problem
description
Case base
DSS
Decision-makin g
reference scheme
Figure 10. Decision-making process of the CBR system.
In emergency, CBR can design emergency-management programs or auxiliary strat-
egies to help managers make correct decisions in emergency affairs, in a timely manner,
to avoid greater losses [4,113]. In management, CBR often provides managers or decision-
makers with past management experience and helps them choose or specify the appropri-
ate strategic guidance through the analysis of the management system, long-term devel-
opment direction, and other aspects [115,116]. In clinical contexts, CBR provides reference
information for experts through medical cases and helps experts analyze the patient’s
physical condition to realize the expert’s determination of the patient’s treatment plan
[117,118].
4.5. Recommendation System
E-commerce is similar to any other form of business. Users have needs, but often the
needs can only be met to a certain extent, and users can only specify the needs incom-
pletely or inaccurately. For users, it is difficult to analyze massive volumes of information
and filter out the information they need; therefore, CBR plays a key role in the develop-
ment of content-based or case-based recommendation systems, such as [119,120]. It in-
cludes the user’s personal preferences and needs, as well as product-related information.
Therefore, CBR can provide products and information that users are interested in or need
according to user needs or preferences.
Abbas et al. [58] proposed a dynamic review recommender ‘DiversityBite’ based on
the CBR framework, which combines reviews and diversity to help users obtain diversi-
fied recipes and meal plan recommendations. Dong and Smyth [121] proposed a person-
alized recommendation method using product reviews as recommendation knowledge.
It extracts cluster features from reviews to form cases through triplet features and feature
clustering steps. In the recommendation process, it calculates the recommendation score
through the linear combination of emotional features and similarity, which is used for
query-based and user-based recommendation scenarios. Li et al. [122] argued that, since
users only interact with items of interest in the recommendation system, that system must
retain very large amounts of personalized information and item sparsity, which seriously
affects the performance of the recommendation system; therefore, they proposed a CBR-
recommender method to reduce data sparsity through data classification and dynamic
clustering, which makes the system run faster in large-scale recommendation research
that dynamically calculates user preferences.
Figure 10. Decision-making process of the CBR system.
4.5. Recommendation System
E-commerce is similar to any other form of business. Users have needs, but often the
needs can only be met to a certain extent, and users can only specify the needs incompletely
or inaccurately. For users, it is difficult to analyze massive volumes of information and
filter out the information they need; therefore, CBR plays a key role in the development of
content-based or case-based recommendation systems, such as [
119
,
120
]. It includes the
user’s personal preferences and needs, as well as product-related information. Therefore,
CBR can provide products and information that users are interested in or need according
to user needs or preferences.
Abbas et al. [
58
] proposed a dynamic review recommender ‘DiversityBite’ based on
the CBR framework, which combines reviews and diversity to help users obtain diversified
recipes and meal plan recommendations. Dong and Smyth [
121
] proposed a personalized
recommendation method using product reviews as recommendation knowledge. It extracts
cluster features from reviews to form cases through triplet features and feature clustering
steps. In the recommendation process, it calculates the recommendation score through
the linear combination of emotional features and similarity, which is used for query-based
and user-based recommendation scenarios. Li et al. [
122
] argued that, since users only
interact with items of interest in the recommendation system, that system must retain very
large amounts of personalized information and item sparsity, which seriously affects the
performance of the recommendation system; therefore, they proposed a CBR-recommender
method to reduce data sparsity through data classification and dynamic clustering, which
makes the system run faster in large-scale recommendation research that dynamically
calculates user preferences.
4.6. Other Applications
In addition to the above applications, CBR has also been applied in other fields, such
as knowledge management (KM), and text and image processing.
KM is usually implemented through the knowledge flow of tasks such as creating,
distributing, and re-using knowledge. The KM application program based on CBR is
achieved by the knowledge base, which turns the knowledge base into a case base and
achieves knowledge management with the CBR cycle process [55,123].

Appl. Sci. 2024,14, 7130 15 of 22
When the knowledge source of CBR is in text form, the method used is called text-
based case-based reasoning; for example, the CBR method is applied to the Text-to-SQL
task in semantic parsing [124], or the Date-to-Text generation task [125,126].
Image processing is an important research field in pattern recognition. Images can be
used as query objects and can also appear in the solution to the problem. In other words, the
image itself is an expression of knowledge. The description, interpretation, and retrieval of
images are also important components in CBR applications. At present, the CBR method is
mainly used as an auxiliary means of image processing to improve performance [
127
–
129
].
5. Summary and Challenge
Aha [
1
] and De Mantaras et al. [
15
] pointed out that the rise and development of
CBR is influenced by cognitive science, analogical reasoning, knowledge-based systems,
and uncertainty systems. Thus, this method has the advantage of being interdisciplinary.
Moreover, the positive aspects of these factors can be used to improve reasoning. As shown
by the above key technologies and application development, the CBR method also has some
defects; for example, in the processing of unstructured data, CBR applications are relatively
rare. In terms of the accurate prediction of parameters, it only provides approximate
answers and cannot be applied to unsupervised learning problems. As Watson stated, CBR,
as a methodology, can be a flexible combination of various technologies to achieve problem
solving, interpretation, and learning processes, and can be applied to different research
areas [
10
]. In recent years, AI technology represented by DL has made breakthrough
progress and has attracted wide attention around the world. The research and application
of CBR are also developing. Combined with the development needs of explainable AI
(XAI) [130], according to the idea of DL, we believe that CBR may have the following two
research directions in future development:
(1) Explainable CBR (XCBR) Decision Support
With the great success of methods such as ML and DL in AI, explanation has been iden-
tified as a key factor in the adoption of AI systems in a wide range of environments [
131
].
The XAI study attempts to address several issues related to the growing need for expla-
nation models, such as; how do we design explanation models? How do we assess the
resulting explanation? What knowledge do we need to construct explanations? How do
we tell AI systems to provide users with suggestions and decisions, etc.? As mentioned at
the beginning of this section, CBR can achieve the function of problem explanation and can
be applied well in explanation tasks. It has been studied and applied in interactive inter-
pretation and memory-based technology to generate interpretation, such as expert systems
and recommendation systems. Therefore, CBR has significant potential and foundation in
explaining opaque (i.e., black box) AI methods and establishing interpretable AI systems.
In 2022, the theme of the 4th XCBR workshop in the 30th ICCBR was case-based
reasoning for the explanation of intelligent systems under the aim of XAI. At present, CBR
has made many contributions to XAI research, such as image explanation tasks [
132
], text
explanation tasks [
133
], and neural network-based explanation tasks [
134
]. However, CBR is
only moderately used in XAI or in certain aspects of intelligent systems; therefore, regarding
the interpretability of complex AI systems, CBR still has much room for development, such
as [135,136].
On the one hand, the interpretation application of CBR in AI systems is mainly
oriented to users and provides decision support for users with interactive interpretation;
however, in fact, the object-oriented aspect of a complex system is not only the users, and
should also include the system operation and maintenance of staff, technical personnel, and
other related personnel. Therefore, the interpretation should vary from person to person,
using CBR to achieve diverse interpretation. How CBR achieves different interpretations
according to different objects in AI systems will be an interesting and challenging research
direction. The diversity of object-oriented interpretation means that XCBR needs to include
the interpretation cases of different objects in the case base. This is not a simple case list,
and its construction problem needs to be solved. At the same time, when new objects and

Appl. Sci. 2024,14, 7130 16 of 22
new cases appear, it means the emergence of new explanations, which update and maintain
the case base.
On the other hand, how to establish a user-centered personalized interpretation model
has always been a problem worthy of study. Darias proposed that the CBR system is a
living system, and XAI is a constantly changing research field. The case base must update
new interpreters to adapt to new problems and solutions; however, one of the biggest
shortcomings of the existing XAI library is the lack of personalized interpretation [
137
].
Darias et al. [
65
] made attempts to address these shortcomings, and they have mentioned
that this is only the first step in personalized interpretation models. Therefore, it is a long-
term development outlook to provide personalized interpretation according to different
user intentions and needs. It involves multi-domain content, user data representation,
AI black box models, and demand analysis, etc. Once these problems and challenges are
solved, it is believed that XCBR will go further in the field of AI.
(2) Deep CBR
In recent years, great success in the field of DL has prompted the CBR community to
commit to applying DL technology to CBR. CBR, as a methodology, has a complete set of
reasoning processes. These processes always need the support of other algorithms and
technologies, and DL technology has unparalleled learning ability for large and complex
case data, undoubtedly providing new research directions and technical support for CBR
research. Table 3describes the differences and similarities between the two from data,
results, learning methods, and other aspects. David proposed that the challenge for the CBR
community is to define necessary tasks and integration. Introducing DL components into a
CBR system in order to realize CBR function can reduce knowledge burden and improve
flexibility, and points to the problems faced by its application in case representation,
adaptation, and so on. At the same time, David also studied the influence of network
architecture on the quality of CBR feature extraction [
138
], which further promoted the
development of DL and CBR.
Table 3. Deep learning versus CBR.
Deep Learning CBR
Data, experience, and knowledge are all examples Cases
It is about learning knowledge It is about learning knowledge
General rules and laws are generated Specific solutions are generated
Technology Methodology
Unsupervised learning possible Unsupervised problem solving cannot be done
Eager learners Lazy learners
Results are not precise or certain Results are not precise or certain
As mentioned in the description above, DL technology has been applied in various
steps in CBR systems [
74
,
118
,
130
]; however, the application of the DL method in CBR is
scattered, which is only reflected in a certain step or in some simple combination with
the CBR method. It is a direct application based on representation; that is, only some
algorithms in DL are selected to implement a process in CBR. Although this can improve
the performance and efficiency of CBR systems to a certain extent, it is only the beginning
of deep CBR research. It lacks a more abstract general CBR system based on DL idea, or a
high-level framework process to integrate advanced CBR with DL architecture. Leake and
Crandall [
30
] advocated using CBR to promote the development and challenges of DL. Can
we, in turn, use DL thinking to design new CBR frameworks and methods to achieve the
whole process of reasoning, learning, and explanation? Alternatively, how can we realize
deep reasoning in CBR? Here, depth refers not only to the comprehensive application of DL
technology in CBR, but also to deeper learning and reasoning based on the CBR structure.
Researchers at Deakin University proposed the implementation from DL to deep
reasoning at the 2021 International Conference on Knowledge Discovery and Data Mining
(KDD). The emphasis in this tutorial is still on implementing reasoning in DL. Obviously,

Appl. Sci. 2024,14, 7130 17 of 22
CBR is more advantageous at the reasoning level, and its lazy learning can reduce the
burden of learning to a certain extent. Therefore, further consideration of the combination
of CBR and DL in applying deep CBR to complex AI systems may be a future development
direction in CBR. To achieve this, a bridge between the two methods needs to be established.
As Glatt et al. [
83
] have noted, there are relatively few studies on the CBR community
considering reinforcement learning (RL) methods to accelerate CBR. This lack of research
may be because the differences in formula and vocabulary hinder the exchange of ideas
and methods between communities; therefore, CBR systems based on DL also need to
consider how to use DL language or symbols to describe the knowledge in CBR, such as
vocabulary knowledge, case knowledge, adaptation knowledge, or rules. This is the first
step in deep CBR, reflecting improvements in methodology and technology.
After the first step of combining DL and CBR, we should further consider the concept
of ‘deep’. As deep learning realizes the depth of learning by the depth of the network, deep
CBR should realize the depth of reasoning with the depth. To date, CBR is limited to a
4R cycle reasoning process. Can CBR be explored in depth, based on this process? For
example, can deep CBR be achieved by the nesting of multiple cycles? It is not necessarily
achieved through multiple cycles of a complete 4R cycle, but, rather, through a process
of several alternating steps in the 4R cycle. Alternatively, the 4R cycle process can be
extended and refined into more detailed reasoning steps to achieve deep reasoning. This is
an improvement upon the deep CBR structure. If these ideas and technical difficulties are
realized, it will be possible to promote the development of CBR to new heights.
Author Contributions: Conceptualization, A.Y. and Z.C.; methodology, Z.C.; software, Z.C.; val-
idation, Z.C.; formal analysis, Z.C.; investigation, Z.C.; resources, A.Y.; data curation, Cheng, Z;
writing—original draft preparation, Z.C.; writing—review and editing, A.Y.; visualization, Z.C.;
supervision, A.Y.; project administration, A.Y.; funding acquisition, A.Y. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was funded by the National Natural Science Foundation of China grant
number 62373017 and 62073006 and the Beijing Natural Science Foundation of China grant num-
ber 4212032.
Conflicts of Interest: The authors declare no conflict of interest.
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