實例推理在澳門之研究
黃志文, 李怡平, 王百鍵, 麥炳源, 韋孟宇
澳門大學 科技學院
摘要
實例推理(Case-Based Reasoning, CBR) 是人工智能的其中一種方法。實例是
指由電腦所儲存的某種物件或狀況。實例推理的推理方法有賴於儲存在實例庫
中的實例，通過比對的方法，由實例庫中取出與用戶輸入的問題狀況配合的過
往成功實例，再加上預先定義好的自調知識 (Adaptation knowledge)，以使調
出實例符合目前的情況。本文描述實例推理的基本概念，推理週期，多種主要
的自調方法，並對實例這種技術作一簡介。此外，本人亦描述實例推理在澳門
的多種應用領域。
1
Research of Case-based Reasoning
in Macau
Vong Chi-man, Li Yi-ping, Wong Pak-kin, Mak Peng-un, Vai Mang-I
Faculty of Science and Technology
University of Macau, Macau
Abstract
Case-based reasoning (CBR) is a methodology in Artificial Intelligence (AI). A case is
a situation, episode, etc. remembered (stored) by a computer, and the attempt to reuse
it is the subject of case-based reasoning [3,4,5]. CBR reasons by means of past cases
stored in its case library, by retrieving similar cases in the past matched against the
user input problem situation, and then using pre-defined adaptation knowledge to
reuse the retrieved case to fit current situation. This paper presents the basic concepts
of CBR, its reasoning cycle, the major classes of adaptation methods used in CBR and
a brief introduction of case-based adaptation (CBA). In addition, several applications
of CBR in Macau are presented in the paper.
1. Introduction
1.1 What is Case-based Reasoning?
At its most simple, CBR is based on the observation that, when we solve a problem,
we often base our solution on one that worked successfully for a similar problem in
the past. An example of CBR would be someone driving to work in the morning. That
person doesn’t explicitly plan his route, he just takes the route he usually takes. If that
person meets a traffic jam, he may remember how he avoided that jam in the past. If
that person tries a new route to avoid the traffic jam and he is successful, he will
remember it and perhaps use it in the future. Another example would be a student
doing a statistics assignment. When he sees a new problem, he will try to see if there
is any similarity between the current problem and the ones he learned in the class. If
there exists one past problem whose solution traces fit onto the current one, the
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student would apply the same past method to solve the current problem. These
examples show the major two steps in the process of CBR –– retrieval and adaptation,
which will be discussed further.
A more formal definition for CBR: CBR is a simple problem-solving paradigm that
involves matching a current problem against problems that were successfully solved
in the past. The process can be augmented by adapting solutions so that they more
closely match the current problem.
1.2 The advantages of Case-based Reasoning
CBR suggests a model of reasoning that incorporates problem solving, understanding,
and learning and integrates all with memory processes [3]. The advantages of CBR
are briefly discussed below:

Reusing old cases is advantageous in dealing with situations that always recur.
With reference to old cases, complexities of solving novel situations can be
reduced.

Traditional reasoning processes cannot recall a relevant case unless it understands
the new situation it is in. This suggests that understanding or interpreting a
situation is a necessary part of the reasoning cycle and both a prerequisite to
problem solving and a co-requisite during problem solving. But the need for
problem understanding is not specific to CBR.

CBR does not require exact description of problem to proceed the reasoning
process while traditional reasoning (rule-based reasoning) cannot work with
incomplete problem description.

CBR emphasizes the use of concrete instances over abstract operators because
they can provide more guidance and operational knowledge in solving a new
problem than can abstract operators. Furthermore, they can show application and
use of knowledge that abstract operators do not supply.

CBR emphasizes manipulation of cases over composition, decomposition, and
recomposition processes. Though sometimes composition tasks still occur in
reasoning, reasoning using concrete cases will come at first and then composition
of operators next.
3

It is usually necessary to adapt an old solution to fit a new situation because no old
case is ever exactly the same as a new one. Adaptation compensates for the
differences between an old situation and a new one, while most parts of the old
case can still be reused.

Learning of CBR occurs as a natural consequence of reasoning by accumulating
new cases. Successful past cases can provide information or procedure to solve
current problem while unsuccessful past cases can warn that certain undesired
results, happened in the past, should be avoided in current situation.

Evaluation of cases can be based on feedback and follow-up procedures to judge
whether the cases are useful and give contribution to learning. If cases are learnt
(stored) without any follow-up analysis and evaluation, the reasoning using past
cases will be unreliable.
1.3 Methodology in Case-based Reasoning
As it was mentioned above, CBR consists of two main parts –– retrieval and
adaptation. But in fact and in detail, CBR is constituted with four RE’s [5]:
1. retrieve
–– retrieve similar past case matched against current problem
2. reuse
–– reuse to solve current problem based on solution of past case
3. revise
–– revise the past solution if any contradiction occurs when
applied to current problem
4. retain
–– retain the final solution along with the problem as a case if
the case is useful in the future
Fig.1 illustrates the whole process of CBR. When user inputs a problem, the problem
is interpreted and converted as a new case into the specific format of the reasoning
system. Then the converted new case enters the stage of RETRIEVAL where the new
case is matched against the previous cases in the case library of the reasoning system.
The retrieved case and the new case are both passed to next stage REUSE where the
solution part of the retrieved case is applied to the new case, with the guidance of
adaptation knowledge. The application of old solution involves substitution of
solution features, structural modification of the solution and/or derivational replay of
solution. These methods will be discussed at next section. After this stage, now the
new case is along with the adapted solution based on the old one. This adapted
solution is considered as a suggested solution, which is still incomplete because it is
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adapted according to the requirement of new case and this solution may have
inconsistency among its solution parts. To ensure the adapted solution is consistent, it
will be passed to next stage REVISE, where the adapted solution will be further
adapted based on the user feedback and additional meta-adaptation knowledge called
repair. This final adapted solution is then returned as a confirmed solution to the new
case but this does not come to an end. It was discussed that CBR learns by
accumulating new cases. However, should any newly derived case be accumulated in
the case library ? The answer is no. CBR should only store the cases that can
contribute to future reasoning of solutions, which could not be done only by the cases
in current case library. In other words, if the cases in a case library are capable enough
to cover the newly adapted solution, this new solution should not be stored in order to
avoid redundancy and inconsistency. Otherwise, it should be stored for future use.
This leads to the final stage of CBR – RETAIN. The case considered as being able to
contribute in the future is named “Learned case” and stored in the case library.
2. Adaptation Methods in Case-based Reasoning
In past, most of the research and development concentrate on the stage of retrieval
and similarity of problem pairs, so many different and efficient for evaluating
similarity of cases were developed. However very few effort has been done on the
complicated and more important part of CBR –– adaptation [4,7,10,11,12,17,18].
Adaptation of CBR can be done by three different methods, according to the
complexity of the problem domain. The methods are substitutional adaptation,
transformational adaptation and derivational replay.
2.1 Substitutional Adaptation
Substitution is the process of choosing and installing a replacement for some part of
an old solution [3]. Substitution is usually done by reinstantiation and parameter
adjustment.
Reinstantiation is the process to find another object of the same class to replace the
old object [3]. It is used when the frameworks of an old and new problem are
obviously the same but roles in the new case are filled differently than roles in the old
5
one. In traveling planning, we want to traveling from Macau to Shanghai. We define
that “traveling way” is a class of the way to traveling, which simply consists of
Problem
New
Case
Learned
Cases
RETRIEVE
RETAIN
Retrieved
Case
Previous
Cases
Tested or
Repaired
Case
New
Case
Adaptation
Knowledge
REUSE
REVISE
New
Case
Confirmed Solution
Suggested Solution
Figure 1. The case-based reasoning cycle
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“Airplane”, “Ship”, “Train” and “Vehicle”. A previous case may record that the
traveling was by train. However, new case requires a limited time constraint so that
“Train” will be replaced / reinstantiated by “Airplane” to fulfill this constraint.
Parameter adjustment is a technique for interpreting values in a new solution based on
those from an old one [3]. In parameter adjustment, changes in parameters in an old
solution are made in response to differences between problem specifications in an old
and a new case. It is a two-step process. First, the old and new problem descriptions
are compared and their differences extracted. Then adaptation knowledge, usually in
the form of rules, are selected based on requirement and applied to the old solution to
create a new one that satisfies the requirement of the new problem. Using the above
traveling example, if time constraint is specified in a new problem, then traveling way
will be substituted by “Airplane”. Furthermore, the parameter of the new solution
“cost of traveling” will be adjusted because the ticket fee for airplane is more
expensive than the fee for ship. This is adjusted by multiplying a certain ratio to the
initial ticket fee to get a consistent new solution.
2.2 Transformational Adaptation
Substitution is appropriate if there already exists an item or concept that can be
substituted for some inappropriate value. But it cannot be used if the item required
does not yet exist. Nor can it be used for insertions or deletions of extra items in an
old solution. [3]
To compensate the inability of adding to or removing items from an existing solution
to become a new solution, another kind adaptation method, transformational
adaptation, is proposed. Under this objective, this method mainly deals with the
reconstruction of solution structure, augmented with substitutional techniques to adapt
a solution.
Using the traveling example, if a traveling by train was adapted to fit the limited time
constraint specified in the current problem, then the “way of traveling” will be filled
in by “Airplane”. At this time, another field “Airport Tax” should be inserted as a part
of solution in the record to specify the amount of tax to pay, which does not need to
specify if traveling way is by ship, train or vehicle. Inversely, if an airplane traveling
7
was adapted to a ship, train or vehicle traveling, the field “Airport Tax” in the old
solution should be removed to maintain the consistency of the new solution.
2.3 Derivational replay
Derivational replay[13,19,20] is the process of using the method of deriving an old
solution or solution piece to derive a solution in the new situation. Rather than storing
and directly reusing a solution itself, CBR can store a trace of how that solution was
generated and replay it in the new situation. A trace is a sequence of reasoning steps
with justification, similar to the operators in traditional rule-based reasoning. So it can
also be considered as a chaining of operators. When the solution is replayed to solve
future problems, the replay process can directly take into account differences between
the old and new situations. Since every step in the solution trace is along with
explanation why this step is taken, memory organization of case structure becomes
very complex. So, this method is not recommended for simple and easy task domain.
In complex task domains such as route planning, design and synthesis tasks, this
adaptation method seems to be most appropriate so far.
Example: In a previous traveling case by train from Macau to Shanghai, it records the
following:
From
To
By
Reason
Justification
Macau
Zhuhai
Vehicle
(vehicle to GuongZhou)
Always necessary
Zhuhai
GuongZhou
Vehicle
(train to Shanghai)
if by train or vehicle
Train
(Goal)
if by train
GuongZhou Shanghai
Old case organized for derivational replay
Then the current problem is to find a traveling path by train from Macau to Beijing.
The CBR process will consider the first solution step. Because the fist step is always
necessary according to the justification, no matter which traveling way is to take, the
new solution will consist of first step from the old solution. Then, the second step is
taken into consideration whether it should be included, now the traveling is by train.
So, we need to go to GuongZhou take a train and hence the second step is also
8
included in the new solution. For the third step, it is also included because it is the
action to travel to Beijing but simple substitution is needed to modify the destination
from Shanghai to Beijing and the new solution will become:
From
To
By
Reason
Justification
Macau
Zhuhai
Vehicle
(vehicle to GuongZhou)
Always necessary
Zhuhai
GuongZhou
Vehicle
(train to Shanghai)
if by train or vehicle
Train
(Goal)
if by train
GuongZhou Beijing
New case generated by applying derivational replay
This example is simple and it seems to be more or less the same as done in
substitutional adaptation. However, it involves synthesis of solution pieces by
considering the solution pieces one by one to determine whether they should
constitute the new solution (transformational adaptation), plus necessary value
substitution (substitutional adaptation).
3. Case-based Adaptation
Adaptation is an important process in CBR as discussed above. Usually adaptation
knowledge are in the form of rules specifying, under a certain situation, how the
value of a feature is modified, or some feature should be inserted or removed to
generate a solution for new problem. At this time, know-how adaptation knowledge
can be applied because the problem domain is simple and well understood so that
features to adapt can be easily allocated by comparing differences between the new
problem and its past similar case. However, in many real world problem domains,
especially planning and design, there are no universal rules for adapting a feature, or
the rules are too abstract to use. Then know-how adaptation knowledge can only be
applied based on expert experience. The selection of which rule from a set of
adaptation rules for a feature may change under different situations. Then another
source of adaptation knowledge is necessary to guide the selection of suitable rule
from a set adaptation rules applied to a feature. Because the selection is based on
expert experience, another level of CBR could be established to support the selection
9
of adaptation rules. This method of integrating two levels of CBR is called case-based
adaptation (CBA) [11,16,17,18].
In case-based adaptation, guidance for adaptation, both what to adapt and how, is
given by a previous case [3]. The case supplying these adaptation information is
called adaptation case. When potential problems with solutions are identified, it is
not always clear exactly what should be fixed to alleviate the problem. If blindly
applying adaptation rules in a planning or design domain, the newly generated
solution may not be idiosyncratic although it satisfies the entire specification
requirement from user. However, previous cases to guide adaptation can promote
novel adaptations leading to creative solution.
Domain expert’s domain knowledge of how adaptation is performed, including which
features to adapt and which adaptation rules are used, etc., is captured into the
adaptation cases, presumably provides adaptation cases. After initial installation of
adaptation case library, as time passes by, the adaptation case library will be filled
with more and more adaptation cases by repeatedly using of the CBR system by users.
This provides another way to acquire expert knowledge that is not easy to capture via
communication into production rules. Of course, the main objective of case-based
adaptation is to provide a more convenient and consistent method to perform case
adaptation in some complex planning or design domains.
4. Methodologies for building CBR systems
Full acceptance of CBR by industry depends on establishing software development
methodologies for CBR, to define how to organize and develop CBR projects.
Lessons from CBR applications form a foundation for defining such methodologies
[9]. One fundamental principle revealed by many experiences is the value of an
iterative development process. Since CBR systems can provide useful results even
with a partial case library, systems can be set up with a set of seed cases that is
augmented as gaps are revealed during use. Basically the process for developing a
CBR application follows three phases:
Case-base design: a general representation for cases is developed using sources at
hand (e.g., documentation, database records, and written accounts by experts). This is
10
accomplished by a coordinated effort involving users, managers, and the developers
of the system. A lexicon of terms used to describe problem features, the selection of
appropriate features for case indexing and the specification of database schemas used
to store cases are defined at this stage.
Initial case-base development: a small case base is initially developed to provide a
base for the application. The initial case base is then extensively reviewed both by
developers and users and is repeatedly refined until a valid case structure and case
base covering a large portion of the application area is complete.
Ongoing development and maintenance: The case base is managed in a traditional
manner by a database administrator. Statistical quality control techniques may be used
to monitor case accuracy and utility. Current research of maintenance of case library
focuses on the RETAIN stage of CBR process which skip the storing of a case if the
case is not covering any novel situation.
5. Application examples of CBR in Macau
CBR can be applied in a variety of different task contexts, including retrieval-only,
classification, diagnosis, planning, design and decision-support. The following are
some of applications of CBR in Macau.
5.1 Case-based Reasoning in Airport Terminal Area Planning
During the daily operations of an airport terminal, many decisions have to be made on
how to allocate facilities during a certain time in order to avoid passenger congestion.
This job falls in the category of airport planning[1,2]. Airport planners and managers
are frequently confronted with two problems:

How to determine the number of various major functional elements of an airport
terminal building that are needed to handle a given passenger throughput, and,
conversely.
11

How to determine the passenger throughput capability of existing airport terminal
facilities.
Figure 3. depicts the passenger flows for arrival and departure and the major
functional facilities1 considered in this project.
To solve these frequent problems, CBR is used to aid in the decision making process
of an airport planner. The system helps the planner in deciding the number of
facilities to allocate at certain times of the day given the number of peak hour
passengers, number of available facilities, etc. The idea for applying CBR to this
process came from a project developed in 1995 in the University of Macau. The
project, ATA-Sim, is a simulator of airport operations that helps predicting future
requirements of airport terminals in terms of facilities. This system is very robust but
it is a simulation, it is more appropriate to be used in medium to long term planning.
When the day-to-day operations of an airport are at stake, a system with a faster
response time and a larger experience in the field of airport planning is needed; this
system fills that gap very well.
5.2 Prediction of radio signal propagation for GSM networks
Another application area of CBR is in the prediction of radio signal propagation in
mobile phone networks [1,2]. A mobile phone network is constituted of transmission
elements distributed around a certain area. Each element has a certain transmission
range determined by the location, height, signal strength, and type of transmission
station and constrained by the obstacles that are in the path of the radio waves,
atmospheric conditions, etc. Predicting future requirements for this kind of network is
very important since the growth of a city is inevitable, new obstacles will prevent
radio waves from propagating to certain areas, old obstacles may be removed, making
stations in other areas unnecessary.
1
These are only a subset of all terminal facilities since the this system is to be used to support
the airport planner in his/her decision making process of how to allocate facilities on a day -today basis.
12
Aircraft
Arrival Passport
Control
Baggage Claim
Arrival Cus toms
Gate
Terminal Building
Entrance/Exit
Doors
Security Check
Departure
Passport Control
Check-in Des ks
Figure 3. Passenger flow in an airport terminal
The addition of a new transmission station in a certain area may arise after
verification that the signal strength in that area is no longer above a certain threshold
value for acceptable communication. If, in the past, a similar situation occurred where
the area had similar characteristics to the area in the current problem, past experience
in solving such a problem can be applied in the solution of the current problem. CBR
systems use prior episodes to solve new problems, thus, this class of system can be
applied to this problem.
13
A
transmitting
station with
360 degrees
beamwidth
A
transmitting
station with
60 degrees
beamwidth
The
transmission
area of the
station
The
transmission
area of the
station
The
transmission
distance of
the station
The
transmission
distance of
the station
Figure 3. A portion of a mobile phone network
The primary focus of the CBR system in this application will be to advise on the
height of the transmission station, the downtilt2 of the antenna and the transmitting
power of the station. These values will be features of cases in the system’s case
library. This case library will contain a series of previous experiences, each in the
form of a case, including all of the stations that are in operation. A problem case is
posed to the system by entering the beamwidth, the distance covered by the
transmitting station and the average height of buildings in the area. The system will
search its case library and return a case with the most similar situation.
The structure of a case will be divided into three different parts, a first part will
contain fields that will be entered and given to the system to start its search, it is the
problem description part. The recommendation, or solution, part will contain the
fields that provide necessary information to establish a station in the new area. Finally
the third part will contain estimates and measures of signal strength in the area of
influence of the new station. The proposed case structure for the CBR system is listed
in the table 1.
2
Downtilt is the downwards inclination angle of the antenna.
14
Field
Field’s class3 Explanation
Average height of buildings
Problem
in the area
Used to narrow the search procedure of CBR
and limit it to cases whose areas have similar
characteristics to the problem area. It would
not be of much interest if the system
recommended the height of the antenna to be
100 meters when the maximum height of a
building in the area was 50 meters.
Beamwidth
Problem
The transmitting beamwidth of the antenna.
Distance covered
Problem
The maximum distance a mobile phone unit
can be from the station.
Station height
Solution
The height the station is from the ground.
Antenna downtilt
Solution
The downwards angle of the antenna.
Transmitting power
Solution
The power the station transmits the signal.
Estimated signal strength
Estimation
The estimated strength of the signal within the
station’s area of influence.
Measured signal strength
Measurement The measured data indicating the signal
strength in the area.
Table 1. Proposed case structure for GSM networks case-based reasoner
Note: Even though in the table above each field is classified under problem, solution,
estimation, and measurement, all the fields are necessary to characterize a case. The
class of each field will only be used as reference when the case-based reasoner if
being consulted.
5.3 Case-based Adaptation applied to Hydraulic Circuit Design4
Hydraulic circuit design (HCD) is an industrial application domain from
Electromechanical Engineering that supports drawing design of circuit used in oil
hydraulic machines. A circuit design process involves sketching a possible circuit
layout, selecting potential components and calculating component sizes or circuit
limits. On completion of a circuit design, building prototypes and testing and
3
The field’s class is for reference regarding whether the field is a problem, solution or just an
field containing estimated of measured signal strength.
4
This is an undergoing project in Faculty of Science and Technology, University of Macau.
15
evaluating the candidate system performance are carried out [14,15]. Selecting
appropriate components and sketching the design layout both are based on the
experience of designer and most of the time, the selected layout and components are
very similar the ones used in past circuit designs. In order to save effort of doing
redundant and tedious task, HCD system is proposed to provide knowledge-based
support to guide designer for the completion of circuit design.
Traditional HCD systems used rule-based reasoning technique to check the
consistence of components in the circuit design. However, solving problem by
analogy even fits into the current design domain. With the help of emerging CBR
methodology, most of the problems mentioned above could be augmented at a certain
level. For example, a similar layout could be retrieved and ready for user modification.
Circuit design with little difference in parameter specification could be adapted
automatically by the system, in just substituting the parameter values or even
replacing certain components to fit the parameter specification. With CBR and CBA,
the circuit design system could learn by accumulating new cases and repetitive
training from expert users to refine its adaptability to new problem cases. Figure 4
briefly illustrates the structure of the program and interaction with users.
6. Conclusions
CBR is an emerging methodology in Artificial Intelligence. Its main purpose is to
retrieve and reuse analogical past experience to solve current problem. In many
domains, the rules of the domain have not yet been formalized or the formalization is
so abstract that it is hard to use because there is so much variation in the way abstract
concepts show themselves. CBR allows problem solving in such domains by basing
reasoning on experience in the domain rather than on formal models, because cases
capture associations between situations, solutions, and outcomes, capturing an
informal understanding of the domain at a concrete level. For some tasks, CBR
complements model-based or rule-based reasoning because cases can easily suggest
previous solutions that are hard to retrieve under ill-defined concepts. When similar
problem statements call for similar solutions, CBR has shown itself efficient and
accurate when asked to solve problems similar to those it has been before. Since CBR
provides a commonsense, intuitive model of problem solving, knowledge acquisition
16
becomes easier than traditional reasoning paradigms. In planning and design domains,
problems always recur. Much effort was wasted in repeating redundant jobs to find
out a new solution while planning and design [4,8]. To save the effort from repeating
redundancy, CBR could be employed to reuse past successful cases to derive a new
solution for new problem. Furthermore, CBR is useful in aiding decision-making [6]
when large amount of constraints have to be considered as discussed in the
application example of Airport Planning. Then previous successful cases could be
retrieved to present to users as reference, while previous failed cases as warning,
which works exactly as the natural and intuitive human reasoning process.
From these advantages over traditional reasoning paradigms, CBR systems is easier to
build and also efficient in solving similar problems, especially in complex domains
such as planning and design.
Users
Design
Result
Design
Specification
Component
Library
Modified Case
Description
Case
Specification
Expert Adaptation
Knowledge
Revised Case
Description
CBR Module
Dynamic Learning Module
(CBA)
Case Library
Adaptation Knowledge
Adaptation
Knowledge
Component
Icons
Drawing
Program
Retrieved Case
Description
Cases
Design
Drawing
User Modification
Graphical User Interface
Adaptation
Knowledge
Library
Figure 4. Program structure for Hydraulic Circuit Design applied with case-based adaptation
17
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