The coupling of data and knowledge has a synergistic effect when building an intelligent data base. The goal is to integrate the data and knowledge almost to the point of indistinguishability, permitting them to be used interchangeably. Examples given in this paper suggest that Case-Based Reasoning is a more integrated way to link data and knowledge than pure rule-based reasoning

Casadaban, Cyprian

Pulaski, Kirt

English

NASA Technical Reports Server

Case-Based Reasoning: 
The Marriage of Knowledge Base and Data Base 
Kirt Pulaski and Cyprian Casadaban 
Martin Marietta Manned Space Systems 
Post Office Box 29304 Mail Stop 3691 
New Orleans, Louisiana 70 1 89 
Abstract 
The coupling of data and knowledge has a synergistic effect when building 
an intelligent data base. The goal is to integrate the data and knowledge almost 
to the point of indistinguishability, therefore permitting them to be used inter- 
changeably. Examples given in this paper suggest that Case-Based Reasoning is a 
more integrated way to link data and knowledge than pure rule-based reasoning. 
1 Introduction 
This paper describes some preliminary results of a NASA Mission Task being 
performed by the Automation & Intelligent Systems group at Martin Marietta 
Manned Space Systems in New Orleans, where the External Tank for the Space 
Shuttle is assembled. The goal of the project is to increase productivity at weld 
stations by decreasing downtime. 
The plan to effect better productivity is to build an intelligent data base that 
gives advice about possible downtime causes and streamlines the follow-up 
paperwork. Efforts in the current fiscal year are producing a data base of reports of 
weld station downtimes as they occur. An ancillary knowledge base is growing as 
a result of the need to deepen the understanding of the weld station data. 
A paradigm of reasoning needs to be selected that will best integrate the 
knowledge base and data base. Case-Based Reasoning (CBR) is being considered 
for several reasons: 
183 
https://ntrs.nasa.gov/search.jsp?R=19890006203 2020-03-20T04:58:35+00:00Z
CBR systems derive their power from knowledge base/data base 
interaction. 
Both the knowledge and data are currently being collected case by case 
from the shop floor. 
Advice-giving is memory-based and so is CBR. 
Cases hold both data and knowledge in one structure, so the link between 
them is highly integrated. 
l CBR solves the problem of case disparity by being sensitive to and exploit- 
ing similarities. 
The organization of this paper is to present the major components of the 
proposed Weld Intelligent Data Base (WIDB). First, a background of CBR is pre- 
sented. 
i 
2 Case-Based Reasoning 
The basic concept of CBR is simple: solve new problems by adapting solu- 
tions from old problems. The representation of a problem solving episode is 
called a case. Similarities between past and present cases establish a very high 
focus for problem solving. This focus is very difficult to achieve when only using 
rule-based reasoning (RBR). 
I 
CBR lends the power of examples to problem solving. Unlike the seg- 
mented explanations attributable to RBR, CBR explains its solutions with whole, 
relevant, concrete and familiar examples. CBR manifests learning as a by-prod- 
uct of adding cases, as they occur, to a case base. Remindings also provide a 
basis for knowledge acquisition (Riesbeck 881. 
The generic system shown in Figure 1 depicts a cooperation between CBR 
and RBR (Pulaski 88). Each method has merits which the other lacks. RBR eases 
the implementation of a heuristic control strategy and is better for fast, non- 
complex, localized inferencing and for recording metaknowledge. CBR weaves 
the history of experience into problem solving. 
3 Weld Data Base 
When a downtime is reported a team of weld experts responds to the call. 
They work together to determine the cause of the problem, how to get the weld 
station operational as soon as possible and what to do to keep the problem 
from occurring again. 
I The result of a weld team call is a completed form which records, in several 
levels of detail, the path that the problem solving took from initial diagnosis 
throaw solution. 
184 
f 1 
Rule- Based 
Reasoner 
problem features cues 
Case-Based b Conceptual ' Problem 
Solver Memory 
case fragments cases 
solution I b' Feedback ' problem solving episode ' 
User 
current problem \Interface 
tool 
eff ec tivitv 
Figure 1 : Generic Case-Based Reasoner 
- . . - - - - 
T50 18 
LWT-48 
Each weld team report form is further broken down, analyzed and entered 
as a record into a data base. The year-to-date data base of weld station 
downtimes consists of about 250 records. Figure 2 shows an abbreviated version 
of a typical data base record that might result from a weld team call. 
problem 
cause 
I date I JAN -25-88 I 
- .. .- D 
TORCH-DOVE 
UNEVEN -TACK 
downtime 
prepared- by 
code 
H4 
UNKNOWN 
C88-2 1 
Figure 2: Weld Team Report Data Base Record 
4 Weld Knowledge Base 
Figure 2 shows data from a weld team report. Certain types of knowledge 
need to be associated with the various fields, and the values in them, to enable 
an intelligent computational process to reason with that information. 
One type of data base field knowledge is a systematic breakdown of 
allowable values for a given field. This knowledge is implemented as a BNF 
grammar which reduces the free English form to a parsed field value. 
185 
For example: 
'No power to control system console.' 
is transformed into 
CONTROL-SYS/CONSOLE/POWER/NOT/EXIST 
according to the syntactic categories 
System/Component/Subcomponent/ModaI/Action 
dictated by the problem description grammar. 
The grammars do more than constrain field values. The grammar 
transformation rules can be used to support other functions such as form-filling 
and natural language (see Section 7). Also, the tokens of the grammars are 
often the same as the indices which cases in the case base are stored by, 
providing deep knowledge for storage and retrieval of cases. 
Another type of knowledge relates field values to other field values. This 
knowledge is implemented as relational links in a field value hierarchy. Figure 3 
shows part of the Problem Tree. The relational links are ISA links. For example, a 
PILOT-ARC-P ROBLE M ISA TORC H-PROBLE M IS A PRO BLE MI 
C R T - P R O B L E M ' p \  
COMPUTER-PROBLEM 
PRI NTER-PRO BLEM d
LIMIT-SWITCH-PROBLEM-\ 
POW ER-SU PPLY-PRO BLEM 
AVC-PROBLEM 
GAS-LINE-PROBLEM 
FIT-U P-PROBLE M 
CONTROL-SYSPROBLEM 2 
d 7FAciLiTiEs-PRoBLm 
PRocEDuRE-PRoBLEM PROBLEM 
SET-UP-PROBLEM d 
VOLTAGE-PROBLEM  
CHILL-BAR-PROBLEM / 
TORCH-ASSEMBLY-PROBLEM J 
AMPERAGE-PROBLEM 7PROCESS-V~ABLE-PROBLEM 
\ToOLI"PROBLEM 
-ToRCH-PROBLEM 
ACTUATOR-PROBLEM 
PILOT-ARC-PROBLEM 
Figure 3: Problem ISA Tree 
186 
Relational knowledge helps to hypothesize uncertain or unknown 
knowledge in the data base!. For example in Figure 2, the name of the person 
who prepared the weld team report is UNKNOWN. Using relational knowledge in 
the Problem Tree and the Prepared-by Tree helps to determine that the person 
who prepared the report was probably someone who usually handles TORCH- 
PROBLEMS. 
Other relational knowledge can be used to link field values. For example, 
a CAUSED-BY link helps to consider causes for a problem. Any cause related to 
the current problem by a CAUSED-BY link is considered as a probable cause for 
that problem. Figure 4 shows that whenever there is a TORCH-CUTTING problem 
then three causes are considered. 
I ~i <GAS-llNE-CAUSE 
TORCH -C UlTl NG- PRO B LE M - CAUSED-BY ORIFICE-CAUSE 
TUNGSTON-CAUSE 
Figure 4: The CAUSED-BY Relation 
5 Weld Case Base 
The weld case base is a union of the weld data base and the weld 
knowledge base. The case base looks very similar to the data base but with an 
implicit deeper understanding of the values found in the cases. One source of 
the deeper understanding is the weld knowledge base. Another source is the 
knowledge that accumulates over time as reminded cases are analyzed and 
reasoned about during advice-giving . 
Figure 5 shows an abbreviated version of a weld case from the case base. 
The case representation shows some of what CBR adds to problem solving. 
When CASE- 145 occurred, the CBR reasoner was reminded-of three previous 
cases which helped to postulate a set of related-problems to check for. Causes 
for the problem and related problems were hypothesized and investigated. 
Causes which were not substantiated were stored as failed-causes along with 
reasons why. The actual cause was substantiated and its justifications were 
recorded. Later, CASE-145 served as a reminding-for two other cases. 
6 Weld Intelligent Data Base 
The advisory function in the WIDB is directly supported by CBR. The design 
of the WlDB includes other functions that CBR does not directly support. The 
following paragraphs mention these functions and what approaches will be 
used to implement them. 
The paper work currently associated with the data collection task will be 
automated. Human interface and form-filling methods will be used to accept 
user input, verify it, categorize it and build a case representation for the CBR 
advice-g iver. 
187 
name I CASE-145 
code 
reminded-of 
..- - 
date JAN-25-88 
tool T5018 
C88- 145 
CASE-007 
CASE -063 
related-problems 
CASE- 129 
AVC -PROBLEM 
failed-causes 
reasons 
cause 
justifications 
WI RE -FEED - PROBLE M 
CONTROL-SYS-CAUSE 
WIRE-FEED-CAUSE 
VOLTAGE -OK 
WELD-BEAD-OK 
UNEVEN-TACK 
MISMATCH-PROBLEM 
WELD-SEAM-PROBLEN 
~ 
Figure 5: Example from the Weld Case Base 
reminding-for 
Report generation using the data base will include summaries, statistical 
analysis, trending and relational analysis. 
START-U P- PRO B LE M 
CASE- 158 
Relational analysis may be aided with the use of a knowledge discovery 
tool, for example IXL by Intelligenceware, Inc. 
The use of similarity networks (Bailey 88) is being considered to augment 
case generalization to improve relational retrieval. Better relational retrieval will 
improve advice-giving. 
A natural language interface will ease querying, browsing and receiving 
advice. 
7 Discussion 
In an advice-giving domain it is very helpful to reason from past examples. 
CBR, by definition, is then a strong candidate for the WlDB domain, plus it has 
even more to offer. The work described here shows that CBR is especially useful 
when a solution involves an intelligent data base; that is, a data base 
cooperating with a knowledge base. 
188 
The performance of an intelligent data base depends on the level of 
integration between the data base and the knowledge base. CBR offers the 
highest level of integration since both data and knowledge are stored in cases 
and are not distinguishable. 
The case base can grow in different ways. As more data and knowledge 
are acquired, more cases are built and added to the case base. Each 
occurrence of advice-giving is also a case, so the system grows each time it is 
used. More cases means better remindings for future advice-giving. This 
learning mechanism does not require the CBR reasoner to change; its 
performance increases as a result of better remindings. This caliber of learning is 
very difficult to achieve with pure rule-based systems. 
There will be times when a CBR reasoner cannot solve a problem or 
subproblem. As Figure 1 suggests, a rule-based reasoner is then used to 
generate the unknown solution. This also leads to learning since the solution is 
saved in the case base and the rule-based reasoner never has to solve that 
problem (or other problems like it) again. 
8 Conclusion 
The wide-spread acceptance of a knowledge-based technique into a 
mainstream computing environment depends on several important issues: 
improvement, embedability and integration. These issues are important 
because knowledge-based techniques rarely produce an entire solution to a 
problem. Rather, the best solutions piece together a mix of subsolutions which 
use both knowledge-based and conventional approaches. 
The design of the WlDB addresses these issues. First of all, a knowledge- 
based technique must offer an improvement. The WlDB augments a 
conventional data base with knowledge. The improvement is a better and 
deeper working understanding of the information in the data base; a result of 
linking data base field values to eachother with knowledge. 
The WIDB uses CBR for advice-giving type problem solving. This offers an 
improvement since conventional methods have difficulty implementing advice- 
giving which is sensitive to a history that is constantly being modified. 
Another issue is embedability: knowledge-based techniques must embed 
into the same environment as conventional methods. The knowledge that the 
WlDB uses to augment its data base is fully embedable into a relational data 
base environment. The knowledge links that connect data base field values to 
eachother are implemented as relations (for example CAUSED-BY and ISA) in 
the relational data base itself. 
189 
The cases for CBR are also embedable into a relational data base 
environment. CBR provides a seamless link between a data base and a 
knowledge base; in fact, the cases in the WIDB hold both data and knowledge. 
Therefore, the cases can be implemented as data base records. 
The last issue is integration. Different system components, whether 
knowledge-based or conventional, must fully integrate into one delivery 
environment. The WIDB integrates a CBR subsystem, a relational data base 
subsystem and a natural language front/back end. Each subsystem will be 
integrated with the others to operate in one delivery environment which is a 386- 
based microcomputer. 
Ref e re nces 
1. Bailey, D., Thompson, D., and Feinstein, J. 'Similarity Networks.' PC AI, July/ 
August, 1988. pp. 29-32. 
2. Pulaski, K. ELMO: An Episodic Long-term Memory Organizer for Case- 
Based Reasoning. AAAl Case-Based Reasoning Workshop, St. Paul, 
Minnesota, August 23,1988. 
3. Riesbeck, C. K. An Interface for Case-Based Knowledge Acquisition. In 
Proceedings of DARPA/ISTO Case-Based Reasoning Workshop, Clearwater 
Beach, Florida, May 11-13, 1988. pp. 312-326. 
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