In recent years there has been increasing interest in applying the computer based problem solving techniques of Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS) to analyze extremely complex problems. A conceptual framework is developed for successfully integrating these three techniques. First, the fields of AI, OR, and DSS are defined and the relationships among the three fields are explored. Next, a comprehensive adaptive design methodology for AI and OR modeling within the context of a DSS is described. These observations are made: (1) the solution of extremely complex knowledge problems with ill-defined, changing requirements can benefit greatly from the use of the adaptive design process, (2) the field of DSS provides the focus on the decision making process essential for tailoring solutions to these complex problems, (3) the characteristics of AI, OR, and DSS tools appears to be converging rapidly, and (4) there is a growing need for an interdisciplinary AI/OR/DSS education

Parnell, Gregory S.

Rowell, William F.

Valusek, John R.

English

NASA Technical Reports Server

N88- 17245 
ARTIFICIAL INTELLIGENCE (AI), OPERATIONS RESEARCH (OR), 
A N D  DECISION suppoRT SYSTEMS (DSS) : A CONCEPT[JAC F'RAM3WORK 
GREGORY S. PARNELL WILLIAW P. ROWELL JOHN R. VALUSEK 
AIR FORCE INSTITUTE OF " N O I O G Y  
School of Engineering 
Department of Operational Sciences 
Wright-Patterson AFB, Ohio 45433-6583 
(513) 
ABSTRACT 
In recent years there has been 
increasing interest in applying the 
computer-based problem-solving 
techniques of Artificial Intelligence 
(AI) , Operations Research (OR) , and 
Decision Support Systems (DSS) to 
analyze extremely complex problems. The 
purpose of this paper is to develop a 
conceptual framework for successfully 
integrating these three techniques. 
First, the fields of AI, OR, and DSS are 
defined and the relationships among the 
three fields are explored. Next, a 
comprehensive adaptive design 
methodology for AI and OR modeling 
within the context of a DSS is 
described. The paper concludes with 
four major observations: (1) The 
solution of extremely complex knowledge 
problems with ill-defined, changing 
requirements can benefit greatly from 
the use of the adaptive design process, 
(2) the field of DSS provides the focus 
on the decision making process essential 
for tailoring solutions to these complex 
problems, (3) the characteristics of AI, 
OR, and DSS tools appears to be 
converging rapidly, (4) there is a 
growing need for an interdisciplinary 
AI/OR/DSS education. 
INTRODUCTION 
In recent years there has been 
increasing interest in applying the 
computer-based problem-solving 
techniques of Artificial Intelligence 
(AI) , Operations Research (OR) , and 
Decision Support Systems (DSS) to 
analyze extremely complex problems. The 
purpose of this paper is to develop a 
conceptual framework for successfully 
integrating these three techniques. 
First, the fields of AI, OR, and DSS are 
defined and the relationships among the 
three fields are explored. Next, a 
comprehensive adaptive design 
methodology for AI and OR modeling 
2 55-2 549 
within the context of a DSS is 
described. Finally, the paper presents 
four major observations about the use of 
AI, OR, and DSS techniques to analyze 
the increasingly complex problems of the 
future. 
AI, OR, AND DSS 
This section briefly characterizes the 
fields of AI, OR, and DSS and examines- 
their fundamental similarities and 
differences. 
AI can be defined as "the study of ideas 
which enable computers to do things that 
make people seem intelligent." 113 AI 
means different things to different 
people. Natural language processing, 
robotics, and expert systems are the 
three major areas of AI. For the kinds 
of problems addressed in this paper 
expert systems (ES) are most applicable. 
An expert system is "an intelligent 
computer program that uses knowledge and 
inference procedures to solve problems 
that are difficult enough to require 
significant human expertise for their 
solution.*l [ z ]  
Expert systems attempt to capture highly 
specialized human expertise in limited 
problem domains. Unlike conventional 
computer programs, ES separate the 
deduction mechanism (inference engine) 
from the knowledge base, which contains 
both the facts and rules. ES also 
provide a friendly user interface as 
well as the capability to explain their 
reasoning and recommendations. ES can 
be viewed as a special class of models 
which assist with a variety of tasks 
including interpretation, prediction, 
diagnosis, design, planning, monitoring, 
debugging, repair, instruction, and 
control. [3] 
OR can be defined as "the application of 
the methods of science to complex 
287 
https://ntrs.nasa.gov/search.jsp?R=19880007861 2020-03-20T07:31:39+00:00Z
problems arising in the direction and 
management of large systems of men, 
machines, materials and money in 
industry, business, overnment, and 
defence. The distinctive approach is to 
develop a scientific model of the 
system, incorporating measurement of 
factors such as chance and risk, with 
which to predict and compare the 
outcomes of alternative decisions, 
strategies or controls.n [4] 
Operations research analysts 
traditionally use a wide range of 
mathematical models to help solve 
problems including mathematical 
programming, stochastic, simulation, and 
network models. For large problems the 
analyst traditionally works with a model 
providing input to the model and 
analyzing the resulting output. 
DSS can be viewed as a evolutionary 
advancement beyond Electronic Data 
Processing (EDP) and Management 
Information Systems (MIS). EDP focuses 
on the generation, storage, processing, 
and flow of data at the operational 
level within the organization. MIS 
places its emphasis on the information 
flow of middle management. The key idea 
behind DSSs is their focus on supporting 
the decision process. The DSS builder 
views a DSS as consisting of three major 
components--a data base, a model base 
and a dialogue component which 
integrates the other two components and 
the user. 
COMPARISON OF COMPUTER-BASED PROBLEM 
SOLVING APPROACHES 
This section compares the relationship 
of the various computer-based problem 
solving approaches with the decision 
maker (summarized in Figure 1). 
D P Y  
C 9 s - D - r  - I- - - -  Dtuw*pccomnnB) -*a I-- m a l  u ? t m ~ l ~  . I * m l  %2 -LlmY 
I-.-.- onh 
.r 
Figure 1 Summary Comparison of Computer- 
based Problem Solving Approaches 
Unlike the other approaches, DSS is 
usually applied to relatively 
unstructured or underspecified problems 
where it is not easy to directly model 
the values of the decision maker using 
an objective or value function. 
Instead, the decision maker's values are 
incorporated into the problem solution 
through the choices that the decision 
maker selects during operation of the 
DSS. Therefore, in the early Stages of 
a DSS evolution, the system will likely 
take on a strong data base orientation. 
EDP and MIS focus on efficiency, that 
is, accomplishing a specific task, such 
as processing a financial transaction, 
with a minimum amount of resources. 
Efficiency is an input-output measure. 
OR ha$ a dual focus--allocating scarce 
resources efficiently and providing 
insight to the decision maker. DSS and 
ES provide the decision maker with new 
capabilities. The novice can use an ES 
to extend his capabilities. Experience 
has shown that because of the 
flexibility of a DSS a user often 
discovers that he can solve problems 
that he had never considered before or 
that could not be solved using other 
solution techniques. 
The newer DSS and ES technologies allow 
the decision maker to interact directly 
with the system rather than relying on 
intermediaries such as a programmer or 
an operations research analyst. 
Particularly noteworthy is the 
capability of ES to make recommendations 
as well as furnish the decision maker 
with logical explanations to support 
these recommendations. This unique 
capability increases the credibility of 
the solutions generated. 
The development approach taken by DSS 
and ES is significantly different from 
those of EDP, MIS, and OR. Both the 
adaptive design and rapid prototyping 
approaches involve initiaJly selecting a 
small but significant problem. The 
design, development, and test phases are 
compressed into a few weeks and 
performed iteratively for a few months 
until a relatively stable system has 
emerged. Experience has shown that user 
requirements constantly change and, in 
reality, the system continues evolving 
until its retirement. The DSS adaptive 
design approach assumes that there 
exists an organizational commitment to 
field the system whereas it is not 
unusual to develop a 'throwaway' ES to 
demonstrate the feasibility of an ES 
technology. 
ADAPTIVE DESIGN METHODOLOGY FOR AI AND 
OR MODELING IN DSS 
This section describes our adaptive 
design methodology for AI and OR 
modeling in DSS. As mentioned earlier, 
a DSS has three components: the models, 
the data, and the dialogue (i.e., man 
machine interface). The. three 
components can best be thought of as the 
three legs of a stool. Like the stool, 
the DSS can not withstand an ineffective 
leg. 
288 
The interrelationships of the three DSS 
components are shown in Figure 2. The 
role of OR/= in DSS is well understood: 
however, the AI aspects require 
additional explanation. First, AI 
emphasizes new types of data (i.e., 
knowledge) and offers new knowledge 
semantic nets, frames, scripts, and 
rules. Second, AI offers new type of 
models, e.g., the cognitive models of 
human thought operationalized in an 
inference engine, for reasoning with the 
knowledge representation schemes. 
Third, AI shells and programming 
environments provide models for 
knowledge representation and MMI 
facilities. 
representation approaches, e.g. I 
7 [System Development )4 
I 
Figure 2. Decision Support System 
Not all problems require a computer- 
based DSS using AI and OR techniques. 
Our experience has shown that the types 
of problem domains that require our 
approach (the cross-hatched area in the 
center of Figure 2) are complex, dynamic 
problem domains where specialized 
(procedural or heuristic) knowledge 
significantly improves the quality of 
recurring decisions. In these domains 
there may be many decision-makers. 
The steps in our adaptive design 
methodology are summarized in Figure 3. 
These concepts have been developed and 
used by over 15 of our thesis students 
at AFIT over the past two years. We 
will focus on the first five steps, 
Components 
since these are unique to our approach. 
It is important to understand that our 
methodology is highly iterative: the 
results of any step may require redoing 
portions of one or more of the previous 
steps. 
1 Decision Process Analy 
2 
3 I Output-Driven Requirements ~ Analysisb 
4 
5 I Tool/Programming 
+ Kernels People 
.) Objectives Solution Quality 
I Current Task Performance Analysd- 
Requirements Matrix 
Environment 
6 
Figure 3. Adaptive Design Methodology 
for AI and OR Modeling in DSS 
The first step in the process is the 
analyis of the decision process. This 
step is the most crucial in the entire 
methodology: fundamental errors in 
understanding of the decision process 
can easily result in solving the wrong 
problem. The most useful concepts have 
come from the DSS literature. 
The major objective of the decision 
process analysis step is the 
identification of the kernel problems. 
We recommend focusing on the user's 
perspective and perf onning a 
technologically- unconstrained analysis 
of the decision process. A very useful 
technique is the concept map: a free- 
wheeling network, similar to a semantic 
net, that aids the analyst in capturing 
the major concepts and the cognitive 
processes of key decision-makers [ 5 ] .  
As an example, Figure 4 provides a 
concept map for determining the intent 
of an ICBM attack on the US [ 6 ] .  Two 
additional components, the feature chart 
[7] and storyboarding [ a ] ,  have also 
been used to capture the user's 
requirements using state-of-the-art 
graphics packages. This step concludes 
with the selection of the kernel 
problems in the decision process. The 
five kernals identified in Figure 4 are 
diplomatic, political, indications & 
warning, military, and economic. 
289 
Figure 4. Concept Map 
The second step in the methodology is 
the analysis of the current task 
performance for each of the kernel 
problems. We focus on the individuals 
involved, the objectives of each 
individual, and the desired quality of 
the solution. Figure 5 displays a 
matrix framework we have found useful. 
Tasks requiring an optimal solution 
suggest an OR model. Tasks where 
specialized knowledge is useful and a 
satisfactory solution is acceptable are 
candidates for AI techniques. 
Unstructured tasks with dynamic 
ob j ectives are candidates for 
conventional data base query techniques. 
Finally, tasks with no feasible 
solutions are candidates for an OR 
analysis. 
SENIOR I MAKER I EXPERT(S)  I USER M*N*GER(SlI DtCiSiON I 
MAJOR TASK 
TASK1 
TASK2 
TASK 3 
ThSK 4 
a 
c - w c a
a 
i I a 
0 
a 
0 .  0 u
2 t 
B u 
H 
z 
i 
s * 
f Nf 
w 
a 
S 0 
N* 
0 optlmal solutlon 
S: ratlsfactory soiutlon 3 speclalind knowlodge AI 
F: foarible solutlon 3 DSS candidate 
N E  no fearibb 801utIoIl 3 OR analyrlr candidate 
Figure 5. STEP 2: Current Task 
Performance Analysis 
290 
The third step in our adaptive design 
methodology is an output driven 
requirements analysis. The development 
of information systems requirements has 
been a major problem for MIS and DSS 
designers [ 9 ] .  users are unable to 
initially specify a complete set of the 
system requirements. Knowledge 
engineering focuses on capturing the 
knowledge of the experts but does not 
offer fundamentally new techniques for 
capturing system requirements. Like 
DSS, knowledge engineering makes 
extensive use of prototype knowledge 
systems to demonstrate the usefulness of 
AI in a problem domain. Our adaptive 
design approach synergistically combines 
AI and DSS concepts to use prototyping 
to capture the critical system 
requirements and provide a framework for 
the management of the adaptive design 
effort by focusing the prototype 
designer’s efforts on the system 
requirements of the operational DSS.  
Figure 6 provides our framework. for 
recording DSS system requirements. This 
framework is used throughout the 
adaptive design effort. The current 
method column comes from the previous 
step. The second column identifies the 
requirements that the current prototype 
can successfully accomplish. The third 
column is only used when the goal of the 
design effort is to develop a prototype 
to establish the feasibility of an 
operational DSS. The fourth column 
identifies the desired requirements for 
an operational system. For the reason$ 
discussed above, all four columns are 
iteratively developed during the 
adaptive design process. 
Figure 6. STEP 3: Desired Output 
Driven Requirements Analysis 
The left hand side of the matrix in 
Figure 6 identifies the types of 
knowledge or data the user would like 
displayed. We have found it useful to 
use storyboards to capture the users 
output requirements for each task. Many 
times we want our AI models to provide 
recommendations and explanations based 
on specialized knowledge in the DSS. 
The processes are the tasks analyzed in 
the previous step. In order for the 
processes to result in the desired 
outputs, static and dynamic inputs are 
required. The static inputs are the 
data and knowledge resident in the DSS. 
The dynamic inputs are provided 
interactively by the user or 
automatically by interfacing systems 
while the DSS is in use. Several AFIT 
thesis have successfully used this 
framework [10,11]. 
Step 4 in our methodology is the 
task/methods matching. Figure 7 
provides an example of adaptive design 
step for a three task scheduling 
problem. Seven possible solution 
methodologies (paths) are identified. 
Three are pure paths: path 1 is 
knowledge engineering, path 3 is OR, and 
path 6 is data base browsing. The other 
four are mixed methodologies. 
ALTERNATIVE METHODS 
Figure 7. STEP 4: Task/Methods 
Step 5 is the analysis of the prototype 
tool/programming environments. Figure 8 
provides a conceptual example for path 4 
of the problem described in the previous 
paragraph. Three alternative approaches 
are identified. Again, each of these 
approaches can be evaluated against 
specific criteria and the selected 
approach can be implemented by the DSS 
designer. Two important trends are 
worth noting in this step. First, many 
AI to-ols increasingly allow the 
programmer to 'interface with database 
programs and conventional languages. 
Second, many conventional hardware and 
software vendor8 are seeking ways to 
incorporate AI programs. 
Matching 
Figure 8. STEP 5: Prototype Tool/Pro- 
gramming Environment Select- 
ion 
Once these steps are completed Step 6 
(system design) and Step 7 (system 
development) proceed as in the mormal 
development of a conventional DSS. If 
AI techniques have been used, Step 8 
(system test and evaluation) may require 
AI test and evaluation techniques, 
e.g., the cases method. 
OBSERVATIONS 
Finally, we make four observations about 
the use of AI, OR and DSS techniques to 
analyze complex decision problems. 
First, most complex, dynamic problem 
domains require the adaptive design 
process to capture the system 
requirements and demonstrate the 
usefulness of a computer-based decison 
aid. Second, DSS provides the most 
useful techniques for the crucial 
decision process analysis step in our 
adaptive design process. Third, 
AI/OR/DSS tools appear to be converging. 
Finally, the effective use of the 
adaptive design process described 
requires an interdisiplinary education 
in AI, OR, and DSS. 
SUMMARY 
This paper developed a conceptual 
framework for integrating AI, OR, and 
DSS techniques. First, the fields of 
AI, OR, and DSS were defined and 
compared. Next, a comprehensive 
adaptive design methodology for AI and 
OR modeling within the context of a DSS 
was described. Finally, the paper 
presented four major observations about 
the use of AI, OR, and DSS techniques to 
analyze the increasingly complex 
problems of the future. 
29 1 
REFERENCES 
1. Headquarters United States Air Force 
Artificial Intelligence Research 8 
Development Master Plan, Pentagon, 
Washington DC, April 30, 1984. 
2. Harmon, Paul and King, David, Expert 
Systems: Artificial Intelligence in 
Business, Wiley Press, 1985, p. 5. 
3. For a list of major applications SeQ 
Donald A. Waterman, A Guide to Expert 
Systems, Addison-Wesley Publishing 
Company, 1986 and Bruce G. Buchanan, 
"Expert Systems: Working Systems and the 
Research Literature," Expert Systems, 
Vol. 3, No. 1, January 1986, pp. 37-51. 
4. Operational Research Quarterly, Vol. 
28, No. 3, i, 1977. 
5. Seagle, John P., and Belardo, 
Salvatore, @'The Feature Chart: A Tool 
for Communicating the Analysis of a 
Decision Support System1@, Information 
and Management, 1986, Vol 10, pp. 11-19. 
7. Bivins, Robert, Class Project in 
TACT 761, "Command and Control Decision 
Aiding", Winter Quarter, 1987. 
8. Andriole, Stephen J., Akey, Mark, 
Dunkelberger, Kirk, and Millis, Phillip 
J. , "Storyboarding for C2 Systems 
Design: A Combat Support System Case 
Study, Forthcoming paper to be presented 
at the 5th Annual Workshop on Command 
and Control Decision Aiding. 
9. Valusek, John R., Information 
Empirical Investigation of Obstacles 
within an Individual, Ph.D. 
Dissertation, University of Wisconsin- 
Madison, 1985. 
Requirements Determination: An 
10. Kennedy, Dale J . ,  A Prototype 
Expert System Advisor for Satellite Support Scheduling, AFIT Masters 
Thesis, .December, 1986. 
11. Phillips, Barbara A., 
Knowledge-Based System to 
System Restoration Management, AFIT 
Masters Thesis, December, 1986. 
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Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS): A conceptual framework

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