Adaptive Decision Support Systems Bijan Fazlollahi, Mihir A...

Fazlollahi, Parikh, Verma/ 1

Adaptive Decision Support Systems

Bijan Fazlollahi, Mihir A. Parikh, and Sameer Verma

Department of Decision SciencesCollege of Business Administration

Georgia State UniversityAtlanta GA 30303

Tel: (404) 651-4000Fax: (404) 651-3498

E-mail: [email protected]

Abstract

Effectiveness of Decision Support Systems (DSS’s) is enhanced through dynamic adaptation of

support to the needs of the decision maker, to the problem, and to the decision context. We

define this enhanced DSS’s as Adaptive Decision Support Systems (ADSS’s) and propose its

architecture. In an ADSS, the decision maker controls the decision process. However, the system

monitors the process to match support to the needs. The proposed architecture evolves from the

traditional DSS models and includes an additional intelligent “Adaptation” component. The

“Adaptation” component works with the traditional data, model, and interface components to

provide adaptive support. The architecture also integrates enhancements proposed in the past

research. In this paper, we have illustrated the proposed architecture with two examples, a

prototype system, and results from a preliminary empirical investigation.

Keywords: Decision Support Systems, Active decision support, Intelligent decision support,Adaptive support, DSS architecture, Cognitive support.

Fazlollahi, Parikh, Verma/ 2

Biographical Information

Bijan Fazlollahi is an Associate Professor of Decision Sciences at Georgia State University,

Atlanta, GA. His research is in the area of Decision Support Systems. He has published over 30

articles in various journals and proceedings including Journal of Information Systems Research,

Interfaces, and Information and Management. He is on the Editorial Board of the Journal of

Database Management. He is a former Fulbright Scholar.

Mihir A. Parikh is a doctoral candidate in Decision Sciences at Georgia State University. He has

received a Bachelor of Mechanical Engineering from Gujarat University, India and a Master of

Business Administration from Georgia State University. His current research interests are in the

areas of decision support and end-user systems, and applications of emerging information

technologies (e.g., neural networks, fuzzy logic, genetic algorithms, multimedia) in decision-

making and business training.

Sameer Verma is a doctoral student in Decision Sciences at Georgia State University, Atlanta

GA. He has a Master of Science in Decision Sciences from Georgia State University, and a

Bachelor of Engineering from Osmania University, Hyderabad, India. His areas of interest and

work include intelligent decision support systems, education support systems, business training

systems and strategic management through decision support and guidance. His focus is on the

implementation of these systems through cognitive style research, using hypermedia / multimedia

technologies.

Fazlollahi, et. al./ 3

Adaptive Decision Support Systems

Abstract

Effectiveness of Decision Support Systems (DSS’s) is enhanced through dynamic

adaptation of support to the needs of the decision maker, to the problem, and to the

decision context. We define this enhanced DSS’s as Adaptive Decision Support Systems

(ADSS’s) and propose its architecture. In an ADSS, the decision maker controls the

decision process. However, the system monitors the process to match support to the

needs. The proposed architecture evolves from the traditional DSS models and includes

an additional intelligent “Adaptation” component. The “Adaptation” component works

with the traditional data, model, and interface components to provide adaptive support.

The architecture also integrates enhancements proposed in the past research. In this

paper, we have illustrated the proposed architecture with two examples, a prototype, and

results from a preliminary empirical investigation.

1. Introduction

DSS’s have benefited from advances in software and hardware technology. The data,

model and interface components of DSS’s are now much more sophisticated and powerful

than they were two decades ago. The databases are larger, more current and easier to

query and search, the models are more complex reflecting reality, and the interfaces are

much more user-friendly. However, the evolution has been in the direction of building a


DSS to provide more effective support for the low level cognitive tasks such as, data

storage & retrieval, data drilling, manipulation, and consistency checking [19]. Little has

been done in developing DSS’s that provide support for the high level cognitive tasks such

as, framing of problems, alternative generation [17], making tradeoffs involved in

preferences, and handling incomplete information, misinformation, and uncertainty. These

high level cognitive tasks involve human mental activities of reasoning, learning, and idea

generation requiring human judgmental inputs.

A primary objective of DSS’s is to help the decision-maker make effective decisions by

identifying what should be done and ensure that the chosen criterion is relevant [7].

Provision of support for the high level cognitive tasks (i.e. the high level cognitive

support) can strengthen the capabilities for achieving the objective. This type of support

extends the limits of “bounded” rationality by promoting improved understanding, better

insights, and more extensive analysis [7,16, 27, 28]. The high level cognitive support is

analogous to referring the decision-making tasks to human staff assistants and staff

advisors. Normally, a staff assistant makes efforts to understand the changing

requirements of the task, the needs of the decision-maker, and the best way to support the

particular decision-maker. For this, the staff assistant constantly monitors the current

status of the task, provides interim reports, and is sensitive to the needs and the

peculiarities of the decision-maker and the context in which the decision is made. The high

level cognitive support adds to the functionality of DSS’s, especially for situations with

complex problems and expert decision-makers. As an example of the added functionality;


some of the tasks may be delegated to the intelligent agents [26]. The intelligent agents

accomplish the tasks on their own initiatives while interacting with the decision-maker and

the decision environment. However, the agents operate within the user control philosophy

of DSS’s [14] where the decision-maker exercises human judgement and provides

judgmental inputs.

The purpose of this paper is to propose an enhanced DSS, adaptive decision support

system (ADSS), which provides the high level cognitive support adapted to the needs of

the user, the decision task characteristics, and the decision context. The paper reviews

past research and discusses ADSS’s as a prescription to the unaddressed requirements of

complex decision-making situations. It proposes an architecture that identifies and

incorporates key components for designing and developing an ADSS. It illustrates the

architecture through building and using a prototype ADSS including results from a

preliminary empirical investigation. Finally, it provides a summary of observations and

recommendations for future directions of research in ADSS’s.

2. Background

DSS’s have evolved to provide more effective support for decision-making. The factors

influencing DSS evolution include (1) the discovery of structure in some judgmental tasks

and then assigning the task to the computer, and (2) improvements in technology allowing

the computer to do more tasks. Keen and Stable as far back as in 1978 foresaw that


decision support may be achieved by exploitation of many technologies [7]. Modern

database technology, graphical user interface, hypermedia, multimedia, expert systems,

neural networks, fuzzy logic, genetic algorithms, distributed systems, client-server, object-

oriented approach are examples of recent technologies that can carry out prescriptions

which were not feasible in 1978. In recent years, some of the emerging technologies have

been used in providing the high level cognitive support. Research in the area of high level

cognitive support is labeled active decision support, inductive learning, decisional

guidance, and adaptive interface.

Manheim [10] suggests active DSS’s which have few features which can provide the high

level cognitive support. These features include:

< maintaining an explicit representation of the decision-maker's conceptual problem-

solving model and using it to guide support activities;

< providing tools for supporting the "natural heuristics", such as "do the easy things

right away" as well as tools for rational model-type such as linear programing and

break-even analysis model; and

< providing tools to enhance the user's ability to balance strategic (global and long

term) and opportunistic (local and short term) thinking.

The active DSS’s are capable of active participation in the decision-making processes. The

systems operate almost independent of explicit directions from the users and provide

support which the users may find helpful [8, 20]. Raghavan [21] identifies support features


of the active DSS’s as monitoring the user activities, making inferences, and conducting

appropriate activities such as alerting, engaging in an insightful conversation, or

automatically carrying out certain tasks. The active DSS’s aim at improving the decision-

making effectiveness through stimulating creative ideas, criticizing choices, and guiding

decision structuring.

The active DSS’s complement users’ problem solving abilities in the application domain

[20]. The DSS’s use alternative models of the problem solving processes, ask the users to

make choices at the intermediate stages allowing the users to determine the problem

solving paths, and maintain updated models of the user problem-solving processes. They

support the users in a number of forms such as suggesting alternative actions and

indicating issues that the users may have overlooked. Rao [20] concludes that the active

DSS’s should be designed as knowledge-based systems.

Piramuthu, et. al. [18] describes an adaptive DSS for real-time scheduling of a flexible

manufacturing system. The DSS dynamically identifies a pattern in the scheduling

environment and matches an appropriate scheduling heuristic rule to the task. The system

architecture includes a “learning and refining” module capable of simulation and inductive

learning for acquisition and refinement of heuristics. The module interacts with the

knowledge-base to provide adaptive scheduling.

Holsapple, et. al. [4] describe an adaptive DSS which utilizes unsupervised inductive


learning, a learning through observation and discovery, to acquire problem processing

knowledge for machine learning. The DSS refines the problem processing knowledge to

match the existing conditions.

Holsapple, et. al. [4] summarize the relationship among DSS paradigms based on two

problem processor related factors of active/reactive and adaptive/non-adaptive. The

traditional DSS’s are labeled as non-adaptive and reactive. They suggest that the focus of

research should be on adaptive and active DSS’s. These are the systems where problem

processors acquire and eliminate knowledge through unsupervised learning and are largely

self-driven. The research is concerned more with learning abilities that improve the

problem processing behavior of a DSS [4].

Silver [25] proposes “decisional guidance” as an enhancement to the DSS’s. The

decisional guidance enlightens or sways its users as they structure and execute their

decision making processes and thus provide meta-support for judgmental activities. The

guidance is implemented in the form of help facilities [12] or embedded intelligence that

inform and advise users. The objective of the decisional guidance is more effective use of

DSS’s leading to more effective decision making.

Several researchers propose adaptive interface, user-controlled or self-adaptive, to allow

for the differences in the users and to enhance DSS’s quality and effectiveness [2, 24].

Adaptability of interface ensures that the system provides flexibility to satisfy the different


users' cognitive styles, the users’ experience level, and different decision approaches.

Thus, adaptable interface allows a DSS to provide ease of learning and user control.

Dutta [3] proposes additional intelligent components to a planning DSS so that the system

can adapt to the changing task requirements. He identifies a need for support in

monitoring, replanning, and managing interdependencies among different temporally

separated actions in the iterative process of planning. The DSS monitors the environment,

handles uncertain and incomplete information, and interprets and integrates conflicting

output from different models and view points.

Although the enhancements proposed in the past research provide the high level cognitive

support through increased DSS functionalities, the research and development in the area

of providing high level cognitive support is fragmented and technologically oriented. The

methodology for providing the support is yet in the infant stage [19]. In particular, there

are no frameworks to guide the identification of the necessary enhancements and addition

of functionalities to the DSS’s that would provide the high level cognitive support. In the

following section, we propose an adaptive decision support system (ADSS) that

incorporates different ideas regarding extensions and enhancements to the traditional

DSS’s for providing the high level cognitive support.

3. Adaptive decision support systems


We define ADSS’s as:

“DSS’s that support human decision making judgements by adapting support to

the high level cognitive needs of the users, task characteristics, and decision

contexts.”

ADSS’s are enhanced DSS’s with an objective to improve the effectiveness of the

decision-maker in performing tasks requiring high degree of human judgement such as

framing problems, generating alternatives, making tradeoffs, and handling equivocality and

uncertainty. ADSS’s due to their emphasis on the high level cognitive support will also

improve user learning and understanding of the decision-making process and the domain

knowledge.

ADSS’s, unlike traditional DSS’s which are adaptive systems only through evolution

[29], are adaptive through adjustments to the skill level and changing needs of the

decision-maker during the decision making process. The decision maker learns through

interaction with the ADSS’s [7]. The learning leads to changes in problem-solving

expertise and support needs. ADSS’s provide support that fits the user's current needs.

Also, the progress through the intelligent, design and choice phases in a dynamic decision

environment leads to changing problem-solving task. ADSS’s adapt to the changing

problem solving model and provide support for the appropriate tasks. Furthermore,

ADSS’s adapt to the decision contexts such as organizational structure. For example, a

decision in a matrix structure would require more coordination with other decision makers


than in a hierarchical structure. In such situations, the support must also provide

mechanism for coordination of decisions. Matching support to the decision-maker, the

decision problem, and the decision context substantially helps the decision-maker to make

effective decisions [7].

Adaptation is achieved by matching support needs with the system support. The support

needs of the user are determined by monitoring the user performance and support history.

The support needs of the task and the contexts are identified through monitoring the

decision process and selecting the appropriate models. ADSS’s monitor the decision-

making process, diagnose problems/ opportunities, and design and implement

interventions. Such abilities rest on having knowledge of the specific user, the problem

domain, an expert model of the decision process, and strategies for intervention. As the

support needs change, the systems dynamically change their support to match the current

needs. Dynamic adaptation enables ADSS’s to better address learning, interaction,

support, and evolution-the key words in the DSS definition [7].

ADSS’s use intelligent technologies to determine the support needs and may provide an

active rather than a passive participation in the decision making process [8]. The active

participation includes performing tasks such as finding patterns in data, selecting

appropriate models, or acting as critiquing agents [13, 26]. It further means that the

user/DSS interaction (a two-way communication) is established with the decision-maker

controlling the process, similar to in the case of a decision-maker with a human staff


assistant.

4. ADSS Architecture

The architecture of decision support systems was first proposed by Sprague and Carlson

[30] as a macro architectural model with three components data, model, and interface.

Later, Turban [32] revised this model and added expert systems/knowledge-base

component to the model. Other researchers [3, 8,9, 10, 11, 23, 24, 25, 31] have

proposed enhanced architectures to encompass particular functionalities not specifically

identified in the original macro model.

********** Insert Figure 1 about here ***********

Figure 1 shows the proposed architecture for ADSS’s. The architecture is an evolution of

the Sprague & Carlson model [30]. In addition to the three; data, model, and interface

components, of the traditional DSS’s, ADSS’s have an “Adaptation” component. The

adaptation component is integrated with the other three components to generate and

provide adaptive support.

ADSS’s have three subsystems: user diagnosis, problem solving, and guidance/instruction.

Each subsystem incorporates data, model and adaptation component. The user diagnosis

subsystem includes information regarding what the user knows and what support the


system has already communicated to the user. The problem solving subsystem includes the

model derived from a theory or stated by the user for appropriately solving the problem.

ADSS’s do not require the general model of human problem solving processes to guide

their automatic intervention in the decision-making processes. Instead the more attainable

descriptive models of specific tasks are used to guide some of the activities of ADSS’s.

The guidance/instruction subsystem includes knowledge about how to intervene in the

decision making processes. The ADSS architecture addresses the functionalities of

ADSS’s which are (1) to monitor the decision-makers, the decision-making tasks and the

decision contexts, (2) to make inferences on the basis of descriptive models, and (3) to

intervene at the discretion of the decision-maker to provide decision support.

Two examples are selected from management science and personal finance domain to

explain the architecture. Example A refers to an ADSS used for selecting appropriate

forecasting model for a given historical data. This decision-making situation is structured

with well-defined statistical models and quantitative methods to identify which forecasting

model is more suitable. Example B refers to an ADSS used for determining appropriate

allocation of assets for an individual investor. The process of asset allocation is dependent

on subjective variables such as the degrees of risk preference, time horizons, and financial

conditions of the investor. This decision-making situation is unstructured as there is a lack

of quantitative models which incorporate the individual investor’s characteristics and

concerns in performing asset allocation.


The components of each ADSS are described in details and are illustrated with examples

A and B in the following sub-sections:

Data: This component is similar to the data component of the Sprague & Carlson model

[20]. It stores raw data about the problems, the concepts and procedures, and the user

history. It has three sub-components: Problem, concept/procedure, and user history.

Problem: This sub-component stores raw data about the problem or decision at

hand. The details of the problem can be obtained from this database and presented

to the user of the system. This sub-component can be an external database

dynamically linked to the other components of the system.

Example A:Problem: Forecasting sales for year 1997Data (historical) : Year Sales

1985 $5,234,667.001986 $8,342,235.00.....................................................................1995 $41,564,982.001996 $59,002,538.00

Example B:Problem: Investment asset allocation for a household with four

members- husband, wife, two young children.Data: Household Income : $60,000 per year

Major expenses: Rent+utilities $1,000Auto loans - $600Other - $2,000

Major financial goals:Retirement - $400,000 in 30 yearsEducation for the children - $50,000 in 14years Buying a house - $25,000 in 2 years


Concept/Procedure: This sub-component stores information on the concepts and

procedures related to the domain knowledge area. This sub-component can also be

dynamically linked with an external concept/procedure library.

Example A: Domain knowledge: Time-series forecasting Model: Linear forecastingConcepts:

what a linear forecasting model is; when it should be used; theunderlying assumptions of the model; the advantages anddisadvantages of using this model.

Procedures:how to find the coefficients of the independent variables; how toimport data into the model.

Example B: Domain knowledge: Personal finance and investmentsConcepts:

What stocks, bonds, or money market funds are; what anemergency fund is; what a portfolio is; risk and return of aportfolio.

Procedures:How to determine portfolio risk; how to determine portfolio return.

User History: Before the user uses the system, the system performs a diagnostic

test and determines the knowledge level of the user in the concepts and

procedures involved in the decision-making process. These diagnostic data are

stored in this component. When the user uses the system, the system continuously

monitors actions of the user. This sub-component stores the sequential historical

actions and interactions such as performance of the user, type of help the user

requested, and time it took the user to solve the problem. Example A:User: Jeff Jones, Performance history: Outcome Time

Problem 1: Right 5 min.Problem 2: Right 7 min.Problem 3: Wrong 3 min.Problem 4: Right 2 min.


Support history: Concept: Linear model,

Exponential Smoothing model, Procedure: Doing a square of differences,

Finding a coefficient for independent variable.

Example B:Performance history (alternate portfolio developed by the user):

Iteration 1: Stocks-30%, Bonds-20%, Cash-50%Iteration 2: Stocks-35%, Bonds-35%, Cash-30%Iteration 3: Stocks-50%, Bonds-40%, Cash-10%Iteration 4: Stocks-50%, Bonds-30%, Cash-20%

Support history:Iteration 1: The user did not ask for supportIteration 2: The user asked for support on:

Concepts: Returns on stocks, risk involved in the CashInvestmentsProcedure: How to determine portfolio return

Iteration 3: The user asked for support on:Concepts: Portfolio evaluation, risk involved in stocksProcedure: How to determine portfolio risk

Iteration 4: The user did not ask for support

Models: This component stores models and knowledge about problem solving,

guidance/instruction, and user diagnosis in three sub-components: Problem solving model,

guidance/instruction model, and user diagnosis model.

Problem Solving Model: This sub-component stores descriptive problem-solving

models for different problems. When a problem is presented, this sub-component

has the knowledge and the models for identifying and solving the problem. This

sub-component has two parts: Associated concepts and associated procedures.

Associated concepts include the models about the identifying concepts involved in

solving the problem. Associated procedures have the models about identifying the


procedures involved in solving the problem.

Example A:Associated concept: If the historical data shows a linear trend with no

seasonality and low fluctuations, then simple linearregression model should be used.

Associated procedure: To use the linear model: (1) find the coefficient, (2)find the intercept, (3) select a future time period toforecast, (4) use the developed model to find thenew level of the dependent variable for the futuretime period.

Example B:Associated concept: If the risk preference is high, time horizon is long,

and financial condition is stable, allocate a largerpart of the portfolio to stocks.

Associated procedure: To determine suitability of the portfolio in terms ofrisk, identify risk preference of the user and matchthat with the risk of the portfolio.

Guidance/Instruction Model: This sub-component has models about presentation

of the concepts and the procedures. It has models to determine when, how, and at

what level a concept or a procedure should be presented.

Example A:Problem: In case of the linear regression model, present the concepts and

procedures related only to the linear model and not otherforecasting models.

User: If the user knowledge is strong in linear model concept, give onlybrief conceptual information.If the user knowledge is weak in linear model concept, give detailedconceptual information about the model along with examples/non-examples of the model.

Example B:Problem: In case of the portfolio return, present only the concept and

procedures involved in determining portfolio return.User: If the user knowledge is strong in the concept of determining

portfolio return but weak in calculating portfolio return, then givebrief conceptual information and detailed step-by-step procedure


for calculating portfolio return.

User Diagnosis Model: This sub-component stores information about how to

interpret the user history. This sub-component has models and rules needed to

interpret the conceptual and the procedural knowledge levels of the user and

determine the level of expertise and support needs.

Example A:Concepts: If the user does not know what a trend is, the difference between

high and low fluctuation, and what seasonality is, then the userknowledge in linear model is weak;If at least two (of the above three) concepts are clear, then the userknowledge in linear model is average;If all the concepts are clear, then the user knowledge in linearmodel is strong.

Procedure: If historically the user was never able to perform this task, then theuser knowledge is weak in finding coefficient of independentvariables ; If the user was able to perform the task successfully half of thetime, then the user knowledge is average in the procedure;If the user was able to perform the task successfully most of thetime, then the user knowledge is strong in the procedure.

Example B:Concepts: If the user portfolio does not match with optimal portfolio given

user’s risk preference, time horizon, and financial condition, he isconceptually weak in understanding one or more of these threedimensions. The degree of weakness can be determined by the sizeof the difference in the portfolios.

Procedure: If the user has never been able to perform the procedure ofdetermining portfolio return, then the user is weak in the procedure.If the user has determined the portfolio return successfully abouthalf of the time, then the user is average in the procedure.

Adaptation: This component integrates the sub-components of the data and model

components to infer about the adaptive support. The component has three sub-

components: Expert problem solving evaluation, user performance evaluation, and


guidance.

Expert Problem Solving Evaluation: This sub-component represents expert's

evaluation of the problem and problem solving knowledge. It matches the problem

sub-component from the data component and the problem solving model sub-

component from the model component and determines the concepts and

procedures related to the problem. It creates a dynamic task profile of the

associated concepts and procedures for solving the given problem. For example,

Let's say problem T11 is selected from the problem sub-component of thedata component. The expert problem solving evaluation sub-componentidentifies the key features of the problem T11, (such as linear trend, lowfluctuations, no seasonality for the example A and low risk preference,short time horizon for the example B). Then, it uses the models from theproblem solving model sub-component of the model component anddetermines the associated concepts and the associated procedures as shownbelow:

Problem: T11.Associated concepts: C1 and C8Associated procedures: P2, P6, P8 and P14.

User Performance Evaluation: This sub-component evaluates the performance

level of the user and develops a dynamic user performance profile for both

concepts and procedures. As the user uses the system, the history of interaction is

recorded in the user history sub-component of the data component. The user

performance evaluation sub-component, uses the user history and interprets the

knowledge of the user based on the user diagnosis knowledge. For example,

Let's say the user is Jeff Jones and has used the system for quite sometimeand the system has accumulated a history of interaction during this time.


The user performance evaluation sub-component matches the interactionsrecorded in the user history sub-component of the data components withthe models form the user diagnosis model of the model component. Fromthe comparison it determines a user profile indicating Jeff’s degree ofknowledge of concepts and procedures as shown below:

User: Jeff Jones. Concepts: Weak-C1, C3 and C7;

Average- C2, C4 and C5; Strong- C6 and C8.

Procedure: Weak-P1, P2, P9 and P14; Average-P3, P5, P6, P7, P8, and P10; Strong-P4, P11, P12, and P13.

Guidance: This sub-component compares the task profile from the expert problem

solving evaluation sub-component with the user profile from the user performance

evaluation sub-component. It compares knowledge required for an associated

concept with user's knowledge in the concept and determines the concept

differences ()C). It also compares the proficiency required to preform an

associated procedure with the user's proficiency in performing the procedure and

determines the procedure differences ()P). Based on )C and )P, this sub-

component determines which concepts and procedures should be presented and at

what level. For example,

Jeff Jones is given the problem T11. This problem has C1 and C8 asassociated concepts and P2, P6, P8 and P14 as associated procedures. Jeffis weak in concept C1 and procedures P2 and P14, average in proceduresP6 and P8, and strong in concept C8. So, the guidance sub-componentdetermines to provide Jeff the detailed information along with examplesand non-examples of C1, P2 and P14, the detailed information about P6and P8, and only the brief information about C8.

Problem: T11.Associated concepts: C1 and C8Associated procedures: P2, P6, P8 and P14.


User: Jeff JonesConcepts: Weak-C1, C3 and C7;

Average- C2, C4 and C5; Strong- C6 and C8.

Procedure: Weak-P1, P2, P9 and P14; Average-P3, P5, P6, P7, P8, and P10; Strong-P4, P11, P12, and P13.

Prescription:Concepts: C1 - Detailed information along with examples

and non-examplesC8 - Brief information

Procedures: P2 - Detailed information along with examplesand non-examples

P6 - Detailed informationP8 - Detailed informationP14 - Detailed information along with examples

and non-examples.

User Interface (dialogue): The user interface component is the link between the user

and the system. This component is the one that is seen and used directly by the user, so the

user may think that this is the system. It is a self-adaptive interface that automatically

adjusts to the users' preferences and tasks, and provides the functionality and form

required to match the interface to a specific user performing a specific task. Self-adaptive

interface promotes ease-of-use and consistency of features in the interface that are

important factors in establishing usefulness and success of the system.

5. ADSS Prototype

Problem Description: We used the exploratory system development process

methodology [21] to investigate the proposed architecture with a prototype system. We

selected forecasting, specifically data analysis and model selection, as the area of domain


knowledge. In this prototype system, the user is provided with the sales data plotted

against time and asked to examine the plot and select the most appropriate forecasting

model to predict future sales. The system presents four cases/problems (labeled A, B, C,

and D) of sales data, each of the problems requires different forecasting model. The user

examines the cases one at a time in a sequential and irreversible order, and selects an

appropriate forecasting model for each case. In solving the problems, the user can access

information about the data and the models pertinent to forecasting. The information may

help in analyzing the data plot and selecting an appropriate model. In this prototype, the

user's history of interactions in solving each case is stored to or retrieved from a database.

Prototype Description: We developed the system, by mapping the conceptual

components of the architecture to different files, programs and other features in

KnowledgePro software package. KnowledgePro is an environment that supports rapid

prototyping in rule-based programming for expert systems. The software allows reading

and writing to a variety of file and graphic formats. An add-on package called KPWIN ++

generates C++ code for the KnowledgePro-based programs and compiles them into run-

time executable files.

Physical representation: As described in the architecture, the system consists of the data,

model, adaptation and user interface components. Each component is divided into sub-

components.


Data: This component consists of the problem, the concepts/procedures, and the

user history sub-components. It has data in the form of independent data files and

random access memory (temporal data).

Problem

The problem data are loaded by runtime programs from an independent file on a

disk. The data are presented to the user in a graphical format (bitmap) as a time

series plot which the user has to analyze.

Concept/Procedure

The concepts and procedures are assembled in text and graphics formats, in

accordance with the problem type and the problem solving stage requirements.

They are stored in files on a disk.

User History

This sub-component deals with temporal data. However, to maintain a cumulative

user profile, the data from the random access memory is dumped to a trace (ASCII

text/database) file, after every significant event. This file contains data regarding

navigation, time stamping, results, performance, etc. In every new session, the

trace file from the previous sessions of the user is accessed to adjust for the

previously learned concepts and procedures.

Model: This component consists of rule-based programs (executables), which store

the various models used by the system. The model component encapsulates three

sub-components: the problem solving model, the guidance/instruction model, and


the user diagnosis.

Problem Solving Model

This sub-component contains the problem solving models, represented through

associated concepts and associated procedures. We have modeled this knowledge

by programming in Knowledge Pro's rule-based expert system shell.

Guidance/Instruction Model

This sub-component is represented by the models that determine the format of the

presentation of the concepts and procedures that the user may require. The

inference is based on the performance of the user.

User Diagnosis Model

This sub-component has rules that diagnose and interpret the user history for

determining the strengths and weaknesses of the user in the domain knowledge.

Adaptation: This component has three sub-components. All sub-components are

exclusively rule based. The expert problem/solving evaluation sub-component

associates the problem file name with the problem solving knowledge rule block.

After comparing the problem and the expert's opinion, the sub-component

determines the expert's representation of the required concepts (C ) and theE

procedures (P ). The user performance evaluation sub-component examines userE

history from the trace file and the user diagnosis knowledge. Using the two, this

sub-component determines the concepts (C ) reviewed and procedure (P )U U

performed by the user. All these values are stored as temporal data in the RAM.


Finally, the guidance sub-component compares the inferences from the expert

problem solving evaluation sub-component and the user performance evaluation

sub-component, and generates the deviations for concepts ()C) and procedure

()P). The guidance sub-component determines which and in what format the

concepts and procedures need to be presented. The concepts and procedures are

obtained from the data component's concept/procedure sub-component, and the

presentation format is obtained from the model component’s guidance/instruction

sub-component. The system bases its inferences of formats and concepts on the

user profile and present user performance ()C and )P). In the prototype system,

the outcome for each of the four cases can be either right or wrong. Therefore, as

more information is gathered, the decision tree develops more branches. As an

example, trees for cases A and B are shown in figures 2 and 3 respectively.



For case A (Figure 2), the system uses the user history, which indicates that the

user could have been right or wrong about case A type of problems in previous

sessions. The outcome here refers to concepts and the format of presentation.

Therefore, while the user is in case A, he/she can get one of the two types of


outcomes. As the system proceeds to case B (Figure 3), the user's performance

profile has changed. It now consists of not only the performance in previous

sessions, but also of the performance on case A. This information is used in

conjunction with the user's history. In case B, the outcomes increase to four. This

is due to the number of combinations. It must be noted, that the user's performance

is a cumulative variable. We have used this approach to decrease the search time

for outcomes, and to make the process of concept retrieval and presentation more

efficient. Based on the inferences made by the guidance/instruction sub-

component, the concepts are presented to the user through the user interface.

Now for case C, we will have updated user history and the performance in case B.

User Interface: The user interface is designed in Microsoft Windows 3.1. By using

windows and buttons, the system provides the ease of navigation. The hypertext is

constrained to prevent the user from getting lost in hyperspace. For example, while

using the system, only the relevant hot regions are activated. These controls are

coded using KnowledgePro's event based topic controls.

Sample Session and Explanation: The user begins by registration. Registration allows the

system to retrieve the user's history from the database. Then the user is presented with an

information screen containing all the relevant and required information for using the

system. After reading the instructions, the user continues by clicking on the Continue

button. The system is designed to keep control over decision making process with the


user.



The next screen consists of two windows (shown in figures 4 and 5), which show the data

plot (Figure 5) for case A, and a menu pad (Figure 4) for access to guidance/instruction,

and for model selection. The user can click on any of the specific information buttons to

get extended guidance/instruction on those models, in the context of the present problem.

For example, by clicking on the "Information" button on "Least Squares Regression", the

information provided consists of textual information on least squares regression, and a

suggestion about the data. The screen also displays the current plot . When the user

selects the least squares model, the system provides feedback, indicating that the choice

was wrong. The system automatically proceeds to case B. This time the user selects

information option for Least Squares Regression. The screen looks different this time.

This is due to the fact that the user chose the wrong model for case A. Therefore the

guidance is more extended. A comparison of the two information screens is shown in

figures for case A and case B.




Comparing the figures 6 and 7, we see that in addition to the information in Case A

(Figure 6), the figure for case B (Figure 7) provides a comparison of example and non-

example. The user selects the least squares model this time. The feedback from the system

indicates the choice to be correct. The system proceeds to case C (Figure 8). In case C, by

clicking on the information option for "Least Squares", it displays a third format. In this

format the information includes three plots illustrating the key factors in forecasting. The

reasoning for this format is based on the performance of the user on previous sessions

(user history) and the performance on cases A and B (i.e. user history, and present user

performance).



The user selects exponential smoothing model. The system indicates that this model is

wrong. At this point, the user has two wrong and one correct choice. The system

continues to case D (Figure 9). Finally, case D reveals information on moving average


model, which explains the modeling technique, shows a data plot, provides graphical

comparisons, and indicates strongly that the user should not use moving average("So you

should not use moving average model..."). Comparing the figures for case C and case D,

we see that the graphical information and text are different. The suggestion also is

different, with a more emphatic statement in case D.

Hence, the system adapts to the user’s history from previous sessions, and makes fine

adjustments as the user goes from one problem to the other.

6. Results from a Preliminary Empirical Investigation:

We used the prototype discussed in the previous section to conduct a preliminary

empirical investigation in a laboratory environment. The study and the results are reported

in detail in other publications and conclusions are briefly discussed in the following1

paragraphs. One hundred and thirty five subjects participated in the study. The preliminary

results from the experiment show that the meta-support in DSS’s increases decision-

making performance, learning and satisfaction of the users. The study examined decisional,

instructional and cognitive aspects related to the support provided by the system.

On the decisional aspects, the decision quality improved, however, the decision time

increased. The reason for the increase in the decision time is that the user spends more

time using the DSS facility to explore more alternatives and increase understanding. The

users with dynamic guidance, where the message content is tailored to the user’s needs,


performed better than the users with predefined guidance, where the users got “canned” or

pre-determined messages. The users with suggestive guidance, where the system provides

recommendation, did not perform better than the users with informative guidance, where

the system provides detailed information without any recommendations. [Refer to Pub. 11

for details].

On the instructional aspects, the users of the DSS with guidance learned significantly more

than the users of the DSS without guidance. In addition, the users of the DSS with

guidance were more satisfied with the overall process of decision-making than the users of

the DSS without guidance. Also, the users with dynamic guidance learned more and were

more satisfied with the process than the users with predefined guidance. Furthermore, the

users with informative guidance learned more than the users with suggestive guidance.

However, both groups were equally satisfied with the process [Refer to Pub. 2 for1

details].

On the cognitive aspects, the influence of guidance on the user depended on the user’s

cognitive styles. We used Jung’s Psychological Types with Myers-Briggs Type Indicators

to determine cognitive styles of the users. The users with Sensing-Dominant and Thinking-

Auxiliary (ST) type performed better than the users with Intuition-Dominant and

Thinking-Auxiliary(IT) type. Furthermore, the ST users with dynamic guidance performed

better than the ST users with predefined guidance. However, the ST users with suggestive

guidance did not perform better than the ST users with informative guidance [Refer to


Pub. 3 for details].1

7. Summary and Conclusion

Traditionally, DSS’s have provided support for low level cognitive tasks. To increase

decision support effectiveness of DSS’s, there is a need to support high level cognitive

tasks which require human mental activities of reasoning and learning. In this paper, we

propose ADSS’s which provide support for high level cognitive tasks by dynamically

adapting system support to the knowledge level of the user, the decision task

characteristics, and the context in which the decision is made. ADSS’s are a result of

integration of research in the field of decision support systems, cognitive science, and

artificial intelligence.

ADSS’s monitor the problem solving processes and the human decision-maker to

determine the support needs for making the judgmental inputs. The systems determine the

gaps in the conceptual and procedural knowledge of the user for performing the given

decision task. Based on the gaps, the systems determine the support needs and customize

the support to match the needs.

ADSS’s have, in addition to the data, model and user interface components of the

traditional DSS’s, an "adaptation" component. The adaptation component uses artificial

intelligence techniques to identify user needs and problem solving model, and matches


them with appropriate level of support.

We developed a prototype ADSS with decisional guidance capabilities to support the

decision-maker in selecting an appropriate forecasting model for a given set of data.

Selection of the appropriate forecasting model required user judgement as to which

forecasting model was more appropriate. The prototype system provided guidance for

making the judgement, the guidance was matched to the particular user’s needs and the

decision task on hand. A preliminary empirical investigation was conducted using the

prototype. The investigation was designed on the basis of a research practice where the

researchers invent and test ways for improving decision-making effectiveness. The results

of the investigation show that the guidance for judgmental inputs improves decision

quality, user learning, and user satisfaction.

This study provides an architecture to integrate the currently disparate and fragmented

research efforts. By applying this architecture in the development of real world DSS’s,

decision-support effectiveness of the DSS’s could be increased.

ADSS’s can be applied to other areas of decision making. We anticipate their usefulness

will be optimal in the areas, (1) where the task environment is unstructured requiring more

judgmental inputs from the decision-maker and (2) where the impact of the decision is

high, such as strategic management and crisis management.


In strategic management and planning, top management has to develop comprehensive

strategies to cope with the instability, uncertainty, and complexity of the environment.

This requires sophisticated and comprehensive understanding of the internal and the

external factors to develop strategic plans for long term direction of the establishment.

While a traditional DSS does not adequately support tasks like problem formulation and

problem structuring, an ADSS can provide support for high level cognitive tasks such as

setting goals and objectives, evaluation of alternative strategies, and stakeholder analysis

[15].

In crisis management situation, a tendency is to consider a limited number of alternatives

and quickly reach a decision. The limited analysis reduces the decision quality by rejecting

a correct course of action, accepting a wrong solution to the problem, solving the wrong

problem, and solving the right problem correctly but too late [22]. ADSS’s can support

the decision-making process by supporting generation and evaluation of more alternatives,

identifying objectives, and evaluating the consequences.

Although we performed a preliminary investigation of the architecture using a prototype

with decisional guidance capabilities, there is a need for further research. We suggest

ADSS research in the following areas:

1. Investigate ADSS’s for unstructured and complex decision-making situations.

2. Develop and test alternative strategies for adaptive support.

3. Study the impact of adaptive support on the expert and novice decision-makers.


4. Investigate the use of emerging technologies (e.g., neural networks, fuzzy logic,

and genetic algorithms) in developing ADSS’s. New developments in on-line

analytical processing and data-mining could also be used in building ADSS’s.

5. Investigate other related issues such as information overload, biasing behavior, and

restriction on flexibility and creativity in the context of ADSS’s.

USER

User Interface (Dialogue)

ExpertProblem Solving

Evaluation

Guidance

Module

UserPerformanceEvaluation

ProblemUserHistory

ProblemSolvingModel

Guidance/InstructionModel

UserDiagnosis

Model

DATA MODELS

ADAPTATION


Figure 1: An Architecture of Adaptive Decision Support Systems

Outcome for Case A

Profile of

User

Right

Wrong


Figure 2: Decision tree for Case A

Outcome forCase B

Result of

A

Right and Right

Wrong and Right

Right and Wrong

Wrong and Wrong

Profile of

User

R

R

W

R

R

W

W

W

R = RightW = Wrong


Figure 3: Decision tree for Case B


Figure 4: Menu pad


Figure 5: Data plot


Figure 6: Guidance screen for Case A


Figure 7: Guidance screen for Case B


Figure 8: Guidance screen for Case C


Figure 9: Guidance screen for Case D


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Footnote

Refer to the following three publications for details:1

Pub. 1: Fazlollahi, Parikh and Verma (1995). Evaluation of Decisional Guidance in Decision Support

Systems: An Empirical Study. Proceedings of the Third International Conference of the Decision

Sciences Institute, June 1995, Pueblo, Mexico; pp. 78-80.

Pub. 2: Fazlollahi, Parikh and Verma (1995). Evaluation of Alternate Instructional Strategies in Intelligent

Coaching Systems: An Empirical Study. Proceedings of the 26th Annual Meeting of the Decision

Science Institute, November 1995, Boston, MA; pp. 499-501.

Pub. 3: Fazlollahi, Parikh and Verma (1995). Influence of Decision Making Cognitive Style on the Design

Features of Intelligent Guidance/Help for DSS: An Empirical Study. Proceedings of the 1995

Information Resources Management Association International Conference, May 1995, Atlanta, GA;

pp. 25-30.

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