Download A Web-based Hybrid Intelligent System Framework

A WEB-BASED HYBRID INTELLIGENT SYSTEM FRAMEWORK
Rajendra M Sonar
Shailesh J Mehta School of Management,
Indian Institute of Technology Bombay
Powai, Mumbai-400076
India
[email protected]
ABSTRACT
With the advent of Web technology, the intelligent
systems need to be web-based so that the decision makers
and users can access and use them using machines
supporting browsers. Web-based Internet and Internet
environment facilitates development of centralized
decision-making solutions. Instead of solving a problem
using a single intelligent technique like expert system,
neural network, case-based reasoning alone, these
techniques are integrated to reduce their weaknesses,
increase the strengths and solve complex tasks. In this
paper we describe a framework, which has advantages of
using latest web plat-form, open standard XML
technology and power of hybrid intelligent system.
system. The web server requires .NET installed on the
server. Following sections describe various building
blocks.
Variable Objects
Database Interface
Data Entry Interface Generator
Expert System (Rule Engine)
Case-based Reasoning Engine
Neural Network Module
KEY WORDS
Hybrid Intelligent Systems, Intelligent System
Architectures, Expert System, Case-based Reasoning
1. Introduction
The recent trend has been developing hybrid intelligent
systems. Various architectures, frameworks and models
have been suggested and used to develop hybrid
intelligent systems in various application domains [1-6].
We have developed a framework that is web-based and
integrates rule-based reasoning, case-based reasoning and
neural network. The framework has been implemented
using Microsoft’s C# (C-Sharp) programming language
under ASP.NET environment [7]. It supports thin clients.
This is a complete redesigned web version of Hybrid
Expert System Shell (HESS) environment [8]. HESS was
built to develop modular hybrid intelligent systems in
client/server environment and database was used as
coupling mechanism.
2. Components of the framework
The framework has various components as shown in
figure 1. These components are implemented using
object-oriented approach. Only one instance of each of
these components runs on web server (Internet
Information Server) except session object that holds the
data and process status specific to the user logged in the
Figure 1: The block diagram of the framework
2.1 Variable Objects
Variable objects is the set of common global variables
that various intelligent systems use (the web-based
interface for manipulating variables objects is shown in
figure 2). Expert systems use them in rules, CBR engine
as set of features, neural-network as input-output
variables etc. Each variable object has properties like
name, data type (logical, string, number, real, trend,
matrix, composite, lists), range of values, default value,
menu name, question to ask, input field name, when
needed scripts. Some variables are goal variables. These
goal variables are used: (a) as agenda/goal for rule-based
reasoning, (b) to enter the facts using data entry
interfaces, (c) to define similarity schema to find
matching cases, (d) to solve a problem by neural network,
(e) input/output SQL (structured query language) query
interfaces, and (f) output interfaces.
Intelligent Systems and Control, Ed. M.H.Hamza, ACTA Press, 2004, pp 254-259.
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2.2 Database Interface
We have used common notation Case (as used commonly
in CBR [9]) to describe facts for rule-based system as
well as to describe an example (vector) in neural network
processing. We call data that is manipulated as user
session data. It holds number of cases for a particular user
logged in the system.
data source/across data sources. These query interfaces
are defined for each goal, user and role.
Variable objects are mapped and linked to various data
source schemas (field name, table name, data source ID,
etc.). The mapping and linking can be set to various and
heterogeneous data sources and this information is used to
access, manipulate and exchange the data among user
session data and data sources
2.3 Data Entry Interface Generator
This component generates web data entry interfaces
(forms). These forms are used to manage cases at the
client machine in off-line mode (means connectivity to
web server is not required except at the time of
submission of cases to the server). The cases in user
session data can even be downloaded and stored on client
machine. Forms are generated based on properties of
variable objects. Whenever, a form is accessed, all
relevant cases in user session data are sent to the client for
updations as shown in figure 3(b).
Figure 2. Web interface for managing variable objects
The database interface (DBI) maintains the list of
connections to various data sources using ODBC (Open
Database Connectivity) and OLE-DB (Object Linking and
Embedding Database). SQL responses are converted into
XML (eXtensible Markup Language) formats after
fetching data from data sources. Records fetched by
calling SQL queries, correspond to cases in user session
data. Similarly XML data is converted into set of SQL
queries to update/insert data as shown in figure 3(a). Data
can be merged from multiple data sources as well as can
be updated and added to multiple data sources.
These forms contain JavaScript code for adding new
cases, modifying cases, on-line validation of data,
navigation, searching, storing and retrieving cases
on/from client’s machine (sample: figure 4). Explicit
Visual Basic/Java scripts can be embedded inside the web
pages that use variable objects defined in the framework
instead of/along with script variables to add rich set of
functionality to forms. The object variable names are
mapped to temporary VB/JavaScript variables while
generating the forms/web pages.
Figure 3. Database interface and data entry interface generator
Access to the applications, data and data sources is based
on user and role of the user. User profiles that describe
roles and data access rights are stored in one of the
database. Data access rights are defined for particular
application (goal/agenda) for that user. While defining
data access rights, various query interfaces are used. Input
Query Interface: set of SQL queries to retrieve input data
from data source/s. Output Query Interface: set of SQL
queries to add or update data to data source/s. Case Query
Interface: set of SQL queries to search similar cases in the
Figure 4. A sample form generated for goal Applicant.Credit Status
2.4 Expert System
Supports rule-based reasoning (forward and backward
reasoning). The rules are stored in IF-THEN-ELSE
format. The system has in-built mechanism which
automatically converts qualitative data into quantitative
and quantitative into qualitative. This is useful to
Intelligent Systems and Control, Ed. M.H.Hamza, ACTA Press, 2004, pp 254-259.
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implement applications that require multi-valued logic
and to quantify qualitative features. It helps to reduce
number of rules required to put explicitly in the rule-base
to transform the data. One of the powerful features of the
expert system engine: it allows use of DHTML (Dynamic
Hyper Text Markup Language) scripts to write business
logic for When Needed/When Changed methods at the
client side. The screen capture in figure 5 shows the fact
Search.Maturity Value is calculated by when needed
script and the value is displayed in text box. In figure 6
(partial text of XML file that stores variable object
information), the corresponding VB script for calculating
Search.Maturity Value is shown. In this example, the
function GetMaturityValue is already included in web
page as a part of VB script library. The input facts like
Search.Deposit Type, Search.Installment Amount,
Search.Deposit Period that are needed to calculate
Search.Maturity Value would be automatically mapped to
corresponding script variables when dynamic web page is
generated. This mapping helps to move values from
variable objects to script variables when page is generated
and sent. Similarly script variable values are moved to
variable objects after page is submitted.
Figure 6. Example: When Needed script
Figure 7. Partial result of expert system for goal Applicant.Credit
Status: XML converted into HTML
2.5 Case-Based Reasoning Engine
Figure 5. Execution of When Needed script and sample output
The entire Question & Answer (Q&A) session is managed
at the server, dynamically creating interactive web pages
and sending them to clients. Q&A session management
includes managing data as well as process status.
Applications that use forward-reasoning method need all
available facts to be provided before starting the
reasoning process. Dynamic data entry interfaces are used
to enter the available facts from the client machine (as
shown in figure 4). Rules are represented and stored in
XML format at the central server in an application object,
multiple rule-sets can be included to host multiple rulebased systems. Various functions are implemented for:
trend analysis, comparing various trends, regression
analysis, to draw various charts, compare trends, matrix
functions etc. to address financial applications.
Cases are represented in XML format (flat structure:
<Feature><Value> pair). Cases are stored in database or
across databases. They are accessed using database
interface. The combination of rule-based and nearest
neighbour methods is used for retrieval [9] [10]. A goal
variable can have corresponding CBR schema describing
set of features, type of similarity measures (numeric,
symbolic, exact), similarities, weights, etc. The case
management interface is shown in figure 8. Schema is
defined for a particular goal variable. A feature can have
different weights in the context of goal variable. For
example, a feature like Age may have different weight in
Insurance application than a Credit Evaluation
application.
Problem case is converted into revised case. Revised case
consists of the facts that are inferred (deduced) from the
base facts provided in problem case by applying set of
rules (expert system). For example, suppose the problem
case consists of features like Applicant.Gross Income,
Applicant.Dependents,
Applicant.Total
Debt,
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Applicant.Property Value, Applicant.Education, etc. The
revised case would have features like Applicant.Financial
Position, Applicant.Job Status, Applicant.Education
Status, etc. This reduces number of features required to
match and retrieve cases. SQL statements are generated
from revised query to retrieve matching cases from the
data sources as shown in figure 9(a). In this example:
select * from Applicant where Fin_Position=’Good’ and
Job_Status=’Good’ and Edu_Status=’Acceptable’. The
variable names are mapped to database fields: like in this
example Applicant.JobStatus is mapped to database field
Job_Status. The SQL statements to retrieve the matching
cases from the data sources are generated based on
various levels defined, starting with level 0 (strict criteria)
to maximum level defined (most relaxed criteria) in CBR
schema. The retrieved cases are further filtered using
nearest neighbour method to get most matching case/s.
2.6 Neural Network Module
Supports simple and quick error back-propagation
algorithm [11]. The algorithm is implemented using
matrix library and uses XML format for storing network
topology, parameters etc. Only trained neural networks
are included in the framework for running hybrid
applications. The data is represented in XML format and
input vector as well as output vector corresponds to a
case. So the input data to be processed and results
produced are treated as set of cases.
3. Integrating Systems
3.1 Use of XML
All components of the framework: expert system, neural
network, case-based reasoning engine, database interface
and even clients (through files) use data represented in
XML format for input, output, storing parameters. These
components can exchange data amongst each other
through common XML interface (figure 10).
Figure 8.Web interface for managing case schema
Figure 10. Integration framework
Figure 9.Case retrieval and adaptation
The rule-based interface on top of CBR facilitates
development of CBR applications based on Q&A
(somewhat similar to conversational CBRs). Instead of
entering entire query, user can be guided by set of
intelligent questions and answers. Upon finishing Q&A,
the CBR engine does the job of searching most similar
cases. Rules are used for adaptation [10] of matching
case/s as shown in figure 9(b). User can retain the adapted
case by adding to the data source/s through database
interface or revise the case by updating to the data
source/s.
A problem to be solved is treated as an input case while
result (along with inputs) is treated as output case
uniformly across all the components. The retrieved
database records (using SQL views) are converted into
input cases by database interface. Similarly output cases
are treated as records to be updated or added to the data
sources. The user session data contains these two types of
cases. The input cases are transformed into output cases
by applying an intelligent technique or hybrid of these
techniques. Since the cases are represented in XML
format, they can be viewed in customized HTML formats
by transforming them using extensible style sheet (xsl)
files (sample screen captures shown in figure 7 and 12).
Extensible style sheets are used to convert XML data into
HTML formats. This transformation is done at web server
before sending response to clients so that the browsers
that do not support XML can also view the data.
Using XML file interface, users can download cases in
file (XML format) on local machine as well can upload
XML file from local machine to the server.
Intelligent Systems and Control, Ed. M.H.Hamza, ACTA Press, 2004, pp 254-259.
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3.2 Sharing Mechanism
User session data act as a coupling mechanism where all
components share and access the data, facts, hypotheses,
etc. as shown in figure 10. This facilitates to integrate
various components (even external systems) to each other
in various modes and approaches in modular way. Each
component shares/accesses required data leaving other
data untouched. There are many sources from which data
is assembled in user session data (explicitly managed by
user, retrieved from data sources, results of intelligent
system execution, etc.).
3.3 Building hybrid intelligent systems
The framework can be configured to build hybrid systems
by integrating intelligent systems in various modes. A
hybrid interface is used to define the set of intelligent
systems to be executed for a particular goal. As discussed
earlier some of the variables are goal variables and hybrid
interface or intelligent techniques are linked to them. For
example goal variable Applicant.Credit Status may be
linked to a CBR schema. So whenever user selects goal as
Applicant.Credit Status and clicks on Find Matching
Case, the CBR engine starts finding the matching cases
for that goal. Execution of intelligent systems can either
be controlled explicitly by the user (by using various
command buttons/links) or by using hybrid interface and
expert system. The modes in which intelligent systems
can be integrated are described below.
In sequential mode, intelligent systems work in sequence
[1], [4]. The output of one system is input to the other
system. For example, expert system can be used to
acquire the input facts using Q&A mode and apply the
rules and produce results (output case). The output case
can be used by CBR engine as a problem case to find
most matching cases. Again expert system (rules) can be
used to adapt the case/s (ES->CBR->ES). Similarly,
expert system can be used to preprocess the data retrieved
from data sources before applying to neural networks and
vice a versa.
component is implemented using C# (which is object
oriented programming language like Java), the public
methods (operations) of one component can be accessed
in other component.
3.4 Integrating databases, intelligent systems and
other business systems
The database schemas are mapped and linked to variable
objects. It is possible to integrate databases to intelligent
systems, and, integrate data from various data sources.
This makes the framework as an information integration
tool [12]. For example, data can be extracted from a
database (say MS-Access); can be cleaned and
transformed using set of rules and updated to another
database (say Oracle). Data entry interface generator can
generate data entry web pages for entering the data
remotely into the databases. Figure 11 shows a web
interface generated for entering/managing data (only few
fields are selected) of loan and deposit products. These
products are treated as input cases and can be viewed as
shown in figure 12 by transforming product details into
HTML format using extensible style sheet.
Figure 11. A sample web form used for remote data entry.
In parallel mode, the same task is solved by more than
one intelligent system to gain better insight. The results
can be compared. For example, credit status of an
applicant can be determined by comparing results by 1)
using rule-based system 2) using CBR engine to find the
most matching customers and, 3) using trained neural
network to classify the customer.
A problem can be divided into sub-problems (sub-tasks)
and each task can be well solved by particular technique
depending upon type of task and suitability of technique.
For example expert system can perform job of qualitative
analysis while neural networks can perform the job of
pattern recognition. In meta-processing [4], one system
controls the other system. The best example is expert
system controlling neural network training. Since each
Figure 12. The XML input cases transformed using XSL.
Intelligent Systems and Control, Ed. M.H.Hamza, ACTA Press, 2004, pp 254-259.
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External applications can be integrated either through the
data sources or by passing parameters. Integrating through
database is simple one, as the database itself acts as a
sharing mechanism [8]. The required input data can be
fetched from the database. Upon execution of intelligent
system, the results can be saved to the database. Other
application can access the results from the database. For
example, salary database can be connected to rule-based
Tax Calculation Expert System. The Tax Calculation ES
can fetch various incomes of employees from the salary
database, calculate the taxes and update the taxes to the
salary database. This makes it easier to keep changing
business rules in the rule-based systems and at the same
time integrate expert systems with other business systems
5. Acknowledgements
Because of simple XML interface, other system can
upload (through client interface) input data to the user
session data in XML. After solving the problem, the
results can be downloaded by the system in XML format
(again through client interface).
[3] Suran Goonatilake & Phillip Treleaven, editors,
Intelligent systems for finance and business (John Wiley
and Sons, 1995).
4. Conclusion and further work
The framework described in this paper represents a
unique and novel way of integrating various intelligent
systems and data sources in web environment. It is an
easy to use and flexible framework to develop and deploy
web-based generic general-purpose hybrid intelligent
systems. The decision makers and users can use such type
of frameworks with just a simple Internet browser.
Representing the problem as well as result (output) as a
case in a common XML format, makes it flexible to store,
interchange and use the session data across various
intelligent systems. There are other advantages of using
this kind of framework apart from developing hybrid
intelligent systems. For example, expert system can sit on
top of various data sources and rules can be used to
integrate and view the data in uniform way.
Use of DHTML scripts for When Needed/When Changed
methods makes expert system technology to extend its
reach, bring richness and flexibility in problem solving. It
helps to combine procedural business logic with rulebased systems. Use of style sheet languages help to
transform XML data into HTML formats thereby saving a
lot of time of designing output pages.
We have a plan to add a genetic optimization module to
the existing framework to address optimization problems
especially similar to solving problems using linear
programming models. We are in the phase of developing
practical hybrid intelligent applications especially in
banking and finance domain. These include developing
dynamic and smart web sites to search products
intelligently and intranet-based hybrid systems for
financial analysis, loan underwriting, credit rating and
scoring models for various banking and financial
institutions in India.
I thank to the Dean (Research & Development), Indian
Institute of Technology Bombay (IITB), for sanctioning
the seed grant for this project.
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