Group Decision Support Systems (GDSS) are computer-based systems that support group collaboration and decision making. A GDSS provides tools for brainstorming, voting, organizing ideas, and facilitating discussions to help groups make decisions more efficiently. It allows groups to have structured electronic meetings where each participant has their own computer connected to the system. The GDSS collects and saves all input to provide a full record of the meeting. Using a GDSS can improve decision quality, increase participation, and help groups make decisions more quickly.

1. Group Decision-Support Systems (GDSS)
• What is a GDSS?
• Components of GDSS
• Overview of a GDSS meeting
• Business value of GDSS
• What Is a GDSS?
• . Supports group and organizational decision making
• Group Decision-Support System (GDSS) is an interactive computer-based system used to
facilitate the solution of unstructured problems by a set of decision makers working together
as a group.
• Provides tools for collaboration and conferencing to support group decision processes.
• Provide tools and technologies toward group decision making and were developed in
response to a growing concern over the quality and effectiveness of meeting.
• GDSS can improve the productivity of decision-making meetings, either
– by speeding up the decision-making process or
– by improving the quality of the resulting decisions
• GDSS meetings are technically different from conventional meetings in three ways:
– each participant has a PC
– a facilitator helps a chairperson to run a meeting
– combination of oral and IT communication
GDSS is a new technology combining (i) communication, (ii) computing, and (iii) decision support
technologies to facilitate the formulation and solution of unstructured problems by a group of people
(DeSanctis and Galluple, 1987).
GDSS is a computer-based system which supports groups of people and engages them in a common
task through an interface of sharing environment (Aiken et al., 1995).
Three Main Components of GDSS:
• Hardware (conference facility, audiovisual equipment, etc.-the facilities that are physically
laid out in a manner that supports group collaboration .

2. • Software tools – developed for meetings in which the participants in the same room as well as
can also be used to have networked meeting in which participants are in different locations.
GDSS software tools -
• Electronic questionnaires-aid the organizers in pre meeting planning by identifying issues of
concern and by helping to ensure that key planning information is not overlooked.
• Brainstorming tools – enable individuals ,simultaneously and anonymously to contribute ideas
on the topics of the meeting.
• Idea organizers- facilitate the organized integration and synthesis of ideas generated during
brainstorming.
• Tools for Voting or setting priorities –make available a range of methods from simple voting,
to ranking in order
• People (Participants, trained facilitator, support staff)
Similarities Between GDSS and DSS
• Both use models, data and user-friendly software
• Both are interactive with “what-if” capabilities
• Both use internal and external data
• Both allow the decision maker to take an active role
• Both have flexible systems
• Both have graphical output
Why Use GDSS?
• High level managers spend 80% of their time making decisions in groups. Applied correctly,
GDSS can reduce this time, arriving at a better decision faster.
• GDSS provides the hardware, software, databases and procedures for effective decision
making.

5. Overview of a GDSS Meeting
• In a GDSS electronic meeting, each attendee has a workstation.
• The workstations are networked and are connected to the facilitator’s console, which serves
as the facilitator’s workstation and control panel, and to the meeting’s file server.
• All data that the attendees forward from their workstations to the group are collected and
saved on the file server.
• The facilitator is able to project computer images onto the projection screen at the front of
the room.
• Many electronic meeting rooms have seating arrangements in semicircles and are tiered in
legislative style to accommodate a large number of attendees.
• The facilitator controls the use of tools during the meeting.
• An attendee is able to view the agenda

6. • During the meeting all input to the integrated screens is saved on the file server and
participants’ work is kept confidential.
• When meeting is completed ,a full record of the meeting is made available to the attendees .
Business Value of GDSS
• Traditional decision-making meetings support an optimal size of three to five attendees. GDSS
allows a greater number of attendees.
• Enable collaborative atmosphere by guaranteeing contributor’s anonymity.
• Enable non attendees to locate organized information after the meeting.
• Tools follow structured methods for organizing and evaluating ideas and for preserving the
results of meeting.
• Documentation of meeting by one group at one site can also be used as input to another
meeting on the same project at another site
• Business Value of GDSS (Continued)

7. • Can increase the number of ideas generated and the quality of decisions while producing the
desired results in fewer meetings
• Can lead to more participative and democratic decision making
Examples of GDSS Software
• Lotus Notes
– Store, manipulate, distribute memos
– Lotus Notes provides a broad range of integrated functionality including email,
calendaring, instant messaging (with additional IBM software voice & video
conferencing and/or web-collaboration), discussions/forums, blogs, an inbuilt
personnel/user directory.
• Microsoft Exchange
– Keep individual schedules
– Decide on meeting times
• NetDocuments Enterprise
– Two people can review the same document together
• Examples of GDSS
• 1) “One example of implementation of GDSS is at IBM. They, as well as many other
corporations, initiated GDSS to improve group meetings. A specific case involved a plant
manager not being able to identify the cause of problems with shop floor control. After having
a meeting for two hours with plant personnel all that resulted were arguments and bad
feelings.
• So after meeting with the company's GDSS facilitator, the manager decided to have ten plant
employees, himself, and two junior analysts participate in a GDSS program. They used
electronic brainstorming and voting to resolve the shop floor control problem.
• The manager and the facilitator decided the topic as"What are the key issues in improving
shop floor control?"
• After brainstorming for 35 minutes and compiling 645 lines of suggestions, ideas and
comments on how to improve shop floor control, the manager found that he had got useful
information about the issue for the first time.
• A list was compiled of the comments and then the members of the group ranked them in
order of importance. The results were displayed and a discussion occurred for ten minutes.

8. The manager thanked the participants and was given a printout of all the discussion and
results of the group vote .
• 2) Another example is Hewlett-Packard. Their human-factors engineers work at locations all
over the world. And they meet in person only once a year. The rest of the time, they have
frequent, ongoing meetings to discuss professional and company issues. But they have these
discussions through an electronic conference and final decision making is done with the aid of
GDSS .
Artificial Intelligence
• artificial intelligence is making machines "intelligent" -- acting as we would expect people to
act.
• A field of science and technology based on disciplines such as computer science, biology,
psychology, linguistics, mathematics, & engineering
• Goal is to develop computers that can think, see, hear, walk, talk, and feel
• simulation of computer functions normally associated with human intelligence – reasoning,
learning, problem solving
• From a business perspective AI is a set of very powerful tools, and methodologies for using
those tools to solve business problems.
• Applications of AI
• Three major areas
• Cognitive science
• Robotics
• Natural interfaces

9. • Cognitive science
– Focuses on researching how the human brain works & how humans think and learn
– Applications
• Expert systems
• Adaptive learning systems-can modify their behavior based on information
they acquire as they operate
• Fuzzy logic systems – solve unstructured problems with incomplete knowledge
by developing approximate inferences and answers as humans do
• Neural networks
• Intelligent agents
• Robotics

10. – Produces robot machines with computer intelligence and computer controlled,
humanlike physical capabilities
• Natural interfaces
– Natural language and speech recognition
– Talking to a computer and having it understand
– Virtual reality
Rule-base system
• An expert system or knowledge-based system is the common term used to describe a rule-
based processing system. It consists two elements:
– A knowledge base (the set of if-then-else rules and known facts)
– An inference engine which contains the reasoning logic used to process the rules and
data.

11. Rule (logic)
• Any rule consists of two parts: the IF part, called the antecedent (premise or condition) and
the THEN part called the consequent (conclusion or action).
IF antecedent
THEN consequent
• A rule can have multiple antecedents joined by the keywords AND (conjunction), OR
(disjunction) or
• a combination of both.
•
• IF antecedent 1IF antecedent 1
• AND antecedent 2OR antecedent 2
• . .
• . .
• . .
• AND antecedent nOR antecedent n
• THEN consequent THEN consequent
• Expert systems can also use mathematical operators to define an object as numerical and
assign it to the numerical value.
• IF ‘age of the customer’< 18
• AND ‘cash withdrawal’> 1000
• THEN ‘signature of the parent’ is required
Rules can represent relations
• Relation
IF the ‘fuel tank’ is empty
THEN the car is dead
Rules can represent recommendations

12. • Recommendation
IF the season is autumn
AND the sky is cloudy
AND the forecast is drizzle
THEN the advice is ‘take an umbrella’
Rules can represent directives
• Directive
IF the car is dead
AND the ‘fuel tank’ is empty
THEN the action is ‘refuel the car’
Rules can represent strategies
• Strategy
IF the car is dead
THEN the action is ‘check the fuel tank’;
step1 is complete
IF step1 is complete
AND the ‘fuel tank’ is full
THEN the action is ‘check the battery’;
step2 is complete
• Software Resources
– Expert system software package contains an inference engine and
other programs for refining knowledge and communicating with users
» Inference engine program processes the knowledge related to
a specific problem, and makes associations and inferences
resulting in recommended courses of action for a user.
(Inference rule-An inference rule is a statement that has two parts, an if clause and a then clause.)

13. » User interface programs, including an explanation program,
allows communication with user
• The model of human knowledge used in ES is called knowledge base
• A knowledge engineer is similar to our system analyst but has special expertise in modeling
the information
• KE works with experts to capture the knowledge they possess then builds the knowledge base
using an iterative, prototyping process until the expert system is acceptable
Basic structure of a rule-based expert system
• The knowledge base contains the domain knowledge useful for problem solving. In a rule-
based expert system, the knowledge is represented as a set of rules. Each rule specifies a
relation, recommendation, directive, strategy and has the IF (condition) THEN (action)
structure. When the condition part of a rule is satisfied, the rule is said to fire and the action
part is executed.
• The database includes a set of facts used to match against the IF (condition) parts of rules
stored in the knowledge base.
• The inference engine carries out the reasoning whereby the expert system reaches a solution.
It links the rules given in the knowledge base with the facts provided in the database.
• The explanation facilities enable the user to ask the expert system how a particular conclusion
is reached and why a specific fact is needed. An expert system must be able to explain its
reasoning and justify its advice, analysis or conclusion.
• The user interface is the means of communication between a user seeking a solution to the
problem and an expert system.

14. Two methods of reasoning when using inference rules are backward chaining and forward chaining.
Forward chaining starts with the data available and uses the inference rules to conclude more data
until a desired goal is reached.
• A strategy for searching the rule base in an expert system that begins with the information
entered by the user and searches the rule base to arrive at a conclusion
• Strategy is fired, when a condition is true.
• An inference engine using forward chaining searches the inference rules until it finds one in
which the if clause is known to be true. It then concludes the then clause and adds this
information to its data. It would continue to do this until a goal is reached.

15. Backward chaining
• A strategy for searching the rule base in an expert system that acts like a problem solver by
beginning with hypothesis and seeking out more information until the hypothesis is either
proved or disproved
• starts with a list of goals and works backwards to see if there is data which will allow it to
conclude any of these goals.
Expert systems applications
• ES asks questions of the user, searches its knowledge base for facts and rules or other
knowledge, explains its reasoning processes when asked and gives expert advice to the user in
the subject area being explored.
• Used in different fields like medicine (diagnose illness), engineering, science and business
(used to improve every step of the product cycle of a business, from finding customers to
shipping products to providing customer service

16. • A good example of application of expert systems in banking area is expert systems for
mortgages. Loan departments are interested in expert systems for mortgages because of the
growing cost of labor which makes the handling and acceptance of relatively small loans less
profitable. They also see in the application of expert systems a possibility for standardised,
efficient handling of mortgage loan, and appreciate that for the acceptance of mortgages
there are hard and fast rules which do not always exist with other types of loans.
MYCIN
• MYCIN was a rule-based expert system for the diagnosis of infectious blood diseases. It also
provided a doctor with therapeutic advice in a convenient, user-friendly manner.
• MYCIN’s knowledge consisted of about 450 rules derived from human knowledge in a narrow
domain through extensive interviewing of experts.
• The knowledge incorporated in the form of rules was clearly separated from the reasoning
mechanism. The system developer could easily manipulate knowledge in the system by
inserting or deleting some rules.
PROSPECTOR

17. • PROSPECTOR was an expert system for mineral exploration developed by the Stanford
Research Institute. Nine experts contributed their knowledge and expertise. PROSPECTOR
used a combined structure that incorporated rules and a semantic network. PROSPECTOR had
over 1000 rules.
• The user, an exploration geologist, was asked to input the characteristics of a suspected
deposit: the geological setting, structures, kinds of rocks and minerals. PROSPECTOR
compared these characteristics with models of ore deposits and made an assessment of the
suspected mineral deposit. It could also explain the steps it used to reach the conclusion.
Applications of ES
• Decision Management
– Loan portfolio analysis
– Employee performance evaluation
– Insurance
– Demographic forecasts
• Diagnostic/Troubleshooting
– Equipment calibration
– Help desk operations
– Software debugging
– Medical diagnosis
– Design/Configuration
– Computer installation
– Communication network
– Optimum assembly plan
– Selection/Classification
– Material Selection
– Information Classification
– Suspect Identification
• Applications (Contd…)Process Monitoring/Control

18. – M/C control
– Inventory control
– Chemical Testing
– Production Monitoring
Developing Expert Systems
• Begin with an expert system shell
• Add the knowledge base
• Built by a “knowledge engineer”
– Works with experts to capture their knowledge
– Works with domain experts to build the expert system
– Limitations
– Limited focus, inability to learn, maintenance problems, developmental costs
– Helpful in solving specific types of problems in a limited domain of knowledge
– fail miserably in solving problems requiring broad knowledge base and subjective
problem solving.
– - Cost of knowledge engineers, expert time & hardware & software resources are
too high
– - Cannot maintain themselves
Example
Countrywide funding corporation, California-is a loan undertaking firm –developed a PC based expert
system to make credit worthiness decisions on loan requests.
Developed CLUES which had 400 rules.
manual processes could handle 6 or 7 applications a day ,with CLUES could evaluate 16 per day.
Does not rely on CLUES to reject loans because can not handle exceptional cases.