McGraw-Hill © 2008 The McGraw-Hill Companies, Inc. All rights reserved.

Chapter 4 DECISION SUPPORT AND ARTIFICIAL INTELLIGENCE

Brainpower for Your Business

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STUDENT LEARNING OUTCOMES

1. Compare and contrast decision support systems and geographic information systems.

2. Define expert systems and describe the types of problem to which they are applicable.

3. Define neural networks and fuzzy logic and the use of these AI tools.

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STUDENT LEARNING OUTCOMES

4. Define genetic algorithms and list the concepts on which they are based and the types of problems they solve.

5. Describe the four types of agent-based technologies.

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VISUALIZING INFORMATION IN MAP FORM FOR DECISION MAKING

Geographic information systems (GISs) allows you to see information spatially, or in map form.

Researchers and scientists used a GIS to map the location of all the debris from the shuttle Columbia

The city of Chattanooga uses a GIS to map the location of its 6,000 trees to help develop a maintenance schedule

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VISUALIZING INFORMATION IN MAP FORM FOR DECISION MAKING

The city of Richmond, VA, used a GIS to optimize its 2,500 bus stop locations in its public transportation system

Sometimes, a picture is worth a thousand wordsRecall from Chapter 1, the form of information often

defines its quality

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VISUALIZING INFORMATION IN MAP FORM FOR DECISION MAKING

1. Do you use Web-based map services to get directions and find the location of buildings? If so, why?

2. In what ways could real estate agents take advantage of the features of a GIS?

3. How could GIS software benefit a bank wanting to determine the optimal placements for ATMs?

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INTRODUCTION

Phases of decision making

1. Intelligence (find what to fix) find or recognize a problem, need, or opportunity (the

diagnostic phase). Detect and interpret signs that indicate a situation

which needs your attention.

2. Design (find fixes) consider possible ways of solving the problem. Develop all the possible solutions

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INTRODUCTIONPhases of decision making (Cont.)

3. Choice (pick a fix) weigh the merits of each solution and choose the best

one. At this stage a course of action is prescribed.

4. Implementation (apply the fix) carry out the chosen solution, monitor the results and

make adjustments as necessary. Your solution will always need fine-tuning.

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Four Phases of Decision Making

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Types of Decisions You FaceStructured decision

Processing a certain information in a specified way so that you will always get the right answer

E.g. calculating gross pay for hourly workers.Can be easily automated with IT.

Nonstructured decisionOne for which there may be several “right” answers,

without a sure way to get the right answerE.g. introduce a new product line, employ a marketting

campaign.What about choosing a job?

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Types of Decisions You FaceRecurring decision

Happens repeatedly (weekly, monthly, quarterly, or yearly)

E.g. deciding how much inventory to carry, at what price to sell the inventory.

Nonrecurring (ad hoc) decisionOne you make infrequently (might be once)E.g. deciding where to build a distribution center,

company mergers.

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Types of Decisions You Face

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CHAPTER ORGANIZATION

1. Decision Support Systems Learning outcome #1

2. Geographic Information Systems Learning outcome #1

3. Expert Systems Learning outcome #2

4. Neural Networks and Fuzzy Logic Learning outcome #3

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CHAPTER ORGANIZATION

5. Genetic Algorithms Learning outcome #4

6. Intelligent Agents Learning outcome #5

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DECISION SUPPORT SYSTEMS

Decision support system (DSS) – a highly flexible

and interactive system that is designed to support

decision making when the problem is not structured

Decision support systems help you analyze, but you

must know how to solve the problem, and how to

use the results of the analysis.

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Alliance between You and a DSS

The union of your know-how and IT power helps you generate business intelligence so that you can quickly respond to changes and manage resources in the most effective and efficient ways possible.

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Components of a DSSA typical DSS has three components:

1. Model management component2. Data management component3. User interface management component

When you begin your analysis, you tell the DSS, using the user interface management component, which model (in the model management component) to use on what information (in the data management component). The model requests the information from the data management component, analyzes it and sends the result to the user interface management component which passes the results back to you.

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Components of a DSS (Cont.)1. Model management component

Consists of both the DSS models and the model management system

A model is a representation of some event, fact, or situation.

Businesses use models to represent variables and their relationships.

E.g. you would use a statistical model called analysis of variance to determine whether newspaper and television are equally effective in increasing sales.

The model management component can’t select the best model for you to use for some problem but it can help you create and manipulate models quickly and easily.

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Components of a DSS (Cont.)2- Data management component

Stores and maintains the information that you want your DSS to use

Consists of both the DSS information and the DSS DBMS.

This information can come from one or more of three resources:

1. Organizational information2. External information, e.g. federal government, Dow

Jones and the Internet.3. Personal information- your own insights and

experience.

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Components of a DSS (Cont.)3- User interface management component

Allows you to communicate with the DSS Consists of the user interface and the user interface

management system. Allows you to combine your know-how with the

storage and processing capabilities of the computer.This the part that you see, through it you enter

information, commands and models.

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Components of a DSS

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GEOGRAPHIC INFORMATION SYSTEMS

Geographic information system (GIS) – DSS

designed specifically to analyze spatial information.

Spatial information is any information in map form

such as roads, the path of a hurricane, etc.

Businesses use GIS software to analyze

information, generate business intelligence, and

make decisions.

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Zillow GIS Software for Denver

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EXPERT SYSTEMS

Expert (knowledge-based) system – an artificial

intelligence system that applies reasoning

capabilities to reach a conclusion

Used for

Diagnostic problems (what’s wrong?) correspond to

the intelligence phase of decision making.

Prescriptive problems (what to do?) correspond to

the choice phase of decision making.

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EXPERT SYSTEMS (Cont.)What’s the difference between a DSS and an

expert system?

To use a DSS, you must have considerable

knowledge or expertise with the situation

A DSS assists you in making decisions.

You must know how to reason things.

In an expert system the know how is in the system.

You need only to provide the facts and symptoms of

the problem.

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Traffic Light Expert System

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What Expert Systems Can and Can’t Do

An expert system canHandle massive amounts of informationReduce errorsAggregate information from various sourcesImprove customer serviceDecrease personnel time spent on tasksReduce cost

An expert system can’tUse common senseAutomate all processes

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NEURAL NETWORKS AND FUZZY LOGIC

Neural network (artificial neural network or ANN) – an artificial intelligence system that is capable of finding and differentiating patterns

A neural network can learn by example and can adapt to new concepts and knowledge.

Neural networks are widely used for visual pattern and speech recognition systems.

Neural networks are called predictive systems since they can see patters in huge volumes of information.

See examples on page 109.

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Neural Networks Can…

Learn and adjust to new circumstances on their ownTake part in massive parallel processingFunction without complete informationCope with huge volumes of informationAnalyze nonlinear relationships

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Fuzzy LogicA way of reaching conclusions based on ambiguous

or vague information. E.g. temperature.

Fuzzy logic – a mathematical method of handling

imprecise or subjective information

Used to make ambiguous information such as

“short” usable in computer systems

Examples: Google’s search engine, washing

machines, etc.

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GENETIC ALGORITHMS

Genetic algorithm – an artificial intelligence system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem

A genetic algorithm is an optimizing system it finds the combination of inputs that give the best output.

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Evolutionary Principles of Genetic Algorithms

1. Selection – or survival of the fittest or giving preference to better outcomes

2. Crossover – combining portions of good outcomes to create even better outcomes

3. Mutation – randomly trying combinations and evaluating the success of each

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Genetic Algorithms Can…

Take thousands or even millions of possible solutions and combine and recombine them until it finds the optimal solution

Work in environments where no model of how to find the right solution exists

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INTELLIGENT AGENTS

Intelligent agent – software that assists you, or acts on your behalf, in performing repetitive computer-related tasks

E.g. the animated paper clip in MS Word that offers suggestions on how to proceed in writing a letter.

TypesInformation agentsMonitoring-and-surveillance or predictive agentsData-mining agents User or personal agents

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Information Agents

Information Agents – intelligent agents that search

for information of some kind and bring it back

Ex: Buyer agent or shopping bot – an intelligent

agent on a Web site that helps you, the customer,

find products and services you want (Amazon.com)

Ex: A CNN Custom News Bot will gather news from

CNN on the topics you want to read about.

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Monitoring-and-Surveillance Agents Monitoring-and-surveillance (predictive) agents

– intelligent agents that constantly observe and report on some entity of interest, a network, or manufacturing equipment, for example.

E.g: Agents that monitor complex computer networks to

predict for system crashes before they happen. Agents that monitor Internet sites, discussion

groups, mailing lists, etc., for stock manipulation. Agents that monitor sites for updated information on

the topic of your choice. Agents that monitor auction sites for products or

sites that you want.

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Data-Mining Agents Data-mining agent – operates in a data warehouse

discovering information

A data-mining agent may detect major shifts in a

trend or a key indicator.

E.g. Volkswagen’s intelligent agent system might

see a problem in some part of the country that is

about to cause payments to slow down. Having this

information, managers can formulate a plan to

protect themselves.

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User Agents

User or personal agent – intelligent agent that takes action on your behalf

Examples:Prioritize e-mailAct as gaming partnerFill out forms for you “Discuss” topics with you

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MULTI-AGENT SYSTEMS AND AGENT-BASED MODELING

Biomimicry – learning from ecosystems and adapting their characteristics to human and organizational situations

Used to1. Learn how people-based systems behave2. Predict how they will behave under certain

circumstances3. Improve human systems to make them more

efficient and effective

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Agent-Based Modeling

Multi-agent system – groups of intelligent agents have the ability to work independently and to interact with each other.

Agent-based modeling – a way of simulating human organizations using multiple intelligent agents, each of which follows a set of simple rules and can adapt to changing conditions.

E.g. Agent-based modeling systems are being used to predict the escape routes that people seek in a burning building.

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Companies that UseAgent-Based Modeling

Southwest Airlines – cargo routingP&G – supply network optimizationAir Liquide America – reduce production and

distribution costsMerck – distributing anti-AIDS drugs in AfricaFord – balance production costs & consumer

demandsEdison Chouest – deploy service and supply vessels

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Swarm Intelligence

Swarm (collective) intelligence – the collective behavior of groups of simple agents that are capable of devising solutions to problems as they arise, eventually learning to coherent global patterns

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Characteristics of Swarm Intelligence1. Flexibility – adaptable to change (small or big) in

the environment around it.2. Robustness – tasks are completed even if some

individuals are removed if some members don’t succeed, work gets done.

3. Decentralization – each individual has a simple job to do and performs it without supervision.

4. Self-organization – methods of problem solving are not prescribed from a central authority, but rather developed by the individuals who are responsible for completing the task.