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

Chapter 4Chapter 4 DECISION SUPPORT AND ARTIFICIAL INTELLIGENCE

Brainpower for Your BusinessBrainpower 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

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

2. 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 Intelligence – find or recognize a problem, need, or

opportunity Design – consider possible ways of solving the

problem Choice – weigh the merits of each solution Implementation – carry out the solution

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Four Phases of Decision Making

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Types of Decisions You Face

Structured decision – processing a certain information in a specified way so that you will always get the right answer

Nonstructured decision – one for which there may be several “right” answers, without a sure way to get the right answer

Recurring decision – happens repeatedlyNonrecurring (ad hoc) decision – one you make

infrequently

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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

1. Genetic Algorithms Learning outcome #4

2. 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

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Components of a DSS

Model management component – consists of both the DSS models and the model management system

Data management component – stores and maintains the information that you want your DSS to use

User interface management component – allows you to communicate with the DSS

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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 formBusinesses 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?)Prescriptive problems (what to do?)

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Traffic Light Expert System

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What Expert Systems Can and Can’t Do

An expert system canReduce errorsImprove customer serviceReduce 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

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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 Logic

Fuzzy logic – a mathematical method of handling imprecise or subjective information

Used to make ambiguous information such as “short” usable in computer systems

ApplicationsGoogle’s search engineWashing machinesAntilock breaks

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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

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Evolutionary Principles of Genetic Algorithms

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

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

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

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

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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

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Data-Mining Agents

• Data-mining agent – operates in a data warehouse discovering information

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User Agents

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

Examples:Prioritize e-mailAct as gaming partnerAssemble customized news reportsFill 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 behave

2. Predict how they will behave under certain circumstances

3. Improve human systems to make them more efficient and effective

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Agent-Based Modeling

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

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

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Business Applications

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 Intelligence

Flexibility – adaptable to changeRobustness – tasks are completed even if some

individuals are removedDecentralization – each individual has a simple job

to do