chapter 4 decision support and artificial intelligence
TRANSCRIPT
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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