Chapter 4 DECISION SUPPORT AND ARTIFICIAL INTELLIGENCE Brainpower for Your Business

1. Chapter 4DECISION SUPPORT AND ARTIFICIAL INTELLIGENCEBrainpower for Your Business

2. STUDENT LEARNING OUTCOMES • Compare and contrast decision support systems and geographic information systems. • Define expert systems and describe the types of problem to which they are applicable. • Define neural networks and fuzzy logic and the use of these AI tools.

3. STUDENT LEARNING OUTCOMES • Define genetic algorithms and list the concepts on which they are based and the types of problems they solve. • Describe the four types of agent-based technologies.

4. 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

5. 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 words • Recall from Chapter 1, the form of information often defines its quality

6. VISUALIZING INFORMATION IN MAP FORM FOR DECISION MAKING • Do you use Web-based map services to get directions and find the location of buildings? If so, why? • In what ways could real estate agents take advantage of the features of a GIS? • How could GIS software benefit a bank wanting to determine the optimal placements for ATMs?

7. 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

8. Four Phases of Decision Making

9. 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 repeatedly • Nonrecurring (ad hoc) decision – one you make infrequently

10. Types of Decisions You Face

11. CHAPTER ORGANIZATION • Decision Support Systems • Learning outcome #1 • Geographic Information Systems • Learning outcome #1 • Expert Systems • Learning outcome #2 • Neural Networks and Fuzzy Logic • Learning outcome #3

12. CHAPTER ORGANIZATION • Genetic Algorithms • Learning outcome #4 • Intelligent Agents • Learning outcome #5

13. 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

14. Alliance between You and a DSS

15. 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

16. Components of a DSS

17. GEOGRAPHIC INFORMATION SYSTEMS • Geographic information system (GIS) – DSS designed specifically to analyze spatial information • Spatial information is any information in map form • Businesses use GIS software to analyze information, generate business intelligence, and make decisions

18. Zillow GIS Software for Denver

19. 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?)

20. Traffic Light Expert System

21. What Expert Systems Can and Can’t Do • An expert system can • Reduce errors • Improve customer service • Reduce cost • An expert system can’t • Use common sense • Automate all processes

22. NEURAL NETWORKS AND FUZZY LOGIC • Neural network (artificial neural network or ANN) – an artificial intelligence system that is capable of finding and differentiating patterns

23. Neural Networks Can… • Learn and adjust to new circumstances on their own • Take part in massive parallel processing • Function without complete information • Cope with huge volumes of information • Analyze nonlinear relationships

24. 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 • Applications • Google’s search engine • Washing machines • Antilock breaks

25. 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

26. 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

27. 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

28. INTELLIGENT AGENTS • Intelligent agent – software that assists you, or acts on your behalf, in performing repetitive computer-related tasks • Types • Information agents • Monitoring-and-surveillance or predictive agents • Data-mining agents • User or personal agents

29. 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

30. 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

31. Data-Mining Agents • Data-mining agent – operates in a data warehouse discovering information

32. User Agents • User or personal agent – intelligent agent that takes action on your behalf • Examples: • Prioritize e-mail • Act as gaming partner • Assemble customized news reports • Fill out forms for you • “Discuss” topics with you

33. MULTI-AGENT SYSTEMS AND AGENT-BASED MODELING • Biomimicry – learning from ecosystems and adapting their characteristics to human and organizational situations • Used to • Learn how people-based systems behave • Predict how they will behave under certain circumstances • Improve human systems to make them more efficient and effective

34. 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

35. Business Applications • Southwest Airlines – cargo routing • P&G – supply network optimization • Air Liquide America – reduce production and distribution costs • Merck – distributing anti-AIDS drugs in Africa • Ford – balance production costs & consumer demands • Edison Chouest – deploy service and supply vessels

36. 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

37. Characteristics of Swarm Intelligence • Flexibility – adaptable to change • Robustness – tasks are completed even if some individuals are removed • Decentralization – each individual has a simple job to do