Presentation Transcript

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 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. Design (find fixes) consider possible ways of solving the problem. Develop all the possible solutions
8. INTRODUCTION Phases of decision making (Cont.) Choice (pick a fix) weigh the merits of each solution and choose the best one. At this stage a course of action is prescribed. Implementation (apply the fix) carry out the chosen solution, monitor the results and make adjustments as necessary. Your solution will always need fine-tuning.
9. Four Phases of Decision Making
10. Types of Decisions You Face Structured 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 decision One for which there may be several “right” answers, without a sure way to get the right answer E.g. introduce a new product line, employ a marketting campaign. What about choosing a job?
11. Types of Decisions You Face Recurring 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) decision One you make infrequently (might be once) E.g. deciding where to build a distribution center, company mergers.
12. Types of Decisions You Face
13. 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
14. CHAPTER ORGANIZATION Genetic Algorithms Learning outcome #4 Intelligent Agents Learning outcome #5
15. 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.
16. 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.
17. Components of a DSS A typical DSS has three components: Model management component Data management component 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.
18. Components of a DSS (Cont.) 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.
19. 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: Organizational information External information, e.g. federal government, Dow Jones and the Internet. Personal information- your own insights and experience.
20. 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.
21. Components of a DSS
22. 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.
23. Zillow GIS Software for Denver
24. 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.
25. 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.
26. Traffic Light Expert System
27. What Expert Systems Can and Can’t Do An expert system can Handle massive amounts of information Reduce errors Aggregate information from various sources Improve customer service Decrease personnel time spent on tasks Reduce cost An expert system can’t Use common sense Automate all processes
28. 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.
29. 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
30. Fuzzy Logic A 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.
31. 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.
32. 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
33. 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
34. 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. Types Information agents Monitoring-and-surveillance or predictive agents Data-mining agents User or personal agents
35. 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.
36. 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.
37. 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.
38. User Agents User or personal agent – intelligent agent that takes action on your behalf Examples: Prioritize e-mail Act as gaming partner Fill out forms for you “Discuss” topics with you
39. 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
40. 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.
41. Companies that UseAgent-Based Modeling 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
42. 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
43. Characteristics of Swarm Intelligence Flexibility – adaptable to change (small or big) in the environment around it. Robustness – tasks are completed even if some individuals are removed  if some members don’t succeed, work gets done. Decentralization – each individual has a simple job to do and performs it without supervision. 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.