1 Lecture 7 Brainpower for Your Business Lecture 7 DECISION SUPPORT AND ARTIFICIAL INTELLIGENCE Brainpower for Your Business

2 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.

3 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.

4 VISUALIZING INFORMATION IN MAP FORM FOR DECISION MAKING oGeographic information systems (GISs) allow you to see information spatially, or in map form. oResearchers and scientists used a GIS to map the location of all the debris from the shuttle Columbia oThe 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 oThe city of Richmond, VA, used a GIS to optimize its 2,500 bus stop locations in its public transportation system oSometimes, a picture is worth a thousand words oRecall from Lecture 1, the form of information often defines its quality

6 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?

7 INTRODUCTION oPhases of decision making 1.Intelligence – find or recognize a problem, need, or opportunity 2.Design – consider possible ways of solving the problem 3.Choice – weigh the merits of each solution 4.Implementation – carry out the solution

8 Four Phases of Decision Making

9 Types of Decisions You Face oStructured decision – processing a certain information in a specified way so that you will always get the right answer oNon-structured decision – one for which there may be several “right” answers, without a sure way to get the right answer oRecurring decision – happens repeatedly oNonrecurring (ad hoc) decision – one you make infrequently

10 Types of Decisions You Face EASIEST MOST DIFFICULT

11 DECISION SUPPORT SYSTEMS oDecision support system (DSS) – a highly flexible and interactive system that is designed to support decision making when the problem is not structured oDecision support systems help you analyze, but you must know how to solve the problem, and how to use the results of the analysis

12 Alliance between You and a DSS

13 Components of a DSS oModel management component – consists of both the DSS models and the model management system oData management component – stores and maintains the information that you want your DSS to use oUser interface management component – allows you to communicate with the DSS

14 Components of a DSS

15 GEOGRAPHIC INFORMATION SYSTEMS oGeographic information system (GIS) – DSS designed specifically to analyze spatial information oSpatial information is any information in map form oBusinesses use GIS software to analyze information, generate business intelligence, and make decisions

16 Zillow GIS Software for Denver

17 ARTIFICIAL INTELLIGENCE oDSSs and GISs support decision making; you are still completely in charge oArtificial intelligence, the science of making machines imitate human thinking and behavior, can replace human decision making in some instances –Expert systems –Neural networks (and fuzzy logic) –Genetic algorithms –Intelligent agents (or agent-based technologies)

18 EXPERT SYSTEMS oExpert (knowledge-based) system – an artificial intelligence system that applies reasoning capabilities to reach a conclusion oUsed for –Diagnostic problems (what’s wrong?) –Prescriptive problems (what to do?)

19 Traffic Light Expert System

20 What Expert Systems Can and Can’t Do oAn expert system can –Reduce errors –Improve customer service –Reduce cost oAn expert system can’t –Use common sense –Automate all processes

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

22 Neural Networks Can… oLearn and adjust to new circumstances on their own oTake part in massive parallel processing oFunction without complete information oCope with huge volumes of information oAnalyze nonlinear relationships

23 Fuzzy Logic oFuzzy logic – a mathematical method of handling imprecise or subjective information oUsed to make ambiguous information such as “short” usable in computer systems oApplications –Google’s search engine –Washing machines –Antilock breaks

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

25 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

26 Genetic Algorithms Can… oTake thousands or even millions of possible solutions and combine and recombine them until it finds the optimal solution oWork in environments where no model of how to find the right solution exists

27 INTELLIGENT AGENTS oIntelligent agent – software that assists you, or acts on your behalf, in performing repetitive computer-related tasks oTypes –Information agents –Monitoring-and-surveillance or predictive agents –Data-mining agents –User or personal agents

28 Information Agents oInformation Agents – intelligent agents that search for information of some kind and bring it back oEx: Buyer agent or shopping bot – an intelligent agent on a Web site that helps you, the customer, find products and services you want

29 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

30 Data-Mining Agents  Data-mining agent – operates in a data warehouse discovering information

31 User Agents oUser or personal agent – intelligent agent that takes action on your behalf oExamples: –Prioritize –Act as gaming partner –Assemble customized news reports –Fill out forms for you –“Discuss” topics with you

32 MULTI-AGENT SYSTEMS AND AGENT-BASED MODELING oBiomimicry – learning from ecosystems and adapting their characteristics to human and organizational situations oUsed to 1.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

33 Agent-Based Modeling oAgent-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 oMulti-agent system – groups of intelligent agents have the ability to work independently and to interact with each other

34 Business Applications oSouthwest Airlines – cargo routing oP&G – supply network optimization oAir Liquide America – reduce production and distribution costs oMerck – distributing anti-AIDS drugs in Africa oFord – balance production costs & consumer demands oEdison Chouest – deploy service and supply vessels

35 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 leading to coherent global patterns

36 Characteristics of Swarm Intelligence oFlexibility – adaptable to change oRobustness – tasks are completed even if some individuals are removed oDecentralization – each individual has a simple job to do

37 End of Lecture