Chapter 4 Decision Support and Artificial Intelligence: Brainpower for Your Business Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin

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. 4-2

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. 4-3

AN NFL TEAM NEEDS MORE THAN ATHLETIC ABILITY  The Patriots football team is a very successful one  The team uses a decision support system to analyze the opposition’s game  The software breaks down the game day video into plays and player actions  With this information the Patriots can better formulate their strategy 4-4

AN NFL TEAM NEEDS MORE THAN ATHLETIC ABILITY 1. DSS with predictive analytics used to gain the advantage in other sports? Choose a sport and explain how that might work. 2. Would allowing coaches to have laptops on the field change the game appreciably? 3. What other aspect of football could be improved by decision support systems? 4-5

INTRODUCTION  Phases 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 4-6

Four Phases of Decision Making 4-7

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 4-8

Types of Decisions You Face EASIEST MOST DIFFICULT 4-9

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 4-10

CHAPTER ORGANIZATION 5. Genetic Algorithms  Learning outcome #4 6. Intelligent Agents  Learning outcome #5 4-11

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 4-12

Alliance between You and a DSS 4-13

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 4-14

Components of a DSS 4-15

Predictive Analytics  Analytics (predictive analytics) – highly computational process of measuring and predicting customer behavior/attitudes  Uses combination of statistics, probability, ops management methods, AI tools, data mining, and predictive modeling  Types  Text – natural language analysis  Content – audio, video, graphical  Web – Web traffic analysis 4-16

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 4-17

Zillow GIS Software for Denver 4-18

ARTIFICIAL INTELLIGENCE  DSSs and GISs support decision making; you are still completely in charge  Artificial 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) 4-19

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?) 4-20

Traffic Light Expert System 4-21

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 4-22

NEURAL NETWORKS AND FUZZY LOGIC  Neural network (artificial neural network or ANN) – an artificial intelligence system that is capable of finding and differentiating patterns 4-23

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 4-24

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 4-25

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 4-26

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 4-27

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 4-28

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 4-29

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 4-30

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 4-31

Data-Mining Agents  Data-mining agent – operates in a data warehouse discovering information 4-32

User Agents  User or personal agent – intelligent agent that takes action on your behalf  Examples:  Prioritize  Act as gaming partner  Assemble customized news reports  Fill out forms for you  “Discuss” topics with you 4-33

MULTI-AGENT SYSTEMS AND AGENT-BASED MODELING  Biomimicry – learning from ecosystems and adapting their characteristics to human and organizational situations  Used 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 4-34

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 4-35

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 4-36

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 4-37

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 4-38