1 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning INTELLIGENT INFORMATION SYSTEMS CHAPTER 13 Hossein BIDGOLI MIS

2 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems LO1 Define artificial intelligence and explain how these technologies support decision making. LO2 Explain an expert system, its applications, and its components. LO3 Describe case-based reasoning. LO4 Summarize types of intelligent agents and how they’re used. LO5 Describe fuzzy logic and its uses. l e a r n i n g o u t c o m e s

3 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning LO6 Explain artificial neural networks. LO7 Describe how genetic algorithms are used. LO8 Explain natural language processing and its advantages and disadvantages. LO9 Summarize the advantages of integrating AI technologies into decision support systems. l e a r n i n g o u t c o m e s (cont’d.) Chapter 13 Intelligent Information Systems

4 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems What Is Artificial Intelligence? Artificial intelligence (AI) –Consists of related technologies that try to simulate and reproduce human thought and behavior –Includes thinking, speaking, feeling, and reasoning AI technologies –Concerned with generating and displaying knowledge and facts

5 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems What Is Artificial Intelligence? (cont’d.) Knowledge engineers try to discover “rules of thumb” –Enable computers to perform tasks usually handled by humans Capabilities of these systems have improved in an attempt to close the gap between artificial intelligence and human intelligence

6 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems AI Technologies Supporting Decision Making Decision makers use information technologies in decision-making analyses: –What-is –What-if Other questions: –Why? –What does it mean? –What should be done? –When should it be done?

7 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Table 13.1 Applications of AI Technologies

8 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Robotics Some of the most successful applications of AI Perform well at simple, repetitive tasks Currently used mainly on assembly lines in Japan and the United States Cost of industrial robots Some robots have limited vision

9 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Robotics (cont’d.) Honda’s ASIMO –One of the most advanced and most popular robots –Works with other robots in coordination Personal robots –Mobility, limited vision, and some speech capabilities Robots have some unique advantages in the workplace compared with humans

10 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Expert Systems One of the most successful AI-related technologies Mimic human expertise in a field to solve a problem in a well-defined area Consist of programs that mimic human thought behavior –In a specific area that human experts have solved successfully Work with heuristics

11 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Components of an Expert System Knowledge acquisition facility Knowledge base Factual knowledge Heuristic knowledge Meta-knowledge Knowledge base management system (KBMS) Explanation facility Inference engine

12 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Exhibit 13.1 An Expert System Configuration

13 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Components of an Expert System (cont’d.) Forward chaining –Series of “If-Then-Else” –Condition pairs are performed “If” condition is evaluated first Then the corresponding “Then-Else” action is carried out Backward chaining –Starts with the goal first—the Then part –Backtracks to find the right solution

14 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Components of an Expert System (cont’d.) Semantic (associative) networks –Represent information as links and nodes Frames –Store conditions or objects in hierarchical order Scripts –Describe a sequence of events

15 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Uses of Expert Systems Airline industry Forensics lab work Banking and finance Education Food industry Personal management Security US Government Agriculture

16 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Expert Systems in Baltimore County Police Department In Baltimore County, an expert system was developed so that detectives could analyze information about burglary sites and identify possible suspects Detectives could enter statements about burglaries, such as neighborhood characteristics, the type of property stolen, and the type of entry used; they could also get information on possible suspects

17 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Criteria for Using Expert Systems Human expertise is needed but one expert can’t investigate all the dimensions of a problem Knowledge can be represented as rules or heuristics Decision or task has already been handled successfully by human experts Decision or task requires consistency and standardization

18 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Criteria for Using Expert Systems (cont’d.) Subject domain is limited Decision or task involves many rules and complex logic Scarcity of experts in the organization

19 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Criteria for Not Using Expert Systems Very few rules Too many rules Well-structured numerical problems are involved Problems are in areas that are too wide and shallow Disagreement among experts Problems are solved better by human experts

20 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Advantages of Expert Systems Never becomes distracted, forgetful, or tired Duplicates and preserves the expertise of scarce experts Preserve the expertise of employees who are retiring or leaving an organization Creates consistency in decision making Improves the decision-making skills of nonexperts

21 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Case-Based Reasoning Problem-solving technique Matches a new case (problem) with a previously solved case and its solution stored in a database If there’s no exact match between the new case and cases stored in the database: –System can query the user for clarification or more information If still no match found: –Human expert must solve the problem

22 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Intelligent Agents Bots (short for robots) Applications of artificial intelligence are becoming more popular –Particularly in e-commerce Consist of software capable of reasoning and following rule-based processes

23 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Intelligent Agents (cont’d.) Characteristics: –Adaptability –Autonomy –Collaborative behavior –Human-like interface –Mobility –Reactivity

24 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Intelligent Agents (cont’d.) Web marketing –Collects information about customers, such as items purchased, demographic information, and expressed and implied preferences “Virtual catalogs” –Display product descriptions based on customers’ previous experiences and preferences

25 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Shopping and Information Agents Help users navigate through the vast resources available on the Web Provide better results in finding information Examples: –PriceScan –BestBookBuys.com – –DogPile Searches the Web by using several search engines Eliminates duplicate results

26 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Personal Agents Agents perform specific tasks for a user Such as: –Remembering information for filling out Web forms –Completing addresses after the first few characters are typed

27 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Data-Mining Agents Work with a data warehouse Detect trend changes Discover new information and relationships among data items that aren’t readily apparent Having this information early enables decision makers to come up with a solution that minimizes the negative effects of the problem

28 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Monitoring and Surveillance Agents Track and report on computer equipment and network systems –To predict when a system crash or failure might occur Example: NASA’s Jet Propulsion Laboratory

29 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Fuzzy Logic Allows a smooth, gradual transition between human and computer vocabularies Deals with variations in linguistic terms by using a degree of membership Designed to help computers simulate vagueness and uncertainty in common situations Works based on the degree of membership in a set

30 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Exhibit 13.3 Degree of Membership in a Fuzzy System

31 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Uses of Fuzzy Logic Used in: –Search engines, chip design, database management systems, software development, and more Examples: –Dryers –Refrigerators –Shower systems –TVs –Video camcorders

32 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Artificial Neural Networks Networks that learn and are capable of performing tasks that are difficult with conventional computers Examples: –Playing chess –Recognizing patterns in faces Used for poorly structured problems Use patterns instead of the “If-Then-Else” rules that expert systems use Create a model based on input and output

33 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Exhibit 13.4 An Artificial Neural Network Configuration

34 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Artificial Neural Networks (cont’d.) Used for many tasks, including: –Bankruptcy prediction –Credit rating –Investment analysis –Oil and gas exploration –Target marketing

35 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Neural Networks in Action Many companies are able to predict customers’ shopping behavior based on past purchases E-banks use neural networks to rank their customers into groups Visa International –Introduced a credit authorization system based on neural networks to reduce credit card fraud –Could cut fraudulent transactions by as much as 40%

36 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Genetic Algorithms Used mostly in techniques to find solutions to optimization and search problems Applications: –Jet engine design, portfolio development, and network design Find the combination of inputs that generates the most desirable outputs Techniques: –Selection or survival of the fittest –Crossover –Mutation

37 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Natural Language Processing Developed so that users can communicate with computers in their own language Provides question-and-answer setting that’s more natural and easier for people to use Products aren’t capable of a dialogue that compares with conversations between humans –However, progress has been steady

38 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Table 13.2 NLP Systems

39 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Natural Language Processing (cont’d.) Categories: –Interface to databases –Machine translation –Text scanning and intelligent indexing programs for summarizing large amounts of text –Generating text for automated production of standard documents –Speech systems for voice interaction with computers

40 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Natural Language Processing (cont’d.) Interfacing –Accepting human language as input –Carrying out the corresponding command –Generating the necessary output Knowledge acquisition –Using the computer to read large amounts of text and understand the information well enough to: Summarize important points and store information so that the system can respond to inquiries about the content

41 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Integrating AI Technologies into Decision Support Systems I-related technologies can improve the quality of decision support systems (DSSs) –Including expert systems, natural language processing, and artificial neural networks Benefits of integrating an expert system into the database component of a DSS are: –Adding deductive reasoning to traditional DBMS functions –Improving access speed

42 MIS, Chapter 13 ©2011 Course Technology, a part of Cengage Learning Chapter 13 Intelligent Information Systems Summary Intelligent information systems –AI technologies are used to support decision-making processes Expert systems –Components Case-based reasoning Intelligent agents Fuzzy logic and genetic algorithms Natural language processing