Business Info Systems- Artificial Intelligence

Artificial intelligence (AI)
-Consists of related technologies that try to simulate and reproduce human thought and behavior
-Includes thinking, speaking, feeling, and reasoningAI technologies
-Concerned with generating and displaying knowledge and facts

-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

Pattern recognition!

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?

Robotics
-One 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 visionSeeing, touching, grasping, handling tasks

-Honda’s ASIMO
One of the most advanced and most popular robots
Works with other robots in coordination
-Personal robots
Limited mobility/vision, and some speech capabilities
-Robots have some unique advantages in the workplace compared with humans

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 heuristic data
Components of an Expert System
-Knowledge acquisition facility
-Knowledge base
Factual knowledge
Heuristic knowledge
Meta-knowledge
-Knowledge base management system (KBMS)
-User interface
-Explanation facility
-Inference engine-Forward chaining
Series of “if-then-else”
Condition pairs are performed
The “if” condition is evaluated first
Then the corresponding “then-else” action is carried out
-Backward chaining
Starts with the goal—the “then” part
Backtracks to find the right solution

-Semantic (associative) networks
Represents information as links and nodes
-Frames
Store conditions or objects in hierarchical order
-Scripts
Describe a sequence of events

Uses of Expert Systems
-Airline industry
-Forensics lab work
-Banking and finance
-Education
-Food industry
-Personnel management
-Security
-US Government
-Agriculture
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
-Subject domain is limited
-Decision or task involves many rules and complex logic
-Scarcity of experts in the organization
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 require human experts
Advantages of Expert Systems
-Never become distracted, forgetful, or tired
-Duplicate and preserve the expertise of scarce experts
-Preserve the expertise of employees who are retiring or leaving an organization
-Create consistency in decision making
-Improve the decision-making skills of nonexperts
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 no match is found after the above query
-Human expert must solve the problem
Intelligent Agents
-Bots (short for robots)
Software capable of reasoning and following rule-based processes
-Are becoming more popular
Especially in e-commerceCharacteristics:
-Adaptability
-Autonomy
-Collaborative behavior
-Humanlike interface
-Mobility
-Reactivity

-Web marketing
Collect 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

Shopping and Information Agents
-Help users navigate through the vast resources available on the Web
-Provide better results in finding information
-Examples
Pricewatch
BestBookBuys
www.mysimon.com
DogPile
-Searches the Web by using several search engines
-Removes duplicate results
Personal Agents
-Perform specific tasks for a user
-Such as:
Remembering information for filling out Web forms
Completing e-mail addresses after the first few characters are typed
Data-Mining Agents
-Work with a data warehouse
-Detect trend changes
-Discover information and relationships among data items that were not readily apparent
-Having this information early enables decision makers to come up with a solution that minimizes the negative effects of the problem
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
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-Used in:
Search engines, chip design, database management systems, software development, and more
-Examples:
Dryers
Refrigerators
Shower systems
TVs
Video camcorders

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 and objects, and filtering spam e-mail
-Used for poorly structured problems
-Uses patterns
Not the if-then-else rules that expert systems use
-Creates a model based on input and output-Used for many tasks, including:
Bankruptcy prediction
Credit rating
Investment analysis
Oil and gas exploration
Target marketing

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
Natural Language Processing
-Developed so that users can communicate with computers in human 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 steadyCategories:
-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

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 the system can respond to inquiries about the content

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
-You can add AI technologies to a DSS’s model base component
-Integrating expert system capabilities into the user interface component can improve the quality and user friendliness of a DSS