What Is Artificial Intelligence?

Topics: Virtual reality, Expert system, Artificial intelligence Pages: 13 (2368 words) Published: December 3, 2013
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 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 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 vision
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-commerce
Characteristics:
Adaptability
Autonomy
Collaborative behavior
Humanlike interface
Mobility
Reactivity...
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