Artificial intelligence began in the 1960s with early attempts at game playing, theorem proving, and problem solving. An expert system is a type of AI that attempts to provide answers to problems where human experts would normally be consulted. Expert systems use knowledge bases, inference engines, and other components to mimic human expertise in a specific domain. Virtual reality allows users to interact with simulated environments through technologies like head-mounted displays, CAVEs, and haptic interfaces.

1. Specialized Business
Information Systems
Chapter 11

2. An Overview of Artificial
Intelligence

3. From the perspective of intelligence : artificial
intelligence is making machines "intelligent" --
acting as we would expect people to act.
AI began in the early 1960s -- the first attempts were
game playing (checkers), theorem proving (a few
simple theorems) and general problem solving (only
very simple tasks)

4. The Nature of Intelligence
• Learn from experience & apply the knowledge
• Handle complex situations
• Solve problems when important information is
missing
• Determine what is important

5. The Nature of Intelligence
• React quickly & correctly to new situations
• Understand visual images
• Process & manipulate symbols
• Be creative & imaginative
• Use heuristics

6. 6
The Difference Between Natural and
Artificial Intelligence (continued)

7. The Major Branches of Artificial
Intelligence

8. An Overview of Expert
Systems

9. Expert Systems
An expert system is software that attempts to
provide an answer to a problem, or clarify
uncertainties where normally one or more
human experts would need to be consulted.
Expert systems are most common in a specific
problem domain, and is a traditional
application and/or subfield of artificial
intelligence.

10. 10
• Computerized expert systems
– Have been developed to diagnose problems,
predict future events, and solve energy
problems
– Use heuristics, or rules of thumb, to arrive at
conclusions or make suggestions

11. ES applications:
From airport tarmacs to online job banks to
medical labs, artificial intelligence is
everywhere.

12. Characteristics of an Expert System
• Can explain their reasoning or suggested decisions
• Can display “intelligent” behavior
• Can draw conclusions from complex relationships
• Can provide portable knowledge
• Can deal with uncertainty
• Not widely used or tested

13. Characteristics of an Expert System
• Limited to relatively narrow problems
• Cannot readily deal with “mixed” knowledge
• Possibility of error
• Cannot refine its own knowledge
• May have high development costs
• Raise legal and ethical concerns

14. Capabilities of Expert Systems

15. When to Use Expert Systems
• High payoff
• Preserve scarce expertise
• Distribute expertise
• Provide more consistency than humans
• Faster solutions than humans
• Training expertise

16. Components of an Expert System

17. Knowledge Base
• Assembling human experts
• The use of fuzzy logic
• The use of rules
• The use of cases

18. The use of fuzzy logic
The use of rules
Suppose a rule base contains
1. If Fritz is green then Fritz is a frog.
2. If Fritz is a frog then Fritz hops.
Suppose a goal is to conclude that Fritz hops. The rule
base would be searched and rule (2) would be selected
because its conclusion (the then clause) matches the goal.
It is not known that Fritz is a frog, so this "if" statement is
added to the goal list. The rule base is again searched and
this time rule (1) is selected because its then clause
matches the new goal just added to the list.
This time, the if clause (Fritz is green) is known to be true
and the goal that Fritz hops is concluded.

19. Inference Engine
• Seek information and relationships from
the knowledge base and provides
answers, predictions and suggestions in
the way a human expert would
• Backward chaining
• Forward chaining

20. Inference Engines
Comparison of backward and forward chaining
 Backward chaining
A method of reasoning that starts with conclusions
and works backward to the supporting facts.
 Forward chaining
A method of reasoning that starts with the facts and
works forward to the conclusions

21. Forward chaining starts with the data available and
uses the inference rules to conclude more data until
a desired goal is reached. Because the data
available determines which inference rules are used,
this method is also called data driven.
Backward chaining starts with a list of goals and
works backwards to see if there is data which will
allow it to conclude any of these goals.

22. The Knowledge Acquisition Facility

23. Expert Systems Development

24. Participants in Developing and Using
Expert Systems
• Domain expert
• Knowledge engineer
• Knowledge user

25. Participants in Developing and Using
Expert Systems

26. Applications of Expert Systems and
Artificial Intelligence
• Credit granting and loan analysis
• Stock picking
• Catching cheats and terrorists
• Budgeting

27. Applications of Expert System and
Artificial Intelligence
• Information management and retrieval
• Games
• Virus detection
• Hospitals and medical facilities

28. Virtual Reality

29. • Virtual reality (VR) is a technology which allows
a user to interact with a computer-simulated
environment, be it a real or imagined one.
• Most current virtual reality environments are
primarily visual experiences, displayed either on
a computer screen or through special
stereoscopic displays, but some simulations
include additional sensory information, such as
sound through speakers or headphones.
• Some advanced, haptic * systems now include
tactile information, generally known as force
feedback, in medical and gaming applications.
• * sense of touch e.g. effect/vibration
VRS

30. Virtual Reality
• Immersive virtual reality - user becomes
fully immersed in an artificial, three-
dimensional world that is completely
generated by a computer
• Virtual reality system - enables one or
more users to move and react in a
computer-simulated environment

31. Interface Devices
• Head-mounted display (HMD)
• Binocular Omni-Orientation Monitor (BOOM)
• CAVE
– A Cave Automatic Virtual Environment (better known by the recursive
acronym CAVE) is an immersive virtual reality environment where projectors are
directed to three, four, five or six of the walls of a room-sized cube.
• Haptic interface

32. List of CAVEs at universities
• The Virtual Reality Applications Center (VRAC) at Iowa State
University is the home to a pair of CAVEs: a 4 walled CAVE and a 6
walled CAVE (renovated in 2007, highest resolution CAVE in
world).
• Duke University has the DiVE, a 6-walled 'CAVE'.
• Indiana University had a 4-wall CAVE at the Bloomington campus
and now has a 4-wall Barco MoVE Lite at the Indianapolis campus
operated by the Advanced Visualization Lab
• University of Reading owns a CAVE which is used for several
research projects including medical visualisation.
• United Arab Emirates University A 3-walled CAVE used in
Architectural Education and Research. ([5])
• University College Dublin has a 4-walled front-projected CAVE.
• University of California, San Diego has a CAVE operated by the
Immersive Visualization Lab in the Atkinson Hall building, also
known as the Calit2 (California Institute for Telecommunications
and Information Technology) building. (www.calit2.net)
• University of Michigan owns a CAVE.
• Newcastle University has a 3-walled CAVE as part of its Virtual
Reality Suite.

33. • CAVEs are used for many things.
• Engineering companies use CAVEs to
enhance product development.
• Prototypes of parts can be created and
tested, interfaces can be developed, and
factory layouts can be simulated, all before
spending any money on physical parts

34. Interface Devices
• Head-mounted display (HMD)

35. Fig 11.14

36. Fig 11.15

38. Useful Applications
• Medicine – used to link stroke patients to
physical therapists
• Education and training – used by military for
aircraft maintenance
• Entertainment
– Star Wars Episode II: Attack of the Clones

39. Useful Applications
• Real Estate Marketing and Tourism
– Used to increase real estate sales
– Virtual reality tour of the White House

40. Summary
• Artificial intelligence - used to describe computers with ability to
mimic or duplicate functions of the human brain
• Intelligent behavior - includes the ability to learn from experience
• Expert systems - can explain their reasoning (or suggested
decisions) and display intelligent behavior
• Virtual reality systems - enables one or more users to move and
react in a computer-simulated environment
• Special-purpose systems - assist organizations and individuals in
new and exciting ways. For example, Segway