Week 11 12 chap11 c-2

Specialized Business Information Systems

Chapter 11

An Overview of Artificial Intelligence

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)

The Nature of Intelligence

• Learn from experience & apply the knowledge• Handle complex situations• Solve problems when important information is

missing• Determine what is important

The Nature of Intelligence

• React quickly & correctly to new situations • Understand visual images• Process & manipulate symbols• Be creative & imaginative• Use heuristics

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The Difference Between Natural and Artificial Intelligence (continued)

The Major Branches of ArtificialIntelligence

An Overview of Expert Systems

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.

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• 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

ES applications:

From airport tarmacs to online job banks to medical labs, artificial intelligence is

everywhere.

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

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

Capabilities of Expert Systems

When to Use Expert Systems

• High payoff• Preserve scarce expertise• Distribute expertise• Provide more consistency than humans• Faster solutions than humans• Training expertise

Components of an Expert System

Knowledge Base

• Assembling human experts

• The use of fuzzy logic

• The use of rules

• The use of cases

The use of fuzzy logicThe 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.

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

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

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.

The Knowledge Acquisition Facility

Expert Systems Development

Participants in Developing and Using Expert Systems

• Domain expert• Knowledge engineer• Knowledge user

Participants in Developing and Using Expert Systems

Applications of Expert Systems and Artificial Intelligence

• Credit granting and loan analysis• Stock picking• Catching cheats and terrorists• Budgeting

Applications of Expert System and Artificial Intelligence

• Information management and retrieval• Games• Virus detection• Hospitals and medical facilities

Virtual Reality

• 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

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

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

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.

• 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

Interface Devices

• Head-mounted display (HMD)

Fig 11.14

Fig 11.15

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

Useful Applications

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

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