Artificial Intelligence

Definition:

Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better.

The Turing Test

According to this test, a computer could be considered to be thinking only when a human interviewer, conversing with bothan unseen human being and an unseen computer, could not determine which is which.

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Artificial Real Items

Airplanes BirdsSilk Flowers FlowersArtificial Snow Snow

AI Major Areas

- Expert Systems - Natural Language Processor - Speech Recognition - Robotics - Computer Vision - Intelligent Computer-Aided Instruction - Data Mining - Genetic Algorithms

Artificial vs. Natural (Human) Intelligence

AI Advantages

1. AI is permanent 2. AI offers ease of duplication 3. AI can be less expensive than natural intelligenc 4. AI is consistent 5. AI can be documented

Natural Intelligence Advantages

1. Natural intelligence is creative. 2. Natural intelligence uses sensory experience directly, whereas most AI systems must work with symbolic input. 3. Human reasoning is able to make use at all times of a very wide context experience and bring that to bear on individual problems, where as AI systems typically gain their power by having a very narrow domain.

Characteristics of a Human Experts

- Recognize and formulate the problem - Solve the problem fairly quickly - Explain the solution - Learn from experience - Restructure knowledge - Break rules - Determine relevance - Degrade gracefully

What Do Experts Know?

It is estimated that a world-class expert, such as a chessgrandmaster, has 50,000 to 100,000 chunks of heuristic information about his/her specialty. On the average, ittakes at least 10 years to acquire 50,000 rules.

Expert Systems

Expert Systems Components

1. Knowledge Acquisition 2. Knowledge Base 3. Inference Engine 4. User Interface 5. Explanation Facility 6. Knowledge Refining System

Different Categories of Expert SystemsCategory Problem Addressed

Interpretation Inferring situation description from observations

Prediction Inferring likely consequences of given situations

Diagnosis Inferring systems malfunctions from observations

Design Configuring objects under constraints

Planning Developing plans to achieve goals

Monitoring Comparing observations to plan vulnerabilities

Debugging Prescribing remedies for malfunctions

Repair Executing a plan to administer a prescribed remedy

Control Interpreting, predicting, repairing, and monitoring

system behavior

What Tasks Are ES Right For?

- Payroll, Inventory

- Simple Tax Returns

- Database Management

- Mortgage Computation

- Regression Analysis

- Facts are Known

- Expertise is Cheap

Too Easy - Use Conventional Software

What Tasks Are ES Right For?

- Diagnosing and Troubleshooting

- Analyzing Diverse Data

- Production Scheduling

- Equipment Layout

- Advise on Tax Shelter

- Facts are known but not precisely

- Expertise is expensive but available

Just Right

What Tasks Are ES Right For?

- Designing New Tools

- Stock Market Forecast

- Discovering New Principles

- Common Sense Problems

- Requires Innovation or Discovery

- Expertise is not available

Too Hard - Requires Human Intelligence

Problems and Limitations

of Expert Systems

- Knowledge is not always readily available. - Expertise is hard to extract from humans. - ES work well only in a narrow domain. - The approach of each expert to problem under consideration may be different, yet correct.

Necessary Requirements for

ES Development

- The task does not require common sense. - The task requires only cognitive, not physical, skills. - There is an expert who is willing to cooperate. - The experts involved can articulate their methods of problem solving. - The task is not too difficult. - The task is well understood, and is defined clearly. - The task definition is fairly stable. - Problem must be well bounded and narrow.

Justification for

ES Development

- The solution to the problem has a high payoff. - The ES can capture scarce human expertise so it will not be lost. - The expertise is needed in many locations. - The expertise is needed in hostile or hazardous environment. - The system can be used for training. - The ES is more dependable and consistent than human expert.

Feasibility Study

A. Financial Feasibility Cost of system development Cost of maintenance Payback period Cash flow analysis

B. Technical Feasibility Interface requirements Network issues Availability of data and knowledge

Security of confidential knowledge Knowledge representation scheme Hardware/software availability Hardware/software compatibility

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C. Operational Feasibility Availability of human resources Priority compare to other projects Implementation issues Management and user support Availability of experts Availability of knowledge engineers