artificial intelligence cis 479/579 bruce r. maxim um-dearborn

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Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

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Page 1: Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

Artificial Intelligence

CIS 479/579

Bruce R. Maxim

UM-Dearborn

Page 2: Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

Overview

• What is artificial intelligence (AI)?• Why study AI?• How can you tell whether a machine or

program is intelligent?• What kinds of things (if any) can be

learned by a machine?• When can (should) machines replace

human experts?

Page 3: Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

What is AI?

• AI is the study of ideas that enable computers to be intelligent

• Intelligence might be defined as the capacity to acquire and apply knowledge

• Ai is the part of computer science concerned with design of computer systems that exhibit human intelligence

Page 4: Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

AI Problem Characteristics

• Decomposable to smaller or easier problems• Solution steps can be ignored or undone• Predictable problem universe• Good solutions are obvious• Uses internally consistent knowledge base• Requires lots of knowledge or uses

knowledge to constrain solutions• Requires periodic interaction between human

and computer

Page 5: Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

Goals of AI

• To make computers more useful by letting them take over dangerous or tedious tasks from human

• Understand principles of human intelligence

Page 6: Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

Physical Symbol Hypothesis(Newell and Simon)

• Given enough symbols related to one another• Given collection of operators capable of

producing new symbol expressions • You have the necessary & sufficient

conditions for intelligent behavior• The problem is can this critical mass be

achieved in real-time within the memory capacity of some machine

Page 7: Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

AI Technique Characteristics

• They capture generalizations• Can be understood by domain expert• Easily modified to correct errors or

reflect changes in the world view• Used in lots of different situations• Reduce its own size/bulk by narrowing

the range of possibilities it must consider at any given time

Page 8: Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

AI System Taxonomy(easiest to hardest)

• Data acquisition• Expert System• Problem Solving• Automatic Programming• Robotic Control• Pattern Recognition• Natural Language Processing• Vision & Scene Analysis

Page 9: Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

Turing Test

• Used as criteria for evaluating success of AI system

• Inquisitor, AI System, Human• Inquisitor connected to AI System and Human

by “wire” and free to ask either any question• If Inquisitor can not determine which is the

Human without guessing AI System is successful

Page 10: Artificial Intelligence CIS 479/579 Bruce R. Maxim UM-Dearborn

AI Success Criteria

• Is the task clearly defined?

• Is there an implemented procedure performing the task?

• Is there an identifiable set of regularities or constraints that the procedure uses to derive its power?