Download CS6659 AI UNIT 1 Notes PDF

TitleCS6659 AI UNIT 1 Notes
TagsTechnology Artificial Intelligence Reason Thought Knowledge Representation And Reasoning
File Size1012.5 KB
Total Pages47
Document Text Contents
Page 1

CS6659 – ARTIFICIAL INTELLIGENCE

1

UNIT I

INTRODUCTION TO Al AND PRODUCTION SYSTEMS

CHAPTER - 1

What is Artificial Intelligence?

1. INTELLIGENCE

 The capacity to learn and solve problems.

 In particular,

 the ability to solve novel problems (i.e solve new problems)

 the ability to act rationally (i.e act based on reason)

 the ability to act like humans



1.1 What is involved in intelligence?

• Ability to interact with the real world

– to perceive, understand, and act

– e.g., speech recognition and understanding and synthesis

– e.g., image understanding

– e.g., ability to take actions, have an effect



• Reasoning and Planning

– modeling the external world, given input

– solving new problems, planning, and making decisions

– ability to deal with unexpected problems, uncertainties



• Learning and Adaptation

– we are continuously learning and adapting

– our internal models are always being ―updated‖

• e.g., a baby learning to categorize and recognize animals

2. ARTIFICIAL INTELLIGENCE

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

The term AI is defined by each author in own ways which falls into 4 categories

1. The system that think like humans.

2. System that act like humans.

3. Systems that think rationally.

4. Systems that act rationally.

Page 2

CS6659 – ARTIFICIAL INTELLIGENCE

2

2.1 SOME DEFINITIONS OF AI

 Building systems that think like humans

―The exciting new effort to make computers think … machines with minds, in the

full and literal sense‖ -- Haugeland, 1985

―The automation of activities that we associate with human thinking, … such as

decision-making, problem solving, learning, …‖ -- Bellman, 1978



 Building systems that act like humans

―The art of creating machines that perform functions that require intelligence

when performed by people‖ -- Kurzweil, 1990

―The study of how to make computers do things at which, at the moment, people

are better‖ -- Rich and Knight, 1991



 Building systems that think rationally

―The study of mental faculties through the use of computational models‖ --

Charniak and McDermott, 1985

―The study of the computations that make it possible to perceive, reason, and act‖

-Winston, 1992



 Building systems that act rationally

―A field of study that seeks to explain and emulate intelligent behavior in terms of

computational processes‖ -- Schalkoff, 1990

―The branch of computer science that is concerned with the automation of intelligent

behavior‖ -- Luger and Stubblefield, 1993

2.1.1. Acting Humanly: The Turing Test Approach

 Test proposed by Alan Turing in 1950

 The computer is asked questions by a human interrogator.

The computer passes the test if a human interrogator, after posing some written questions, cannot

tell whether the written responses come from a person or not. Programming a computer to pass,

the computer need to possess the following capabilities:

 Natural language processing to enable it to communicate successfully in English.

 Knowledge representation to store what it knows or hears

 Automated reasoning to use the stored information to answer questions and to draw new

conclusions.

 Machine learning to adapt to new circumstances and to detect and extrapolate patterns.

To pass the complete Turing Test, the computer will need

 Computer vision to perceive the objects, and

 Robotics to manipulate objects and move about.

Page 23

CS6659 – ARTIFICIAL INTELLIGENCE

23

• For real world problems, it is often useful to introduce heuristics based on relatively

unstructured knowledge. It is impossible to define this knowledge in such a way that

mathematical analysis can be performed.

• In AI approaches, behavior of algorithms are analyzed by running them on computer as

contrast to analyzing algorithm mathematically.

•There are at least many reasons for the adhoc approaches in AI.

 It is a lot more fun to see a program do something intelligent than to prove it.

 AI problem domains are usually complex, so generally not possible to produce

analytical proof that a procedure will work.

 It is even not possible to describe the range of problems well enough to make

statistical analysis of program behavior meaningful.

•But still it is important to keep performance question in mind while designing algorithm.

•One of the most important analysis of the search process is straightforward i.e., ―Number

of nodes in a complete search tree of depth D and branching factor F is F*D‖.

•This simple analysis motivates to

 Look for improvements on the exhaustive search.

 Find an upper bound on the search time which can be compared with exhaustive

search procedures.

2.4 PROBLEM CHARACTERISTICS

Heuristic search is a very general method applicable to a large class of problem. In order to

choose the most appropriate method (or combination of methods) for a particular problem it is

necessary to analyze the problem along several key dimensions:

2.4.1 Is the problem decomposable into a set of independent smaller sub problems?

Example: Suppose we want to solve the problem of computing the integral of the following

expression ∫(x
2
+ 3x + sin

2
x * cos

2
x) dx

Page 47

CS6659 – ARTIFICIAL INTELLIGENCE

47

9. Explain how the steepest accent hill climbing works?

10. Write in detail about the mean end analysis procedure with example?

Similer Documents