AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

Introduction

Chapter 1

Artificial Intelligence Course

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

What Is Artificial Intelligence (AI)? (1/2)

� Branch of computer science that is concernedwith the automation of intelligent behavior.

� Design and study of computer programs thatbehave intelligently

� Study of how to make computers do things atwhich, at the moment, people are better.

� Designing computer programs to make computerssmarter.

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

What Is Artificial Intelligence (AI)? (2/2)

� Develop programs that respond flexibly insituations that were not specifically�House-cleaning robots

�Perceive its surroundings

�Navigate on the floor

�Respond to events

�Decide what to do next

�Space exploration

� Synonyms of AI: machine intelligence

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

History of AI

Symbolic AI

1943: Production rules 1956: “Artificial Intelligence” 1958: LISP AI language1965: Resolution theorem proving

1970: PROLOG language1971: STRIPS planner1973: MYCIN expert system1982-92: Fifth generation computer systems project1986: Society of mind

1994: Intelligent agents

Subsymbolic AI

1943: McCulloch-Pitt’s neurons 1959: Perceptron1965: Cybernetics1966: Simulated evolution1966: Self-reproducing automata

1975: Genetic algorithm1982: Neural networks1986: Connectionism1987: Artificial life

1992: Genetic programming1994: DNA computing

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

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AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

Research Goals

� Making machines more useful

� Understanding intelligence

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

Comparison of AI with ConventionalProgramming

Artificial Intelligence

a. primarily symbolic b. heuristic search (solution steps implicit) c. control structure usually separate from domain knowledge d. usually easy to modify, update and enlarge e. some incorrect answers often tolerable f. satisfactory answers usually

acceptable

Conventional computer programming

a'. algorithmic (solutions steps explicit) b'. primarily numeric c'. information and control integrated together d'. difficult to modify

e'. correct answers required

f'. best possible solution usually sought

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

1.1 AI in Practice

� Examples of AI Systems�Language translation systems�Air traffic control systems�Supervisory systems (intelligent buildings)�Automated personal assistants(softbots)�Intelligent highways�Robots for hazardous conditions�Medical diagnosis�Factory automation�Finance and business

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

AI in Practice: Space Exploration

� The camera image was taken byNASA’s Viking 1 Orbiter spacecraftwhile searching for a landing site forthe Viking 2 Lander.

A prototype mobile robotdesigned by researchers atNASA’s Jet Propulsion Lab forexploring the Martian surface

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

AI in Practice

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

AI in Practice

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

1.2 AI Theory

� Key elements for realizing AI�Representation�Reasoning�Planning�Learning

� Examples of AI Theory� Inferring structure from motion in machine vision� Finding consistent hypothesis in learning� Probabilistic inference in diagnostic reasoning� Search in automated planning� Parsing sentences in language understanding

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

1.3 Identifying and Measuring Intelligence

� Turing test: an intelligence test for computers

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

1.4 Computational Theories of Behavior

� Representation (Figs. 1.2 and 1.3):A formal system or set of mathematical conventions bywhich the types of information that play a role in thetheory are made explicit.

� Syntax and semantics�Representation = notation (syntax) + denotation (semantics) + computation�Syntax: checks well-formedness�Semantics: assigns true or falsee.g.: "New York is the closest city to Boston"

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

Representations (Examples #1)

� Fig 1.2: Schematic description of a computational theoryconcerned with processing camera images to producegraphical representations of polyhedral objects

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

Representations (Examples #2)

� Fig 1.3: Alternative representations for a system of roads

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

1.5 Automated Reasoning (1/3)

� Automated reasoning: output conclusions from world-

knowledge representation

� Inference and symbolic manipulation

� Knowledge of physics: representation of problems in

mathematical symbols.

� Knowledge of calculus: manipulation of symbols by

rules for integration and differentiation (= inference)

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

1.5 Automated Reasoning (2/3)

� Representing common-sense knowledgee.g: John is at home. He has to drive 40 kms to get to work. He obeys the 80-km-per-hour speed limit. ⇒ It will take John at least a half hour to get to work.

� Rules of inference used to arrive at thisconclusion:�Universal instantiation

�Modus ponens

AI Lecture Notes (C)1999 SNU Dept. of Computer Engineering

1.5 Automated Reasoning (3/3)

� Combinatorial problems and search:�Many AI problems involve many separate decisions

that tend to depend on one another in complicated ways ⇒ search techniques

� Complexity and expressivity�O(n) vs. NP-complete problems�First-order predicate logic