Artificial Intelligence (Sistemas Inteligentes) Pedro Cabalar - - PowerPoint PPT Presentation

Artificial Intelligence

(Sistemas Inteligentes)

Pedro Cabalar

• Depto. Computación

Universidade da Coruña, SPAIN

Chapter 1. Introduction

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 1 / 13

Outline

1

A definition of AI

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 2 / 13

A definition of AI

1

A definition of AI

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 3 / 13

A definition of AI

A bit of debate: what is AI?

Take 5 min. to tell what Artificial Intelligence (AI) is all about. . .

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 4 / 13

A definition of AI

A bit of debate: what is AI?

Take 5 min. to tell what Artificial Intelligence (AI) is all about. . . Debate: half class will be defenders and half class attackers.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 4 / 13

A definition of AI

A bit of debate: what is AI?

Take 5 min. to tell what Artificial Intelligence (AI) is all about. . . Debate: half class will be defenders and half class attackers. What is AI? It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. John McCarthy http://www-formal.stanford.edu/jmc/whatisai/ node1.html

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 4 / 13

A definition of AI

A bit of debate: what is AI?

What do you think? Give arguments why AI is a . . .

1

science

2

engineering

3

a constant disappointment

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 5 / 13

A definition of AI

What is AI?

A keypoint: what is intelligence?

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 6 / 13

A definition of AI

What is AI?

A keypoint: what is intelligence? A definition will depend on human intelligence and we ignore many of its mechanisms.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 6 / 13

A definition of AI

What is AI?

A keypoint: what is intelligence? A definition will depend on human intelligence and we ignore many of its mechanisms. The perception of intelligent behavior has changed along History. Example: a calculator looked intelligent a hundred years ago while some wouldn’t say a chess program looks intelligent today.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 6 / 13

A definition of AI

What is AI?

A keypoint: what is intelligence? A definition will depend on human intelligence and we ignore many of its mechanisms. The perception of intelligent behavior has changed along History. Example: a calculator looked intelligent a hundred years ago while some wouldn’t say a chess program looks intelligent today. Many definitions of AI have been made. They can be classified as follows:

Imitating human behavior: thinking like humans vs acting like humans

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 6 / 13

A definition of AI

What is AI?

A keypoint: what is intelligence? A definition will depend on human intelligence and we ignore many of its mechanisms. The perception of intelligent behavior has changed along History. Example: a calculator looked intelligent a hundred years ago while some wouldn’t say a chess program looks intelligent today. Many definitions of AI have been made. They can be classified as follows:

Imitating human behavior: thinking like humans vs acting like humans Focus on rational behaviour: thinking rationally vs acting rationally

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 6 / 13

A definition of AI

Acting humanly: the Turing test

Alan Turing (1912 - 1954) The Turing test: we have two terminals A=controlled by a computer; B=with a human behind.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 7 / 13

A definition of AI

Acting humanly: the Turing test

Alan Turing (1912 - 1954) The Turing test: we have two terminals A=controlled by a computer; B=with a human behind. C is a human interrogator that must find out who is who.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 7 / 13

A definition of AI

Acting humanly: the Turing test

Alan Turing (1912 - 1954) The Turing test: we have two terminals A=controlled by a computer; B=with a human behind. C is a human interrogator that must find out who is who. We say A is intelligent = C cannot tell.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 7 / 13

A definition of AI

Acting humanly: the Turing test

Can you imagine what A should be capable of?

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 8 / 13

A definition of AI

Acting humanly: the Turing test

Can you imagine what A should be capable of?

Natural language Knowledge representation Automated reasoning Machine learning

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 8 / 13

A definition of AI

Acting humanly: the Turing test

Can you imagine what A should be capable of?

Natural language Knowledge representation Automated reasoning Machine learning

“Total” Turing test: includes video signal, perception and exchange

• f physical objects. . .

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 8 / 13

A definition of AI

Acting humanly: the Turing test

Can you imagine what A should be capable of?

Natural language Knowledge representation Automated reasoning Machine learning

“Total” Turing test: includes video signal, perception and exchange

• f physical objects. . .

Computer Vision Robotics

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 8 / 13

A definition of AI

Acting humanly: the Turing test

Can you imagine what A should be capable of?

Natural language Knowledge representation Automated reasoning Machine learning

“Total” Turing test: includes video signal, perception and exchange

• f physical objects. . .

Computer Vision Robotics

These are the six main areas of AI and became the real goal, rather than the test itself.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 8 / 13

A definition of AI

Thinking humanly: Cognitive modeling

Cognitive Science tries to join AI models with experimental techniques from Psychology to build (testable) theories about the human mind.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 9 / 13

A definition of AI

Thinking humanly: Cognitive modeling

Cognitive Science tries to join AI models with experimental techniques from Psychology to build (testable) theories about the human mind. Two ways of tackling the problem of cognitive modeling:

1

Symbolic modeling: Use knowledge-based systems to capture abstract mental functions handling symbols. Marvin Minsky’s school. Marvin Minsky (1927-)

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 9 / 13

A definition of AI

Thinking humanly: Cognitive modeling

Cognitive Science tries to join AI models with experimental techniques from Psychology to build (testable) theories about the human mind. Two ways of tackling the problem of cognitive modeling:

1

Symbolic modeling: Use knowledge-based systems to capture abstract mental functions handling symbols. Marvin Minsky’s school. Marvin Minsky (1927-)

2

Subsymbolic modeling: try to follow the neural and associative properties of the human brain using connectionst/neural network models.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 9 / 13

A definition of AI

Thinking humanly: Cognitive modeling

Cognitive Science tries to join AI models with experimental techniques from Psychology to build (testable) theories about the human mind. Two ways of tackling the problem of cognitive modeling:

1

Symbolic modeling: Use knowledge-based systems to capture abstract mental functions handling symbols. Marvin Minsky’s school. Marvin Minsky (1927-)

2

Subsymbolic modeling: try to follow the neural and associative properties of the human brain using connectionst/neural network models.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 9 / 13

A definition of AI

Thinking rationally: laws of thought

John McCarthy (1927-2011) Logicist tradition in AI. John McCarthy’s school.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 10 / 13

A definition of AI

Thinking rationally: laws of thought

John McCarthy (1927-2011) Logicist tradition in AI. John McCarthy’s school. Logic: solid background since Aristotle. Three chronological eras: Philosophy, Mathematics and Computational Logic.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 10 / 13

A definition of AI

Thinking rationally: laws of thought

John McCarthy (1927-2011) Logicist tradition in AI. John McCarthy’s school. Logic: solid background since Aristotle. Three chronological eras: Philosophy, Mathematics and Computational Logic. Obstacles: too rigid for dealing with uncertainty; high computational cost for practical problems.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 10 / 13

A definition of AI

Thinking rationally: laws of thought

John McCarthy (1927-2011) Logicist tradition in AI. John McCarthy’s school. Logic: solid background since Aristotle. Three chronological eras: Philosophy, Mathematics and Computational Logic. Obstacles: too rigid for dealing with uncertainty; high computational cost for practical problems. All AI systems must face these same obstacles, but they appeared first in the logicist tradition.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 10 / 13

A definition of AI

Acting rationally: rational agent

Agent = “something that acts”

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 11 / 13

A definition of AI

Acting rationally: rational agent

Agent = “something that acts” Computer agents are expected to: operate autonomously, perceiving the environment, adapting to change, taking on another’s goals, etc.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 11 / 13

A definition of AI

Acting rationally: rational agent

Agent = “something that acts” Computer agents are expected to: operate autonomously, perceiving the environment, adapting to change, taking on another’s goals, etc. A rational agent should achieve the best outcome or, when there is uncertainty, the best expected outcome.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 11 / 13

A definition of AI

Acting rationally: rational agent

Agent = “something that acts” Computer agents are expected to: operate autonomously, perceiving the environment, adapting to change, taking on another’s goals, etc. A rational agent should achieve the best outcome or, when there is uncertainty, the best expected outcome. Note that making correct inferences (logicist approach) is sometimes part of a rational agent. Other actions (example: reflect reactions) can also be rational but not inferential.

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 11 / 13

A definition of AI

Acting rationally: rational agent

Agent = “something that acts” Computer agents are expected to: operate autonomously, perceiving the environment, adapting to change, taking on another’s goals, etc. A rational agent should achieve the best outcome or, when there is uncertainty, the best expected outcome. Note that making correct inferences (logicist approach) is sometimes part of a rational agent. Other actions (example: reflect reactions) can also be rational but not inferential. Computational limitations make perfect rationality unachievable: → design best program for given machine resources

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 11 / 13

A definition of AI

AI prehistory

Philosophy logic, methods of reasoning mind as physical system foundations of learning, language, rationality Mathematics formal representation and proof algorithms, computation, (un)decidability, (in)tractability probability Psychology adaptation phenomena of perception and motor control experimental techniques (psychophysics, etc.) Economics formal theory of rational decisions Linguistics knowledge representation grammar Neuroscience plastic physical substrate for mental activity Control theory homeostatic systems, stability simple optimal agent designs

Pedro Cabalar (UDC) ( Depto. Computación Universidade da Coruña, SPAIN ) AI Chapter 1. Introduction 12 / 13

A definition of AI

Potted history of AI

1943 McCulloch & Pitts: Boolean circuit model of brain 1950 Turing’s “Computing Machinery and Intelligence” 1952–69 Look, Ma, no hands! 1950s Early AI programs, including Samuel’s checkers program, Newell & Simon’s Logic Theorist, Gelernter’s Geometry Engine 1956 Dartmouth meeting: “Artificial Intelligence” adopted 1965 Robinson’s complete algorithm for logical reasoning 1966–74 AI discovers computational complexity Neural network research almost disappears 1969–79 Early development of knowledge-based systems 1980–88 Expert systems industry booms 1988–93 Expert systems industry busts: “AI Winter” 1985–95 Neural networks return to popularity 1988– Resurgence of probability; general increase in technical depth “Nouvelle AI”: ALife, GAs, soft computing 1995– Agents, agents, everywhere . . . 2003– Human-level AI back on the agenda

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