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Artificial Intelligence

Introduction

Fall 2008

professor: Luigi Ceccaroni


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Instructors

Luigi Ceccaroni

Omega building - Office 111

[email protected] Nria Castell Ario

FIB building - Second floor

[email protected]


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Course description

This course introduces:

Representations

Techniques

Architectures

This course also explores applications of:

Rule chaining

Heuristic search

Constraint propagation

Constrained search

Decision trees

Knowledge representation

Knowledge-based systems Natural-language processing

It accounts for 7.2 credits of work load, distributed as:

3.6 credits for theory

2.4 for recitations

1.2 for laboratory


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Web pages

http://www.lsi.upc.es/~bejar/ia/ia.html

http://www.lsi.upc.edu/~luigi/MTI/AI-2008-fall/ai.html

http://raco.fib.upc.es/


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Background

Students need the following knowledge (at theundergraduate level) to appropriately follow the course: English language

Propositional and predicate logic; capacity to formulate a

problem in logical terms Logical inference; strategies of resolution; capacity to solveproblems by resolution

Graph and tree structures; algorithms for search in trees andgraphs

Computational complexity; calculation of algorithm execution's

cost There are assignments that expect students to be able

to read and write basic Java. This is the only formal pre-requisite.


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Aim of the course

The general objectives of the course can besummarized as: To identify the kind of problems that can be solved

using AI techniques; to know the relation between AI

and other areas of computer science. To have knowledge of generic problem-solving

methods in AI.

To understand the role of knowledge in present IA; toknow the basic techniques of knowledge

representation and their use. To be able to apply basic AI techniques as support

for the solution of practical problems.

To be able to navigate the basic bibliography of AI.


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Topics

[ 1.] Search

[1.1] Problem representation

[1.2] Search in state space [1.3] Uninformed search

[1.4] Informed search (A*,IDA*, local search)

[1.5] Games

[1.6] Constraint satisfaction


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Topics

[2.] Knowledge representation and

inference

[2.1] Methodologies for knowledgerepresentation

[2.2] Rule-based systems

[2.3] Structured representations: frames and

ontologies


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Topics

[3.] Knowledge-based systems

[3.1] Definition and architecture

[3.2] Expert systems [3.3] Knowledge engineering

[3.4] Approximate reasoning


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Topics

[ 4.] Natural language

[4.1] Textual, lexical and morphological

analyses

[4.2] Levels of natural language processing

[4.3] Logical formalisms: definite clause

grammars

[4.4] Applications and current areas ofinterest


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Topics

[ 5.] Machine learning

[5.1] Decision trees


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Bibliography

There are no required readings, apartfrom the course lecture notes. Additionalreading can be found in the following text:

Russell, Stuart J. and Peter Norvig

Artificial intelligence: a modern approach. 2nd

edition Upper Saddle River, NJ: Prentice Hall, 2002

ISBN: 0137903952.


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What is AI?

There is no single definition, but severalapproaches, that Russell-Norvigsummarize in four main ones.

These approaches follow different pointsof view.

Their influences are diverse (Philosophy,

Mathematics, Psychology, Biology...). Their fields of application are ample and

interrelated.


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Approaches to AI

Systems that act like humans

The study of how to obtain that computers perform tasks atwhich, at the moment, people are better (Rich and Knight, 1991)

Systems that think like humans

The effort to make computers think... machines with minds in thefull and literal sense (Haugeland, 1985)

Systems that think rationally

The study of the mental faculties through the study ofcomputational models (Charniak and McDermott, 1985)

Systems that act rationally

The effort to explain and emulate the intelligent behavior interms of computational processes (Shalkoff, 1990)


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Other approaches to AI

Good Old-Fashioned Artificial Intelligence

(GOFAI) (Douglas Lenat: Cyc) Cramming a computer full of knowledge about the

real world and methods to manipulate it

Intelligence intimately tied up with having and being

able to use knowledge

Its worth to go to school

Robotic insects example

Legs controlled by a central computer that has a detailed 3D

map of the terrain and knows all the relevant laws of physics

and strategies

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Other approaches to AI

Learning through experience (Rodney

Brooks: Cog, Kismet)

Machines that experience the world in muchthe way human beings do

You can learn stuff on your own

Robotic insects example

Each leg containing a small circuit that tells itabout basic movements

All local computation physically coupled through

the body, with gait emerging spontaneously from

circuits interaction 16


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Areas of work in AI

Theory

Philosophy

Applications


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Turing test


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The Chinese room (John Searle)


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Eliza (1966)

Conversation with ELIZA

E> Hello. I am ELIZA. How can I help you?

P> I am feeling sad.

E> I am sorry to hear you are sad.P> I had some nightmares tonight.

E> You say you had some nightmares tonight?

P> Yes, my dreams are worrying me.

E>What does that dream suggest to you?

P> Thrill, scare.E> I am not sure I understand you fully.

P> My dreams annoy me.

E> Do you dream often?

P> Yes, daily.


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Lost in translation

English Russian (or Spanish) English

The spirit is willing, but the flesh is weak

,

The vodka is good, but the meat is rotten

(Actually: Spirit is willingly ready, but flesh is weak or

The alcohol is arranged, but the meat is weak)


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Autonomous robots


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Personalization,

recommendation


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US District Court judge

John E. Jones III

Proponents of intelligent design arguedthat their supposedly scientific alternativeto evolutionary theory should be

presented in biology classes.

An objective student can reasonably infer

that the schools favored view is areligious one, and that the school isaccordingly sponsoring a form of religion.


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One book

What if I want to read just one book about

artificial intelligence?

Darwin's Dangerous Idea by Daniel Dennett

In favor of materialistic Darwinism

Victims: Noam Chomsky, Roger Penrose, John

Searle and, specially, Stephen Jay Gould

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