Transcript Artificial Intelligence

Artificial Intelligence
Introduction
Fall 2008
professor: Luigi Ceccaroni
Instructors
• Luigi Ceccaroni
– Omega building - Office 111
– [email protected]
• Núria Castell Ariño
– FIB building - Second floor
– [email protected]
Course description
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This course introduces:
– Representations
– Techniques
– Architectures
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This course also explores applications of:
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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
Web pages
• http://www.lsi.upc.es/~bejar/ia/ia.html
• http://www.lsi.upc.edu/~luigi/MTI/AI-2008fall/ai.html
• http://raco.fib.upc.es/
Background
• Students need the following knowledge (at the
undergraduate 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 solve
problems by resolution
– Graph and tree structures; algorithms for search in trees and
graphs
– 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 prerequisite.
Aim of the course
• The general objectives of the course can be
summarized 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; to
know 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.
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
Topics
• [2.] Knowledge representation and
inference
– [2.1] Methodologies for knowledge
representation
– [2.2] Rule-based systems
– [2.3] Structured representations: frames and
ontologies
Topics
• [3.] Knowledge-based systems
– [3.1] Definition and architecture
– [3.2] Expert systems
– [3.3] Knowledge engineering
– [3.4] Approximate reasoning
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 of
interest
Topics
• [ 5.] Machine learning
– [5.1] Decision trees
Bibliography
• There are no required readings, apart
from the course lecture notes. Additional
reading 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.
What is AI?
• There is no single definition, but several
approaches, that Russell-Norvig
summarize in four main ones.
• These approaches follow different points
of view.
• Their influences are diverse (Philosophy,
Mathematics, Psychology, Biology...).
• Their fields of application are ample and
interrelated.
Approaches to AI
• Systems that act like humans
– The study of how to obtain that computers perform tasks at
which, 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 the
full and literal sense (Haugeland, 1985)
• Systems that think rationally
– The study of the mental faculties through the study of
computational models (Charniak and McDermott, 1985)
• Systems that act rationally
– The effort to explain and emulate the intelligent behavior in
terms of computational processes (Shalkoff, 1990)
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
– “It’s 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 much
the way human beings do
– “You can learn stuff on your own”
– Robotic insects example
• Each leg containing a small circuit that tells it
about basic movements
• All local computation physically coupled through
the body, with gait emerging spontaneously from
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circuits’ interaction
Areas of work in AI
• Theory
• Philosophy
• Applications
Turing test
The Chinese room (John Searle)
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.
Lost in translation
English → Russian (or Spanish)→ English
“The spirit is willing, but the flesh is weak” →
→ Дух охотно готов, но плоть слаба →
→ “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”)
Autonomous robots
Personalization,
recommendation
US District Court judge
John E. Jones III
• Proponents of intelligent design argued
that their supposedly scientific alternative
to evolutionary theory should be
presented in biology classes.
• “An objective student can reasonably infer
that the school’s favored view is a
religious one, and that the school is
accordingly sponsoring a form of religion.”
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