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(Fundamentals of)
Artificial Intelligence
(and Knowledge-based
Systems)
Artificial Intelligence
... this course state-space representation basic search
INTRODUCTION to...
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The mind beaten by the machine?
Is chess playing a proof of intelligent behaviour?
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Other examples of success:
Chatbot Alice:
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> Im Daniel
A dialogue with Alice-bot:
Nice to meet you Daniel.> Do you know Clinton?
He was the second Impeached President of the UnitedStates.
> Do you like him?Sure he has never done anything to me.
> What is an impeachment?
It is a good question. I have to process that one for a
while. I would do a search for it.> Who are you?
I ask myself that question, "Who am I?"
> What do you do?
I am chatting with people on the net.
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Dialogue continued:
> What is your favorite topic?My favorite subject is artificial intelligence.
> What is artificial intelligence?
Artificial intelligence is the branch of engineering and
science devoted to constructing machines that think. Ican ask someone about it.
> Can you do anything else than chatting?
Doing anything else than chatting is not among my
strongest traits....
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Dialogue continued (2):
> How old am I?
Is this a trick question? You said you were how many yearsold? My guess is that you are really a kid.
> What would you like to know about me?Tell me about your educational background.
> I am a professor in computer scienceYou are a professor in computer science? How do you likeyour work?
> Its kind of nice teaching courses on artificial intelligenceI guess it is a lot of fun.
> What should I tell the students about you?Be sure to think before you speak. State your point clearlyand slowly and gauge the listener's response before goingany further.
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Is Alice intelligent?
ABSOLUTELY NOT !
~ 50000 fairly trivial input-response rules.+ some pattern matching
+ some knowledge
+ some randomness
NO reasoning component
BUT: demonstrates human-like behaviour.
Won the turing award
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Other examples of success (2):
Data-mining:
Which characteristics in the 3-dimensionalstructure of new molecules indicate that they maycause cancer ??
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Data mining:
An application of Machine Learning techniques It solves problems that humans can not solve,because the data involved is too large ..
Detecting cancerrisk molecules isone example.
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Data mining:
A similar application: In marketing products ...
Predicting customerbehavior insupermarkets isanother.
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Many other applications:
In language and speech processing:
In robotics:
Computervision:
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Interest in AI is not new !
A scene from the 17-hundreds:
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About intelligence ...
When would we consider a program intelligent ?
When do we consider a creative activity of humans
to require intelligence ?
Default answers : Never? / Always?
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Does numeric computation
require intelligence ?
For humans? Xcalc3921 , 56
x 73 , 13286 783 , 68
For computers?
Also in the year 1900 ?
When do we consider a program intelligent?
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To situate the question:
Two different aims of AI:
Long term aim:develop systems that achieve a level of intelligence
similar / comparable / better? than that of humans.
not achievable in the next 20 to 30 years
Short term aim:on specific tasks that seem to require intelligence:
develop systems that achieve a level of intelligencesimilar / comparable / better? than that of humans.
achieved for very many tasks already
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The long term goal:
The Turing Test
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The meta-Turing test
The meta-Turing test counts a thing as intelligent ifit seeks to devise and apply Turing tests to
objects of its own creation.-- Lew Mammel, Jr.
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Reproduction versus Simulation
At the very least in the context of the short termaim of AI:
we do not want to SIMULATE human intelligenceBUT:
REPRODUCE the effect of intelligence
Nice analogy with flying !
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Artificial Intelligence
versus
Natural Flight
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Is the case for most of the
successful applications ! Deep blue
Alice
Data mining Computer vision
...
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To some extent, we DO simulate:
Artificial Neural Nets:
A VERY ROUGH imitation of a brain structure
Work very well for learning, classifying and patternmatching.
Very robust and noise-resistant.
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Different kinds of AI relate to
different kinds of Intelligence
Some people are very good in reasoning or
mathematics, but can hardly learn to read or spell ! seem to require different cognitive skills!
in AI: ANNs are good for learning and automation
for reasoning we need different techniques
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Which applications are easy ?
For very specialized, specific tasks: AI
Example:ECG-diagnosis
For tasks requiring common sense: AI
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Modeling Knowledge
and managing it .
The LENAT experiment:
15 years of work by 15 to 30 people, trying tomodel the common knowledge in the word !!!!
Knowledge should be learned, not engineered.
AI: are we only dreaming ????
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Multi-disciplinary domain:
Engineering:robotics, vision, control-expert systems, biometrics,
Computer Science:AI-languages , knowledge representation, algorithms,
Pure Sciences:statistics approaches, neural nets, fuzzy logic,
Linguistics:computational linguistics, phonetics en speech,
Psychology:cognitive models, knowledge-extraction from experts,
Medicine:human neural models, neuro-science,...
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Artificial Intelligence is ...
In Engineering and Computer Science:The development and the study of advanced
computer applications, aimed at solving tasksthat - for the moment - are still better
preformed by humans.
Notice: temporal dependency ! Ex. : Prolog
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About this course ...
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Choice of the material.
Few books are really adequate:
E. Rich ( Artificial Intelligence):
good for some parts (search, introduction,knowledge representation), outdated
P.Winston ( Artificial Intelligence):didactically VERY good, but lacks technical depth.
Somewhat outdated.
Norvig & Russel ( AI: a modern approach):
encyclopedic, misses depth.
Poole et. Al ( Computational Intelligence):
very formal and technical. Good for logic.
Selection and synthesis of the best parts of differentbooks.
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Selection of topics:Contents Handbook of AI
Ch.:Artificial Neural Networks
Ch.: Introduction to AI
Ch.: Logic, resolution, inference
Ch.:Search techniques
Ch.:Game playing
Ch.:Knowledge representation
Ch.:Phylosophy of AI
Ch.:Machine Learning
Ch.:Natural Language
Ch.:Planning
not for MAICS and SLT
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Technically: the contents:
- Search techniques in AI(Including games)
- Constraint processing
(Including applications in Vision and language)- Machine Learning
- Planning
- Automated Reasoning(Not for MAI CS and SLT)
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Another dimension to
view the contents:
1. Basic methods for knowledge representationand problem solving.
the course is mainly about AI problemsolving !
2. Elements of some application areas:
learning, planning, image understanding,language understanding
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Contents (3):
Different knowledge
representation formalisms ...
State space representation and productionrules.
Constraint-based representations.
First-order predicate Logic.
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each with their corresponding
general purpose problem solving
techniques:
State space representation an production rules.
Search methods Constraint based formulations.
Backtracking and Constraint-processing
First order predicate Logic.
Automated reasoning (logical inference)
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Contents (4):
Some application areas:
Game playing (in chapter on Search)
Image understanding (in chapter on
constraints) Language understanding (constraints)
Expert systems (in chapter on logic)
Planning
Machine learning
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Aims:
Many different angles could be taken:
Empirical-Experimental AIAlgorithms in AI
Formal methods in AI
Cognitive aspects of AI Applications
Neural Nets
Probabilistics and Information Theory
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Concrete aims:
Provide insight in the basic achievements of AI. Prepares for more application oriented courses on
AI, or on self-study in some application areas
ex.: artificial neural networks, machine learning,computer vision, natural language, etc.
Through case-studies: provide more background inproblem solving. Mostly algorithmic aspects.
Also techniques for representing and modeling.
The 6-study point version: 2 projects for hands-onexperience.
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A missing theme:
AGENTS !
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A missing theme:
AGENTS (2).
Yet, a central theme in recent books !BUT:Have as their main extra contribution:
Communication between system and: other systems/agents
the outside world
In particular, also a useful conceptual model forintegrating different components of an AI system
ex: a robot that combines vision, natural languageand planning
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BUT: no intelligence without
interaction with the world!!
See: experiment in middle-ages.
See also philosophy arguments against AI
Plus: multi-agents is FUN !
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Practical info (FAI)
Exercises: 12.5 OR 20 hours: mainly practice on the main methods/algorithms
presented in the course
important preparation for the examination
Course material: copies of detailed slides
for some parts: supporting texts
Required background: understanding of algorithms (and recursion)
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Practical info (AI)
Exercises: 25 or 22.5 hours: mainly practice on the main methods/algorithms
presented in the course
important preparation for the examination
Course material: copies of detailed slides
for some parts: supporting texts
Required background: understanding of algorithms (and recursion)
B k d T t
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Introduction:
State-space Intro:
Basic search,Heuristic search:
Optimal search:
Advanced search:
Games:
Version Spaces:
Constraints I & II:
Image understanding:
Automated reasoning:
Planning STRIPS:
Planning deductive:
Natural language:
No document
No document
Winston: Ch. Basic search
Winston: Ch. Optimal search
Russel: Ch. 4
Winston: Ch. Adversary search
Winston: Ch. Learning by managing..
Word Document on web page
Winston: Ch. Symbolic constraint
Short text logic (to follow)
Winston: Ch. Planning
Winston: Ch. Planning
Winston: Ch. Frames and Common ...
The basics, but
no complexity
IDA*, SMA*
Almost complete
The essence
Complete
Complete
Intro
Almost complete
Intro
Complete
Background Texts
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Examination
Open-book exercise examination counts for 1/2 of the points
Closed-book theory examination
Together on 1/2 day
The projects (6 pt. Version)
2 projects
Count for 8 out of 20 points
Deadlines to be anounced soon
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Alternative examinations possible:
For 3rdyear BSc
and Initial MScStudents
Designing your own exercise (for each part) andsolving it (not for FAI)
criteria: originality, does the exercise illustrateall aspects of the method, complexity of the
exercise, correctness of the solution