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(Fundamentals of) Artificial Intelligence (and Knowledge-based Systems)

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(Fundamentals of) Artificial Intelligence (and Knowledge-based Systems) INTRODUCTION to... Artificial Intelligence... this course state-space representation – PowerPoint PPT presentation

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Title: (Fundamentals of) Artificial Intelligence (and Knowledge-based Systems)


1
(Fundamentals of) Artificial Intelligence (and
Knowledge-based Systems)
INTRODUCTION to...
Artificial Intelligence ... this course
state-space representation basic search
2
The mind beaten by the machine?
  • Is chess playing a proof of intelligent behaviour?

3
Other examples of success
  • Chatbot Alice

4
A dialogue with Alice-bot
gt Im Daniel
  • Nice to meet you Daniel.
  • gt Do you know Clinton?
  • He was the second Impeached President of the
    United States.
  • gt Do you like him?
  • Sure he has never done anything to me.
  • gt 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.
  • gt Who are you?
  • I ask myself that question, "Who am I?"
  • gt What do you do?
  • I am chatting with people on the net.

5
Dialogue continued
  • gt What is your favorite topic?

My favorite subject is artificial
intelligence. gt What is artificial intelligence?
Artificial intelligence is the branch of
engineering and science devoted to constructing
machines that think. I can ask someone about
it. gt Can you do anything else than chatting?
Doing anything else than chatting is not among my
strongest traits. ...
6
Dialogue continued (2)
  • gt How old am I?

Is this a trick question? You said you were how
many years old? My guess is that you are really a
kid. gt What would you like to know about me?
Tell me about your educational background. gt I am
a professor in computer science You are a
professor in computer science? How do you like
your work? gt ... gt What should I tell the
students about you? Be sure to think before
you speak. State your point clearly and slowly
and gauge the listener's response before going
any further.
7
Is Alice intelligent?
  • ABSOLUTELY NOT !
  • 130000 fairly trivial input-response rules.
  • some pattern matching
  • some knowledge
  • some randomness
  • NO reasoning component
  • BUT demonstrates human-like behaviour.
  • Won the turing award

8
Other examples of success (2)
  • Data-mining
  • Which characteristics in the 3-dimensional
    structure of new molecules indicate that they may
    cause cancer ??

9
Data mining
  • An application of Machine Learning techniques
  • It solves problems that humans can not solve,
    because the data involved is too large ..

10
Data mining
  • A similar application
  • In marketing products ...

11
Many other applications
12
Interest in AI is not new !
  • A scene from the 17-hundreds

13
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?

14
Does numeric computation require intelligence ?
  • For humans?

286 783 , 68
  • For computers?
  • Also in the year 1900 ?
  • When do we consider a program intelligent?

15
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 intelligence similar / comparable /
    better? than that of humans.
  • achieved for very many tasks already

16
The long term goal
  • The Turing Test

17
Reproduction versus Simulation
  • At the very least in the context of the short
    term aim of AI
  • we do not want to SIMULATE human
    intelligence BUT
  • REPRODUCE the effect of intelligence

Nice analogy with flying !
18
Artificial Intelligence versus Natural Flight
19
Is the case for most of the successful
applications !
  • Deep blue
  • Alice
  • Data mining
  • Computer vision
  • ...

20
To some extent, we DO simulate Artificial Neural
Nets
  • A VERY ROUGH imitation of a brain structure
  • Work very well for learning, classifying and
    pattern matching.
  • Very robust and noise-resistant.

21
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

22
Which applications are easy ?
23
Modeling Knowledge and managing it .
The LENAT experiment 15 years of work by 15
to 30 people, trying to model the common
knowledge in the word !!!! Knowledge should be
learned, not engineered. AI are we only
dreaming ????
24
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,...

25
Artificial Intelligence is ...
  • In Engineering and Computer Science
  • The development and the study of advanced
    computer applications, aimed at solving tasks
    that - for the moment - are still better
    preformed by humans.
  • Notice temporal dependency !
  • Ex. Prolog

26
About this course ...
27
Selection of topics
28
Technically the contents
  • - Search techniques in AI
  • - Machine Learning
  • - Constraint Processing
  • - Artificial Neural Networks
  • - Planning
  • - Automated Reasoning

29
Another dimension to view the contents
  • 1. Basic methods for knowledge representation
    and problem solving.
  • the course is mainly about AI problem solving
    !
  • 2. Elements of some application areas
  • learning, planning

30
Contents (3) Different AI problem solving
paradigms...
  • State space representation and production rules.
  • Constraint-based representations.
  • First-order predicate Logic.

31
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)

32
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 in
    problem solving.
  • Mostly algorithmic aspects.
  • Also techniques for representing and modeling.

33
Practical info (FAI)
  • Exercises about 12 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)

34
Background Texts
The basics, but no complexity IDA,
SMA Almost complete The essence Complete Complete
Intro Almost complete Intro Complete
No document No document Winston Ch. Basic
search Winston Ch. Optimal search Russel and
Norvig 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 ...
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
35
Examination
  • Assignment deliver a report deadline end
    November (danny.deschreye_at_cs.kuleuven.be)
  • Designing your own exercise (for 4 parts) and
    providing a model solution for it
  • criteria originality, does the exercise
    illustrate all aspects of the method, complexity
    of the exercise, correctness of the solution
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