(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 Its kind of nice teaching courses
on artificial intelligence I guess it is a lot
of fun. 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 !

• 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

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 questionTwo 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
The meta-Turing test

• The meta-Turing test counts a thing as
  intelligent if it seeks to devise and apply
  Turing tests to objects of its own
  creation. -- Lew Mammel, Jr.

18
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 !
19
Artificial Intelligence versus Natural Flight
20
Is the case for most of the successful
applications !

• Deep blue

• Alice
• Data mining
• Computer vision
• ...

21
To some extent, we DO simulateArtificial Neural
Nets

• A VERY ROUGH imitation of a brain structure

• Work very well for learning, classifying and
  pattern matching.
• Very robust and noise-resistant.

22
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

23
Which applications are easy ?
24
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 ????
25
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,...

26
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

27
About this course ...
28
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
  different books.

29
Selection of topics
not for MAI CS and SLT
30
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)

31
Another dimension toview 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, image understanding,
  language understanding

32
Contents (3)Different knowledge representation
formalisms ...

• State space representation and production rules.

• Constraint-based representations.
• First-order predicate Logic.

33
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)

34
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

35
Aims

• Many different angles could be taken

36
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.
• The 6-study point version 2 projects for
  hands-on experience.

37
A missing themeAGENTS !
38
A missing themeAGENTS (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
  for integrating different components of an AI
  system
• ex a robot that combines vision, natural
  language and planning

39
BUT no intelligence without interaction with the
world!!

• See experiment in middle-ages.
• See also philosophy arguments against AI
• Plus multi-agents is FUN !

40
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)

41
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)

42
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 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
43
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

44
For 3rd year BScand Initial MScStudents

• Alternative examinations possible

• Designing your own exercise (for each part) and
  solving it (not for FAI)
• criteria originality, does the exercise
  illustrate all aspects of the method, complexity
  of the exercise, correctness of the solution