This document provides an overview of an introductory course on artificial intelligence and knowledge-based systems. It discusses the topics that will be covered in the course, including state-space representation, basic search techniques, games, version spaces, constraints, image understanding, automated reasoning, planning, natural language, and machine learning. It also provides details on the course structure, examination format, required background, and recommended reading materials. The goal is to introduce students to the basic achievements of AI and provide background in problem solving techniques through case studies and hands-on exercises.

1. (Fundamentals of)
Artificial Intelligence
(and Knowledge-based
Systems)
… Artificial Intelligence
... this course
… state-space representation
… basic search
INTRODUCTION to...

2. 2
The mind beaten by the machine?
 Is chess playing a proof of intelligent behaviour?

3. 3
Other examples of success:
Chatbot Alice:

4. 4
> I’m Daniel
A dialogue with Alice-bot:
Nice to meet you Daniel.
> Do you know Clinton?
He was the second Impeached President of the United
States.
> 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.

5. 5
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. I
can ask someone about it.
> Can you do anything else than chatting?
Doing anything else than chatting is not among my
strongest traits.
...

6. 6
Dialogue continued (2):
> 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.
> What would you like to know about me?
Tell me about your educational background.
> I am a professor in computer science
You are a professor in computer science? How do you like
your work?
> It’s kind of nice teaching courses on artificial intelligence
I 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 clearly
and slowly and gauge the listener's response before going
any further.

7. 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. 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. 9
Data mining:
 An application of Machine Learning techniques
 It solves problems that humans can not solve,
because the data involved is too large ..
Detecting cancer
risk molecules is
one example.

10. 10
Data mining:
 A similar application:
 In marketing products ...
Predicting customer
behavior in
supermarkets is
another.

11. 11
Many other applications:
 In language and speech processing:
 In robotics:
 Computer
vision:

12. 12
Interest in AI is not new !
 A scene from the 17-hundreds:

13. 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. 14
Does numeric computation
require intelligence ?
 For humans? Xcalc
3921 , 56
x 73 , 13
286 783 , 68
 For computers?
Also in the year 1900 ?
 When do we consider a program ‘intelligent’?

15. 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. 16
The long term goal:
The Turing Test

17. 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. 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. 19
Artificial Intelligence
versus
Natural Flight

20. 20
Is the case for most of the
successful applications !
 Deep blue
 Alice
 Data mining
 Computer vision
 ...

21. 21
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.

22. 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. 23
Which applications are easy ?
 For very specialized, specific tasks: AI
Example:
ECG-diagnosis
 For tasks requiring common sense: AI

24. 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. 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. 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. 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. 29
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 MAI
CS and SLT

30. 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. 31
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 area’s:
 learning, planning, image understanding,
language understanding

32. 32
Contents (3):
Different knowledge
representation formalisms ...
State space representation and production
rules.
 Constraint-based representations.
 First-order predicate Logic.

33. 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. 34
Contents (4):
Some application area’s:
 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. 35
Aims:
 Many different angles could be taken:
Empirical-Experimental AI
Algorithms in AI
Formal methods in AI
Cognitive aspects of AI Applications
Neural Nets
Probabilistics and Information Theory

36. 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. 37
A missing theme:
AGENTS !

38. 38
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 for
integrating different components of an AI system
ex: a robot that combines vision, natural language
and planning

39. 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. 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. 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. 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

43. 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. 44
 Alternative examinations possible:
For 3rd year BSc
and Initial MScStudents
 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