Transcript Constraint propagation
(Fundamentals of)
Artificial Intelligence
(and Knowledge-based Systems)
INTRODUCTION to...
… Artificial Intelligence ... this course … state-space representation … basic search
The mind beaten by the machine?
Is chess playing a proof of intelligent behaviour?
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Other examples of success:
Chatbot Alice: 3
A dialogue with Alice-bot:
> I’m Daniel 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.
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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. I can 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 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?
> ...
> 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.
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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’ 7
Other examples of success (2):
Data-mining: Which characteristics in the 3-dimensional structure of new molecules indicate that they may cause 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 cancer risk molecules is one example.
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Data mining:
A similar application: In marketing products ...
Predicting customer behavior in supermarkets is another.
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Many other applications:
Computer vision: In language and speech processing: In robotics: 11
Interest in AI is not new !
A scene from the 17-hundreds: 12
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? 13
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’? 14
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 15
The long term goal:
The Turing Test
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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 !
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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 pattern matching.
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 21
Which applications are easy ?
For very specialized, specific tasks: AI Example: ECG-diagnosis For tasks requiring common sense: AI 22
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 ????
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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 tasks that - for the moment - are still better preformed by humans.
Notice: temporal dependency !
– Ex. : Prolog 25
About this course ...
Selection of topics: Contents Ch.: Introduction to AI … … … … Ch.:Search techniques … … … … Handbook of AI Ch.:Planning … … … … Ch.:Natural Language … … … … Ch.:Game playing … … … … Ch.: Logic, resolution, inference … … … … Ch.:Knowledge representation … … … … Ch.:Machine Learning … … … … Ch.:Artificial Neural Networks … … … … Ch.:Phylosophy of AI … … … …
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Technically: the contents:
- Search techniques in AI - Machine Learning - Constraint Processing - Artificial Neural Networks - Planning - Automated Reasoning 28
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 29
Contents (3): Different AI problem solving paradigms...
State space representation and production rules.
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) 31
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.
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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) 33
Background Texts
Introduction: Advanced search: No document State-space Intro: Optimal search: No document Basic search,Heuristic search: Winston: Ch. Basic search Winston: Ch. Optimal search Russel and Norvig: Ch. 4 Games: Winston: Ch. Adversary search Version Spaces: Winston: Ch. Learning by managing..
Constraints I & II: Word Document on web page Image understanding: Winston: Ch. Symbolic constraint … Automated reasoning: Short text logic (to follow) Planning STRIPS: Winston: Ch. Planning Planning deductive: Winston: Ch. Planning Natural language: Winston: Ch. Frames and Common ...
The basics, but no complexity IDA*, SMA* Almost complete The essence Complete Complete Intro Almost complete Intro Complete
Examination
Assignment – deliver a report – deadline end November ([email protected]): 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 35