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(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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(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?
Other examples of success: Chatbot Alice:
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.
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. ...
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.
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’
Other examples of success (2): Data-mining: • Which characteristics in the 3-dimensional structure of new molecules indicate that they may cause cancer ??
Detecting cancer risk molecules is one example. Data mining: • An application of Machine Learning techniques • It solves problems that humans can not solve, because the data involved is too large ..
Predicting customer behavior in supermarkets is another. Data mining: • A similar application: • In marketing products ...
Computer vision: • In language and speech processing: • In robotics: Many other applications:
Interest in AI is not new ! • A scene from the 17-hundreds:
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?
Xcalc 3921 , 56 x 73 , 13 Does numeric computation require intelligence ? • For humans? 286 783 , 68 • For computers? • Also in the year 1900 ? • When do we consider a program ‘intelligent’?
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
The long term goal: The Turing Test
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.
Reproduction versus Simulation • At the very least in the context of the short termaim of AI: • we do not want to SIMULATE human intelligence BUT: • REPRODUCE the effect of intelligence Nice analogy with flying !
Is the case for most of the successful applications ! • Deep blue • Alice • Data mining • Computer vision • ...
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.
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
For very specialized, specific tasks: AI Example: ECG-diagnosis • For tasks requiring common sense: AI Which applications are easy ?
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 ????
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,...
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
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.
Contents Handbook of AI Ch.: Introduction to AI … … … … Ch.:Planning … … … … Ch.:Search techniques … … … … Ch.:Natural Language … … … … Ch.:Game playing … … … … Ch.:Machine Learning … … … … Ch.:Artificial Neural Networks … … … … Ch.: Logic, resolution, inference … … … … Ch.:Knowledge representation … … … … Ch.:Phylosophy of AI … … … … Selection of topics: not for MAI CS and SLT
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)
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 area’s: • learning, planning, image understanding, language understanding
Contents (3):Different knowledge representation formalisms ... • State space representation and production rules. • Constraint-based representations. • First-order predicate Logic.
… 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)
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
Neural Nets Empirical-Experimental AI Algorithms in AI Formal methods in AI Cognitive aspects of AI Applications Probabilistics and Information Theory Aims: • Many different angles could be taken:
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.
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
BUT: no intelligence without interaction with the world!! • See: experiment in middle-ages. • See also philosophy arguments against AI • Plus: multi-agents is FUN !
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)
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)
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:
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
For 3rd year BScand 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 • Alternative examinations possible: