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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? 2 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. 4 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. ... 5 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. 6 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’ 7 Other examples of success (2): Data-mining: Which characteristics in the 3-dimensional structure of new molecules indicate that they may cause cancer ?? 8 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. 9 Data mining: A similar application: In marketing products ... Predicting customer behavior in supermarkets is another. 10 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 16 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. 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 ? For very specialized, specific tasks: AI Example: ECG-diagnosis For tasks requiring common sense: AI 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 ... 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. 28 Selection of topics: Contents Handbook of AI Ch.: Introduction to AI … … … … Ch.:Planning … … … … Ch.:Search techniques … … … … Ch.:Natural Language … … … … Ch.:Game playing … … … … Ch.:Machine Learning … … … … Ch.: Logic, resolution, inference … … not for MAI … … CS and SLT Ch.:Artificial Neural Networks … … … … Ch.:Knowledge representation … … … … Ch.:Phylosophy of AI … … … … 29 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) 30 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 31 Contents (3): Different knowledge representation formalisms ... State space representation and production rules. Constraint-based representations. First-order predicate Logic. 32 … 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) 33 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 34 Aims: Many different angles could be taken: Neural Nets Empirical-Experimental AI Algorithms in AI Cognitive aspects of AI Formal methods in AI Applications Probabilistics and Information Theory 35 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. 36 A missing theme: AGENTS ! 37 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 38 BUT: no intelligence without interaction with the world!! See: experiment in middle-ages. See also philosophy arguments against AI Plus: multi-agents is FUN ! 39 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) 40 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) 41 Background Texts Introduction: No document State-space Intro: No document Basic search,Heuristic search: Winston: Ch. Basic search The basics, but Optimal search: Winston: Ch. Optimal search no complexity Advanced search: Russel: Ch. 4 IDA*, SMA* Games: Winston: Ch. Adversary search Almost complete Version Spaces: Winston: Ch. Learning by managing.. The essence Constraints I & II: Word Document on web page Complete Image understanding: Winston: Ch. Symbolic constraint … Complete Automated reasoning: Short text logic (to follow) Intro Planning STRIPS: Winston: Ch. Planning Almost complete Planning deductive: Winston: Ch. Planning Intro Natural language: Winston: Ch. Frames and Common ... Complete 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 43 For 3rd year BSc and 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 44