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Knowledge Based Systems and Artificial Intelligence Introduction Aleksandra Pizurica Department Telecommunications and Information Processing (TELIN) Image Processing and Interpretation (IPI) Group Statistical Image Modeling Lab Lecturer Prof. Aleksandra Pizurica Department Telecommunications en Information processing (TELIN) • Research group Image Processing and Interpretation (IPI) – Statistical Image Modeling Lab Research: statistical image modeling, multiresolution representations of images, image and video restoration; understanding and modeling visual perception and visual intelligence Teaching: Computer graphics, Knowledge based systems and artificial intelligence Office: Sint-Pietersnieuwstraat 41, (Technicum), TELIN, 2.05 Email: [email protected]; Tel: 09 264 34 15 Web: http://telin.ugent.be/~sanja 2 Study material • Slides (available on Minerva) • Book: S. Russel and P. Norvig: Artificial Intelligence – A Modern Approach (denoted as [R&N] in the slides) • Some other articles/chapters from books C. Bishop, Pattern Recognition and Machine Learning, Ch. 8: Graphical Models 3 Exam • Written exam (2/3) Theory (closed book) Problem solving (open book) • Projects/computer exercises (1/3) 4 What is AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Four categories of AI definitions 5 Acting humanly: The Turing test • The Turing test (Alan Turing, 1950) was designed to provide a satisfactory operational definition of intelligence • Suggested major components of AI: Knowledge representation (store what it hears or knows) Automated reasoning (use the stored info to draw conclusions) Machine learning (adapt to new circumstances; detect and extrapolate patterns) Language processing (e.g., able to communicate in English or another language) • Extension - total Turing test includes video to test perceptual abilities 6 Thinking humanly: Cognitive Science • If the goal is to have a program that thinks like a human, we need to determine first how humans think Introspection (catch our own thoughts as they go by) Psychological experiments (observing a person in action) Brain imaging (observing the brain in action) • Cognitive science aims at constructing testable theories of human mind using experimental psychology and computer models Scientific theories of internal activities in the brain at different levels of abstraction Validation of these theories can be • Predicting and testing behavior of human subjects (top-down) • Direct identification from neurological data (bottom-up) –cognitive neuroscience • AI, cognitive science and cognitive neuroscience are separate fields that fertilize each other 7 Thinking rationally: Laws of Thought • Aristotle (384 – 322 BC): codifying the “right thinking” (irrefutable reasoning) His syllogisms provided patterns for argument structures that always yielded correct conclusions when given the right premises. • E.g., “Socrates is a man; all men are mortal; therefore, Socrates is mortal” Studying these lows of thought (supposedly governing the operation of mind) initiated the field of logic. • Direct line through mathematics and philosophy to modern AI Logicians in the 19th century: a precise notation for statements about all kinds of objects and relations among them By 1965 programs existed for solving “in principle” any problem in logistic notation • Logicist tradition in AI is still present. Problems: Not easy to take informal knowledge and state it in the formal terms of logistic notation, especially when the knowledge is not entirely certain Exhausting the computational resources (solvable “in principle” ≠ solvable in practice) 8 Acting rationally • Rational behavior: doing the right thing • The right thing: that which is expected to maximize goal achievement, given the available information • Doesn't necessarily involve thinking (e.g., blinking reflex) but thinking should be in the service of rational action Making correct inferences (reason logically) is sometimes part of acting rationally But, correct inference is not all of rationality • The approach of acting rationally has two important advantages over the other listed categories of AI definitions: More general (e.g. all the skills needed for Turing test are needed to act rationally; also more general than “lows of thought”, which are only one of possible mechanisms for acting rationally) Much better suited for formal mathematical description than approaches based on human behavior and human thought 9 Rational agents • Agent: an entity that perceives and acts (from Latin agere, to do) • Rational agent is one that acts so as to achieve the best outcome, or when there is uncertainty, the best expected outcome • This course is about designing rational agents • Abstractly, an agent is a function from percept histories to actions: • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance • In practice, computational limitations make perfect rationality unachievable design best program for given machine resources 10 The foundations of AI 11 History of AI 12 State of the art in AI • Robotic vehicles Driverless robotic cars e.g. in 2005 DARPA Grand Challenge (driving on a rough terrain) and in 2007 Urban Challenge (obeying all traffic regulations while negotiating with other traffic and obstacles) • Speech recognition • Autonomous planning and scheduling E.g. NASA’s remote agent program (1999) “It's one small step in the history of space flight. But it was one giant leap for computer-kind, with a state of the art artificial intelligence system being given primary command of a spacecraft” http://ti.arc.nasa.gov/tech/asr/planning-and-scheduling/remote-agent/ • Game playing DEEP BLUE defeated world champion Garry Kasparov (1997) 13 State of the art in AI • Spam fighting Algorithms learn to classify messages as spam • Logistics planning • Computer aided diagnosis E.g. in radiology the computer output is already routinely used as a "second opinion" in assisting radiologists' image interpretations • Robotics E.g. robotic vacuum cleaners, robotic lawn mowers,… but also in more delicate domains, like robotic surgery • Machine translation • … 14 Contents of this course 1. Introduction to artificial intelligence (R&N, ch. 1,2) Foundations and history of AI Intelligent agents 2. Problem solving Solving problems by searching (R&N, extracts from ch. 1-5) Constraint satisfaction problems (R&N, ch. 6) 3. Knowledge, reasoning and planning Planning (R&N, ch. 10-11) Knowledge representation (R&N, ch. 12) 15 Contents of this course, contd. 4. Uncertain knowledge and reasoning Uncertainty and probabilistic reasoning (R&N, ch. 13 en 14) Reasoning over time and hidden Markov models (R&N, ch. 15) Graphical models and inference (Bishop*, ch. 8) Simple and complex decisions (R&N, ch. 16 en 17) 5. Learning (R&N, ch. 18) Learnig from examples Artificial neural networks 6. Perception Image perception and visual intelligence *[Bishop]: C. Bishop, Pattern Recognition and Machine Learning 16 Intelligent Agents (R&N, ch. 2) 17 Problem Solving (R&N, ch. 3) 18 Uncertainty and probabilistic reasoning (R&N, ch. 14, 15) • Why uncertainty? Too complex, non-deterministic, partially observable environment • Probabilistic reasoning Knowledge representation taking into account uncertainty • Basic principles of Bayesian networks A arrows: causal nodes: random influence variables B C D Bus too late Train too late Too late at work Joost too late Late for the meeting 19 Statistical learning and Hidden Markov models (R&N, ch. 15, 20) • Bayesian learning methods Maximum Likelihood (ML); Maximum A Posteriori (MAP); Naive Bayes • Dynamic Bayesian Networks Kalman filters, Hidden Markov Models (HMM) • Markov Random Fields • Inference Expectation-Maximization (EM), particle filters, MCMC samplers 20 Graphical models and inference (C. Bishop: Pattern Recognition and Machine Learning, hoofdstuk 8) Belief propagation Factor graph Bericht Bericht http://ai.cs.washington.edu/people/nath 21 Simple and complex decisions (R&N, ch. 16, 17) • Decision networks, influence diagrams Combining utility theory and probability theory Acting based on what we want and what we know • Sequential decision problems • Multiple agents: Game theory 22 Learning from examples (R&N, ch. 18) Decision tree Input: a vector of attributes Output: a single decision Example: waiting for a free table in a raetaurant [R&N, Fig. 18.2] 23 Learning from examples (R&N, ch. 18) Artificial neural networks R&N, Fig. 18.20 (b) R&N, Fig. 1.2 24 Modeling visual perception • Mathematical models of visual perception • Important for Understanding the human visual system Building real “intelligent” visual procesors T. Poggio et al http://www.csail.mit.edu/ 25