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Artificial Intelligence: INTRODUCTION

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1 Artificial Intelligence: INTRODUCTION
27/06/1438 Artificial Intelligence: INTRODUCTION

2 Artificial Intelligence
27/06/1438 Short presentation Dr. Abdullah Alsheddy د. عبدالله عبدالعزيز الشدي Office: FR64 Textbook: S. Russell and P. Norvig Artificial Intelligence: A Modern Approach, Prentice Hall, 3rd Edition, 2009 Grading: Quizzes/Presentation/Participation (20%) Project (20%) Midterm test (20%) Final Exam: 40% Artificial Intelligence : Introduction 27/06/1438

3 Artificial Intelligence
27/06/1438 Course overview Introduction and Agents (chapters 1,2) Search (chapters 3,4,5,6) Logic (chapters 7,8,9) Planning (chapters 11,12) Uncertainty (chapters 13,14) Learning (chapters 18,20) Robotics (chapter 25,26) Introduzione all’I.A.: definizione di intelligenza, adattività. Tecniche di ricerca: ricerca su grafi, minimax, A*. Rappresentazione della conoscenza: introduzione alla logica matematica, sistemi a frame, reti di conoscenza, completezza e decidibilità, RDF e OWL. Dimostrazione automatica di teoremi: programmazione logica, risoluzione SLD, inferenze non monotoniche e logiche modali, introduzione al linguaggio Prolog. Situation calculus. Introduzione ai sistemi esperti: forward e backward reasoning, l’algoritmo Rete, il sistema JESS. Problematiche di pianificazione. Introduzione ai sistemi subsimbolici: algoritmi genetici, reti neuronali artificiali, a-life. Sistemi di agenti. Usi industriali dell’I.A. Artificial Intelligence : Introduction 27/06/1438

4 Artificial Intelligence
27/06/1438 Chapter1 : Outline What is AI A brief history The state of the art Artificial Intelligence : Introduction 27/06/1438

5 Artificial Intelligence
27/06/1438 What is AI? Intelligence: “the capacity to learn and solve problems” (Websters dictionary) in particular, the ability to solve novel problems the ability to act rationally the ability to act like humans Artificial Intelligence build and understand intelligent entities or agents 2 main approaches: “engineering” versus “cognitive modeling” Artificial Intelligence : Introduction 27/06/1438

6 Intelligent behavior Humans Computer
Artificial Intelligence : Introduction 27/06/1438

7 Why AI? Cognitive Science: As a way to understand how natural minds and mental phenomena work e.g., visual perception, memory, learning, language, etc. Philosophy: As a way to explore some basic and interesting (and important) philosophical questions e.g., the mind body problem, what is consciousness, etc. Engineering: To get machines to do a wider variety of useful things e.g., understand spoken natural language, recognize individual people in visual scenes, find the best travel plan for your vacation, etc. Artificial Intelligence : Introduction 27/06/1438

8 Artificial Intelligence
27/06/1438 Weak vs. Strong AI Weak AI: Machines can be made to behave as if they were intelligent Strong AI: Machines can have consciousness Subject of fierce debate among philosophers and AI researchers. E.g. Red Herring article and responses Artificial Intelligence : Introduction 27/06/1438

9 AI Characterizations Artificial Intelligence : Introduction 27/06/1438

10 AI Characterizations Discipline that systematizes and automates intellectual tasks to create machines that : Act like humans System passing the Turing Test (1950) Learning from Knowledge (adapt) Representing Knowledge (memorize) Solve Pb (argue) Understanding (communicate) Theoretical Act rationally Rational agent (199X) acts according to his beliefs to reach goals (not only logical) Pragmatic Think like humans Cognitive modeling (GPS (Newel & Simon,61)) Complex Think rationally logical thinking Pascal [ ] (calculating machine) Leibniz [ ] (reasoning machine) Babbage [ ] (Analytical Engine) limited Artificial Intelligence : Introduction 27/06/1438

11 Systems that act like humans
Artificial Intelligence 27/06/1438 Systems that act like humans When does a system behave intelligently? Turing (1950) Computing Machinery and Intelligence "Can machines think?"  "Can machines behave intelligently?" Operational test of intelligence: imitation games Test requires the collaboration of major components of AI: knowledge, reasoning, language understanding, learning, … Interrogator interacts with a computer and a person. Computer passes the Turing test if interrogator cannot determine which is which. Artificial Intelligence : Introduction 27/06/1438

12 Systems that act like humans
AI is the art of creating machines that perform functions that require intelligence when performed by humans Methodology: Take an intellectual task at which people are better and make a computer do it Turing test Prove a theorem Play chess Plan a surgical operation Diagnose a disease Navigate in a building Artificial Intelligence : Introduction 27/06/1438

13 Systems that think like humans
Artificial Intelligence 27/06/1438 Systems that think like humans How do humans think? Requires scientific theories of internal brain activities (cognitive model): How to validate? requires : Predicting and testing human behavior Identification from neurological data Brain imaging in action Cognitive Science vs. Cognitive neuroscience vs. Neuroimaging They are now distinct from AI Share that the available theories do not explain anything resembling human intelligence. Three fields share a principal direction. Artificial Intelligence : Introduction 27/06/1438

14 Systems that think rationally
Artificial Intelligence 27/06/1438 Systems that think rationally Capturing the laws of thought Aristotle: What are ‘correct’ argument and thought processes? Correctness depends on irrefutability of reasoning processes. This study initiated the field of logic. The logicist tradition in AI hopes to create intelligent systems using logic programming. Problems: Not all intelligence is expressed by logic behavior What is the purpose of thinking? What thought should one have? Artificial Intelligence : Introduction 27/06/1438

15 Systems that act rationally
Artificial Intelligence 27/06/1438 Systems that act rationally Rational behavior: “doing the right thing” The “Right thing” is that what is expected to maximize goal achievement given the available information. Can include thinking, yet in service of rational action. Action without thinking: e.g. reflexes. Artificial Intelligence : Introduction 27/06/1438

16 Systems that act rationally
Artificial Intelligence 27/06/1438 Systems that act rationally Two advantages over previous approaches: More general than law of thoughts approach More amenable to scientific development. Yet rationality is only applicable in ideal environments. Moreover rationality is not a very good model of reality. Artificial Intelligence : Introduction 27/06/1438

17 Artificial Intelligence
27/06/1438 Think/Act Rationally Always make the best decision given what is available (knowledge, time, resources) Perfect knowledge, unlimited resources  logical reasoning Imperfect knowledge, limited resources  (limited) rationality Connection to economics, operational research, and control theory But ignores role of consciousness, emotions, fear of dying on intelligence Artificial Intelligence : Introduction 27/06/1438

18 Artificial Intelligence
27/06/1438 Rational agents An agent is an entity that perceives and acts This course is about designing rational agents An agent is a function from percept histories to actions: For any given class of environments and task we seek the agent (or class of agents) with the best performance. Problem: computational limitations make perfect rationality unachievable. Artificial Intelligence : Introduction 27/06/1438

19 Artificial Intelligence
27/06/1438 Foundations of AI Different fields have contributed to AI in the form of ideas, view points and techniques. Philosophy: Logic, reasoning, mind as a physical system, foundations of learning, language and rationality. Mathematics: Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability. Psychology: adaptation, phenomena of perception and motor control. Economics: formal theory of rational decisions, game theory. Linguistics: knowledge representation, grammar. Neuroscience: physical substrate for mental activities. Control theory: homeostatic systems, stability, optimal agent design. Artificial Intelligence : Introduction 27/06/1438

20 Artificial Intelligence
27/06/1438 A brief history What happened after WWII? 1943: Warren Mc Culloch and Walter Pitts: a model of artificial boolean neurons to perform computations. First steps toward connectionist computation and learning (Hebbian learning). Marvin Minsky and Dann Edmonds (1951) constructed the first neural network computer 1950: Alan Turing’s “Computing Machinery and Intelligence” First complete vision of AI. Idea of Genetic Algorithms Artificial Intelligence : Introduction 27/06/1438

21 Artificial Intelligence
27/06/1438 A brief history (2) The birth of (the term) AI (1956) Darmouth Workshop bringing together top minds on automata theory, neural nets and the study of intelligence. Allen Newell and Herbert Simon: The logic theorist (first non-numerical thinking program used for theorem proving). For the next 20 years the field was dominated by these participants. Great expectations ( ) Newell and Simon introduced the General Problem Solver. Imitation of human problem-solving Arthur Samuel (1952-) investigated game playing (checkers ) with great success. John McCarthy(1958-) : Inventor of Lisp (second-oldest high-level language) Logic oriented, Advice Taker (separation between knowledge and reasoning) Artificial Intelligence : Introduction 27/06/1438

22 Artificial Intelligence
27/06/1438 A brief history (3) The birth of AI (1956) Great expectations continued .. Marvin Minsky (1958 -) Introduction of microworlds that appear to require intelligence to solve: e.g. blocks-world. Anti-logic orientation, society of the mind. Herbert Gelernter (1959) : constructed the geometry theorem Prover. Artur Samual ( ) : a series of programs for checkers. Collapse in AI research ( ) Progress was slower than expected. Unrealistic predictions. Some systems lacked scalability. Combinatorial explosion in search. Fundamental limitations on techniques and representations. Artificial Intelligence : Introduction 27/06/1438

23 Artificial Intelligence
27/06/1438 A brief history (4) AI revival through knowledge-based systems ( ) General-purpose vs. domain specific E.g. the DENDRAL chemistry project (Buchanan et al. 1969) First successful knowledge intensive system. Expert systems MYCIN to diagnose blood infections (Feigenbaum et al.) Introduction of uncertainty in reasoning. Increase in knowledge representation research. Logic, frames, semantic nets, … Artificial Intelligence : Introduction 27/06/1438

24 Artificial Intelligence
27/06/1438 A brief history (5) AI becomes an industry ( present) First successful commercial expert system R1 at DEC (McDermott, 1982) Fifth generation project in Japan (1981) : a 10-year plan to build intelligent computer running Prolog. American response …: US formed the microelectronics and Computer Technology Corporation designed to assure national competitiveness (chip design and human-interface research) Puts an end to the AI winter. AI industry boomed from a few million to billion dollars in Period called “AI winter” in which many companies suffered as they failed to deliver on extravagant promises. Connectionist revival ( present) Parallel distributed processing (RumelHart and McClelland, 1986); back-propagation learning (computer science and psychology). Artificial Intelligence : Introduction 27/06/1438

25 Artificial Intelligence
27/06/1438 A brief history (6) AI becomes a science ( present) In speech recognition: hidden markov models In neural networks In uncertain reasoning and expert systems: Bayesian network formalism Problem solving The emergence of intelligent agents ( present) The whole agent problem: “How does an agent act/behave embedded in real environments with continuous sensory inputs” “Ideally, an intelligent agent takes the best possible action in a situation : study the problem of building intelligent agents in this sense”. Artificial Intelligence : Introduction 27/06/1438

26 State of the art : AI today 1/2
Autonomous planning and scheduling : on-board autonomous planning program to control the scheduling of operations for a spacecraft (Jonhson et al., 2000). Game playing : IBM’s Deep Blue became the first computer program to defeat the world champion in chess match (Goodman and Keene, 1997), Autonomous control : the ALVINN computer vision system was trained to steer a car to keep it following a lane (for 2850 miles ALVINN was in control of steering in 98%, only 2% for human control mostly at exit ramps). Diagnosis : medical diagnosis programs based on probabilistic analysis have been able to perform at level of an expert physician in several areas in medicine (Heckerman 1991). Robot driving: DARPA grand challenge Artificial Intelligence : Introduction 27/06/1438

27 Stanley RobotStanford Racing Team
Artificial Intelligence : Introduction 27/06/1438

28 Major research areas (Applications)
Artificial Intelligence 27/06/1438 Major research areas (Applications) Natural Language Understanding Image, Speech and pattern recognition Uncertainty Modeling Problem solving Knowledge representation ….. Artificial Intelligence : Introduction 27/06/1438

29 AI Success Story : Medical expert systems
Artificial Intelligence 27/06/1438 AI Success Story : Medical expert systems Programs listed by Special Field Gynecology Imaging Analysis Internal Medicine Intensive Care Laboratory Systems Orthopedics Pediatrics Pulmonology & Ventilation Surgery & Post-Operative Care Trauma Management Antibiotics & Infectious Diseases Cancer Chest pain Dentistry Dermatology Drugs & Toxicology Emergency Epilepsy Family Practice Genetics Geriatrics Artificial Intelligence : Introduction 27/06/1438

30 Pattern Recognition Applications
Artificial Intelligence 27/06/1438 Pattern Recognition Applications Handwriting and document recognition Signature, biometrics (finger, face, iris, etc.) Trafic monitoring, Remote Sensing guided missile, target homing Artificial Intelligence : Introduction 27/06/1438

31 Artificial Intelligence
27/06/1438 Future of AI Making AI Easy to use Easy-to-use Expert system building tools Web auto translation system Recognition-based Interface Packages Integrated Paradigm Symbolic Processing + Neural Processing AI in everywhere, AI in nowhere AI embedded in all products Ubiquitous Computing, Pervasive Computing Artificial Intelligence : Introduction 27/06/1438

32 Quiz Does a plane fly? Does a boat swim? Does a computer think?
Artificial Intelligence : Introduction 27/06/1438

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