CSE 471598 Introduction to Artificial Intelligence http www

CSE 471/598 Introduction to Artificial Intelligence http: //www. public. asu. edu/~huanliu/AI 09 F/cse 471 -598. htm Fall 2009

Introduction You: a future AI Expert TA: Ali Abbasi (mabbasi 2 at asu. edu) Time and Place: Please see our course web page Me: Huan Liu, huan. liu at asu. edu (http: //www. public. asu. edu/~huanliu) n n My office hours (can be changed upon req) Slides are updated periodically CSE 471/598 H. Liu 2

Course Introduction What is AI (many definitions of AI) n n One definition: a field to enable computers with human-level intelligence with attempts to understand intelligent entities. We will evaluate many definitions later. What is this course about (or why should everyone learn AI? ) n n n understand ourselves better build automated intelligent agents to advance research improve problem solving skills CSE 471/598 H. Liu 3

Course workload and evaluation n We will work hard together - “No pain, no gain!” w Projects (30%, 2 -3) – all in Lisp w Exam(s) (2*25%) w Homework (~20%) w Quizzes and class participation (~10% extra) n n n Which grading system you prefer (w/wo +/-) Late penalty, YES and exponentially increased Academic integrity (http: //www. public. asu. edu/~huanliu/conduct. html) CSE 471/598 H. Liu 4

Course plan Text Book: AI - A Modern Approach n 2 nd Edition in green (3 rd Edition will be out Nov. 09) Reading assignment: chapters covered About 13 -15 chapters Our lofty goal: “to finish all the 27 chapters” To be realistic, Try to keep up and avoid catch up one major subject per week TIP CSE 471/598 H. Liu 5

Major Topics Intelligent agents Problem solving Knowledge and reasoning Acting logically Learning Uncertainty CSE 471/598 H. Liu TIP Comprehend the topics with your common sense 6

Welcome to this class! We will work together and hard throughout this semester and your active participation is crucial for the success of the class – the REAL shortcut to your success n Who don’t want shortcut? Apparent vs. true shortcuts Questions and suggestions are always welcome. n E. g. , if you find anything interesting to share, incorrect or unclear, send an email or talk to me, or discuss it in class You get feedback from us (TA and me), and I expect feedback from you, too Use my. ASU to send email and for discussions CSE 471/598 H. Liu 7

Introduction of AI - Gearing up for a fun semester about intelligent agents - What is an intelligent agent in your view?

What is AI About thinking and acting n We are not alone, but … (Homo (genus)) http: //en. wikipedia. org/wiki/Homo_(genus) Acting humanly: The Turing test (by Turing 1950) n n n Its original purpose What do we need to pass the test? http: //www. loebner. net/Prizef/loebner-prize. html Does that serve our purpose of developing AI? Thinking humanly: Cognitive modeling n n “Think-aloud” to learn from human and recreate in computer programs (GPS) What the Eyes see, a camera cannot http: //www. topcharoen. co. th/web/illusion-a 19. gif CSE 471/598 H. Liu 9

What is AI (2) Thinking rationally: Syllogisms, Logic n n n What would you act on the $50 i. Books incident? Unable to deal with uncertainty Some paradoxes: Liar, Barber w Gödel's incompleteness and Turing's undecidability Acting rationally: A rational agent (something that acts) to achieve best or best expected outcomes n Some rational actions do not involve inference w An example – a reflex doe not need inference A set of definitions (Figure 1. 1) CSE 471/598 H. Liu 10

Foundations of AI Philosophy (428 B. C. - Present) – reasoning and learning n n Can formal rules be used to draw valid conclusions? How does the mental mind arise from a physical brain? Where does knowledge come from? How does knowledge lead to action? CSE 471/598 H. Liu 11

Mathematics (c. 800 - Present) - logic, probability, decision making, computation n What are the formal rules to draw conclusions? What can be computed? How do we reason with uncertain information? Economics (1776 -present) n n n How should we make decisions so as to maximize payoff? How should we do this when others may not go along? How should we do this when the payoff may be far in the future? CSE 471/598 H. Liu 12

Neuroscience (1861 -present) n How do brains process information w Processing speed, memory size in a computer (Figure 1. 3) Psychology (1879 - Present) investigating human mind n How do humans and animals think and act? w Mind Wide Open (the use of f. MRI) Computer engineering (1940 - Present) ever improving tools n How can we build an efficient computer? w Moors Law, Raptures for the Geeks CSE 471/598 H. Liu 13

Control theory and Cybernetics (1948 present) n n How can artifacts operate under their own control? Feedback and adapt Linguistics (1957 - Present) - the structure and meaning of language n n How does language relate to thought? Computational linguistics CSE 471/598 H. Liu 14

Brief History of AI Gestation of AI (1943 -1955) n n n Mc. Culloch and Pitts’s model of artificial neurons Minsky’s 40 -neuron network Alan Turing’s Computing Machinary and Intelligence Birth of AI (1956) n n n A 2 -month Dartmouth workshop of 10 attendees – the name of AI Newell and Simon’s Logic Theorist Should another name like `computational rationality’ be used? Any suggestion? Early enthusiasm, great expectations (1952 1969) n GPS by Newell and Simon, Lisp by Mc. Carthy, Blockworld by Minsky CSE 471/598 H. Liu 15

AI facing reality (1966 - 1973) n Many predictions of AI’s coming successes w A computer would be a chess champion in 10 years (1957) n n n Machine translation – Syntax is not enough Intractability of the problems attempted by AI “What computers cannot do” in 76 Knowledge-based systems (1969 - 1979) n n Knowledge is power, acquiring knowledge from experts Expert systems (MYCIN) AI - an industry (1980 - present) n Many AI systems help companies to save money and increase productivity (Cyc) CSE 471/598 H. Liu 16

The return of neural networks (1986 – present) n n PDP books by Rumelhart and Mc. Clelland Connectionist models vs. symbolic models AI – a science (1987 – present) n n n Build on existing theories vs. propose brand new ones Rigorous empirical experiments Learn from data – machine learning, data mining AI – intelligent agents (1995 – present) n Working agents embedded in real environments with continuous sensory inputs CSE 471/598 H. Liu 17

Some examples of AI applications Smart bombs Deep Blue, and others E-Game industry E-Business Intelligent houses Intelligent appliances Robo. Cup Mars rovers Biometrics Communications (email, word processor, social media) Auto driving from E to W (98% vs. 2%) Consumer protection Social Networking Sites CSE 471/598 H. Liu 18

Concluding remarks “The real value of the discipline, Mr. Lazowska said, is less in acquiring a skill with technology tools - the usual definition of computer literacy than in teaching students to manage complexity; to navigate and assess information; to master modeling and abstraction; and to think analytically in terms of algorithms, or step-bystep procedures. ” from http: //www. nytimes. com/2005/08/23/technology/23 geeks. html What is AI about? CSE 471/598 H. Liu 19

Refresher for LISP What is it? n ANSI Common Lisp, Paul Graham, Prentice Hall Input (e. g. , terminal, files) Output (e. g. , files, printing) Processing (various operations) How to run it? CSE 471/598 H. Liu 20