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Introduction to Artificial Intelligence COMP 307 Xiaoying Sharon Gao Mengjie Zhang Computer Science Victoria University of Wellington The 307 Group COMP 307 1:2 • Lecturers • Xiaoying Sharon Gao, CO229, 463 5978, [email protected] • Mengjie Zhang, CO355, 463 5654, [email protected] • Tutors • Urvesh Bhowan, [email protected] • Bing Xue, [email protected] • Su Nguyen, [email protected] Menu • Course Organisation. • What is Artificial Intelligence? • Tasks for Artificial Intelligence • Approaches to Artificial Intelligence • History of Artificial Intelligence • Reading: text book Ch 1, 2, 26, 27 COMP 307 1:3 The Course COMP 307 1:4 Objectives: • Understand the basic problems, principles, approaches, and algorithms used in AI • Be able to use a variety of AI techniques to solve real problems • Have a basis for further learning and research in AI Lectures, tutorials, helpdesks COMP 307 1:5 • Lectures: • Week 1: Monday Friday 3:10-4:00, HT119 • Other weeks: Monday, Tuesday 3:10-4:00, HT119 • Tutorials • Some weeks after week 1 • Friday 3:10-4:00, HT119 • Helpdesks: • Some weeks after week 1 • Lab/date/time to be announced • tutorials and helpdesks will be announced in lectures, on web page or by email. • First week: no tutorials, no helpdesks, no Tuesday lecture. Course Outline: Topics COMP 307 1:6 • Prolog (1.5 weeks) • Rule based systems (1.5 weeks) • Machine learning (3 weeks) • Evolutionary computation (2 weeks) • Natural language understanding (1.5 weeks) • Other (2 weeks) • Search Techniques • Clustering or classification Assessment COMP 307 1:7 4 assignments • • • • • Hand out in lectures Mostly due 2-3 weeks later each about 5-10%, total of 25% mixture of programming and discussion/analysis/paper exercises submit online, sometimes on paper (to be announced) Exam • 3hr • 75% Mandatory requirement: • at least a D grade on exam Course Materials COMP 307 1:8 • Text book: • Stuart J. Russell and Peter Norvig, Artificial Intelligence. A Modern Approach, Prentice-Hall, NJ, 2nd edition, 2002 or 3rd edition, 2009 • Prolog: online manual, online tutorials, some books in library. • Web page http://ecs.victoria.ac.nz/Courses/COMP307_2012T1/ Read course outline (if it is ready) What is AI? COMP 307 1:9 • Programming computers to solve tasks that would require intelligence for people to solve. • An approach to understanding the intelligence (human or in general) by building systems that exhibit intelligence. • The study of how to make computers do things which, at the moment, people do better. • Computing problems we don't know how to solve yet — the hard part of Computer Science • A computer passing the Turing Test. Example Tasks for AI • Speech recognition for ESL tutoring • Natural language queries for search engine • Personalised news/email filters • Personalised Web search • Opinion mining • Medical diagnosis • Specialised medical test interpretation • Credit Card fraud detection • Computer system configuration • Vehicle assembly COMP 307 1:10 Example Tasks for AI COMP 307 1:11 • Plan daily task schedule for the Mars Rover • Planning/Scheduling orders and deliveries from a warehouse • Machine vision for security monitoring • Robot landmine sweeping • Self-driving vehicles • Self-Customising help (the MS Office paperclip) • Characterising gene function from experimental data • Identifying customer categories from customer data. COMP 307 1:12 Views of AI Several views Systems that think like humans Systems that think Systems that act like humans Systems that act intelligently intelligently AI: Engineering or Science? COMP 307 1:13 Engineering: • Building intelligent systems to solve problems in the world ⇒ Understanding mechanisms, algorithms, representations for building intelligent systems Science: • Understanding nature of intelligence (human or otherwise) ⇒ Implementing models of intelligence to evaluate and understand ⇒ Exploring consequences of different algorithms and representations Approaches to AI COMP 307 1:14 Symbolic AI • Representation and Reasoning at an abstract level ⇒ Representations and algorithms that manipulate symbols The physical symbol system hypothesis: A machine manipulating physical symbols has the necessary and sufficient means for general intelligence. (Newell and Simon) ⇒ "Old" AI Computational AI • The brain doesn't have symbols; use numbers ⇒ Representation and reasoning using lower level mechanisms ⇒ Probability based models and computation ⇒ Neural Networks ⇒ Genetic and Evolutionary Algorithms Strong AI and Weak AI Can machine think? Can machines be conscious? COMP 307 1:15 History of AI COMP 307 1:16