Xiaoying Sharon Gao Mengjie Zhang Computer Science Victoria University of Wellington Introduction to Artificial Intelligence COMP 307

COMP 307 1:2 The 307 Group Lecturers Xiaoying Sharon Gao, CO229, , Mengjie Zhang, CO355, , Tutors Urvesh Bhowan, Bing Xue, Su Nguyen,

COMP 307 1:3 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 1:4 The Course 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

COMP 307 1:5 Lectures, tutorials, helpdesks 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 . First week: no tutorials, no helpdesks, no Tuesday lecture.

COMP 307 1:6 Course Outline: Topics 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

COMP 307 1:7 Assessment 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

COMP 307 1:8 Course Materials Text book: Stuart J. Russell and Peter Norvig, Artificial Intelligence. A Modern Approach, Prentice-Hall, NJ, 2nd edition, 2002 or 3 rd edition, 2009 Prolog: online manual, online tutorials, some books in library. Web page Read course outline (if it is ready)

COMP 307 1:9 What is AI? 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.

COMP 307 1:10 Example Tasks for AI Speech recognition for ESL tutoring Natural language queries for search engine Personalised news/ filters Personalised Web search Opinion mining Medical diagnosis Specialised medical test interpretation Credit Card fraud detection Computer system configuration Vehicle assembly

COMP 307 1:11 Example Tasks for AI 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 Systems that think like humans intelligently Systems that actSystems that act like humans intelligently

COMP 307 1:13 AI: Engineering or Science? 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

COMP 307 1:14 Approaches to AI 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

COMP 307 1:15 Strong AI and Weak AI Can machine think? Can machines be conscious?

COMP 307 1:16 History of AI