CS3243 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE AY2003/2004 Semester 2 Introduction:Chapter 1
CS3243 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE AY2003/2004 Semester 2 Introduction: Chapter 1
CS3243 Course home page:http://www.comp.nus.edu.sg/~cs3243 IVLE for schedule,lecture notes,tutorials,assignment,grading, office hours,etc. Textbook:S.Russell and P.Norvig Artificial Intelligence:A Modern Approach Prentice Hall,2003,Second Edition Lecturer:Min-Yen Kan (S15 05-05) Grading:Class participation(10%),Programming assignment(15%). ● Midterm test(20%),Final exam (55%) Class participation includes participation in both lectures and tutorials (attendance,asking and answering questions,presenting solutions to tutorial questions). Note that attendance at every lecture and tutorial will be taken and constitutes part of the class participation grade. ● Midterm test (in class,1 hr)and final exam(2 hrs)are both open-
CS3243 • Course home page: http://www.comp.nus.edu.sg/~cs3243 • IVLE for schedule, lecture notes, tutorials, assignment, grading, office hours, etc. • • Textbook: S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition • Lecturer: Min-Yen Kan (S15 05-05) • • Grading: Class participation (10%), Programming assignment (15%), • Midterm test (20%), Final exam (55%) • • Class participation includes participation in both lectures and tutorials (attendance, asking and answering questions, presenting solutions to tutorial questions). • Note that attendance at every lecture and tutorial will be taken and constitutes part of the class participation grade. • • Midterm test (in class, 1 hr) and final exam (2 hrs) are both openbook
Outline ·Course overview ·Vhat is Al? ·A brief history ·The state of the art
Outline • Course overview • What is AI? • A brief history • The state of the art
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) Natural Language Processing (chapter 22,23)
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) • Natural Language Processing (chapter 22,23)
What is Al? Views of Al fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally The textbook advocates "acting rationally
What is AI? Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally The textbook advocates "acting rationally
Acting humanly:Turing Test Turing (1950)"Computing machinery and intelligence": "Can machines think?">"Can machines behave intelligently?" Operational test for intelligent behavior:the Imitation Game HUMAN INTERROGATOR AI SYSTEM Predicted that by 2000,a machine might have a 30%chance of fooling a lay person for 5 minutes Anticipated all major arguments against Al in following 50 years Suggested major components of Al:knowledge,reasoning, language understanding,learning
Acting humanly: Turing Test • Turing (1950) "Computing machinery and intelligence": • "Can machines think?" → "Can machines behave intelligently?" • Operational test for intelligent behavior: the Imitation Game • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes • Anticipated all major arguments against AI in following 50 years • Suggested major components of AI: knowledge, reasoning, language understanding, learning •
Thinking humanly:cognitive modeling 1960s "cognitive revolution":information- processing psychology ● Requires scientific theories of internal activities of the brain .--How to validate?Requires 1)Predicting and testing behavior of human subjects (top-down) or 2)Direct identification from neurological data (bottom-up) ● Both approaches (roughly,Cognitive Science and Cognitive Neuroscience)
Thinking humanly: cognitive modeling • 1960s "cognitive revolution": informationprocessing psychology • • Requires scientific theories of internal activities of the brain • • -- How to validate? Requires 1) Predicting and testing behavior of human subjects (top-down) or 2) Direct identification from neurological data (bottom-up) • Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) • are now distinct from AI
Thinking rationally:"laws of thought" Aristotle:what are correct arguments/thought processes? Several Greek schools developed various forms of logic:notation and rules of derivation for thoughts;may or may not have proceeded to the idea of mechanization 】 Direct line through mathematics and philosophy to modern Al Problems: 1.Not all intelligent behavior is mediated by logical deliberation 2.What is the purpose of thinking?What thoughts should I have?
Thinking rationally: "laws of thought" • Aristotle: what are correct arguments/thought processes? • • Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization • • Direct line through mathematics and philosophy to modern AI • • Problems: 1. Not all intelligent behavior is mediated by logical deliberation 2. What is the purpose of thinking? What thoughts should I have? 3
Acting rationally:rational agent 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 ●
Acting rationally: rational agent • 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 •
Rational agents An agent is an entity that perceives and acts ● This course is about designing rational agents ● Abstractly,an agent is a function from percept histories to actions: [fP*→A列 For any given class of environments and tasks, we seek the agent (or class of agents)with the best performance
Rational agents • An agent is an entity that perceives and acts • • This course is about designing rational agents • • Abstractly, an agent is a function from percept histories to actions: • [f: P* → A] • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance • • Caveat: computational limitations make perfect
