1. Text Book Outline

2. Outline Introduction –Chapter 1: Introduction –Chapter 2: Intelligent agents Problem solving –Chapter 3: Solving problems by searching –Chapter 4: Beyond classical search –Chapter 5: Adversarial search –Chapter 6: Constraint satisfaction problems

3. Example of Search Tic-Tac-Toe ‧‧‧ Goals ‧‧‧

4. Outline (Cont.) Knowledge and reasoning –Chapter 7: Logical agents –Chapter 8: First-order logic –Chapter 9: Inference in First-order logic –Chapter 10: Classical planning –Chapter 11: Planning and acting in the real world –Chapter 12: Knowledge representation

5. Example of Logic Logic ( 邏輯 ) – 張三是個律師 lawyer( 張三 ) – 所有律師都很有錢 ( 對所有人來說，如果他是律師，那麼他很有錢 ) – 如果張三是律師，那麼張三很有錢 lawyer( 張三 )→rich( 張三 )

6. Example of Inference Inference using Modus Ponens ( 推論 ) a→blawyer( 張三 )→rich( 張三 ) alawyer( 張三 ) brich( 張三 ) – 如果張三是律師，那麼張三很有錢 – 已知張三是個律師 – 結論：張三很有錢

7. Example of Planning NTU NTOU Taipei Main Keelung 29 4 train 0.5 NTOU Eng.Gungguan MRT 5 bus 1 39.5 taxi

8. Outline (Cont.) Uncertain knowledge and reasoning –Chapter 13: Quantifying uncertainty –Chapter 14, 15: Probabilistic reasoning –Chapter 16, 17: Making decisions Learning –Chapter 18~21 Other topics –Natural Language –Robots

9. Example of Learning A cleaning robot has experiences: How does it decide which room needs cleaning?

10. Example of Learning (Cont.) Decision Tree 決策樹 Neural Network 有無 學生 身份 職員 系所 教授 電機 資工 有無 身份 類神經網路類神經網路 樓層 系所 辦公室 輸入 有無垃圾筒 輸出

11. Introduction Chapter 1

12. What is AI? Views of AI fall into four categories: Systems that … Think like humansThink rationally Act like humansAct rationally The textbook advocates "acting rationally"

13. 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

14. 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) are now distinct from AI

15. Thinking rationally: "laws of thought" Aristotle: the first attempt to codify “right thinking” –Laws which always yield correct conclusions given correct premises –Logic Problems: 1.Not all intelligent behavior is mediated by logical deliberation 2.What is the purpose of thinking? What thoughts should I have?

16. 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

17. 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 rationality unachievable  design best program for given machine resources

18. State of the art Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego) During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft Proverb solves crossword puzzles better than most humans

19. Can the following tasks currently be solved by computers? Playing a decent game of table tennis (Ping-Pong). Driving in Victorville, California. Driving in the center of Cairo, Egypt. Buying a week’s worth of groceries at the market. Buying a week’s worth of groceries on the Web. Playing a decent game of bridge at a competitive level. Discovering and proving new mathematical theorems. Writing an intentionally funny story. Giving competent legal advice in a specialized area of law. Translating spoken English into spoken Swedish in real time. Performing a complex surgical operation.