Introduction to AI

# Introduction to AI

Télécharger la présentation

## Introduction to AI

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
##### Presentation Transcript

1. Introduction to AI Russell and Norvig: Chapter 1 CMSC421 – Fall 2006

2. Meta-Intro • Personnel: Myself, two TAs: Galileo Namata and Vivek Sehgal • Answers to some important questions: • What are the prerequisites for this course? • How can I do well in this course? • What are the course logistics? • How do we stay awake in the late afternoon… ? • What will we learn in this class?

3. What are the prerequisites? • Assume you know how to program. In addition, you should know: • basic algorithms, data structures and computational complexity • i.e., searching graphs (DFS, BFS) • lists, trees, graphs, etc. • Difference between an O(n) and an O(2n) algorithm • basic logic • Truth table for x OR y, x AND y, x IMPLIES y • basic probability • P(A v B) = P(A) + P(B) – P(A & B) • P(A | B) = P(A & B) / P(B)

4. How do I do well in this course? • Attend class • Participate in class • Do reading • Suggestion: 1) Set aside 20 minutes to skim chapter before lecture. 2) After lecture, go back and read the text in depth. • Start written assignments early • Assignments are not designed to be done the night before they are due • Start programming assignments EARLY • Do practice problems to study for exams • Form study groups. Working together (not copying) is highly encouraged** ** More on this in a few slides

5. What are the course logistics? • Web Page: • http://www.cs.umd.edu/class/fall2006/cmsc421/ • Mailing list: • http://mailman.cs.umd.edu/mailman/listinfo/cmsc421_2006 • Forum: • https://forum.cs.umd.edu/forumdisplay.php?f=43

6. How do we stay awake? • and learn something, ! • Course Ettiquette: • Arrive to class on time; if you must leave during class, please try to limit the disruption/distraction • No cell phones, no side discussions • No laptops during lectures • Participate, Participate, Participate • ask questions – if you don’t understand the material, probably there is someone else who does not either! • some in class exercises • Feedback: • please provide feedback • there will be several opportunities, but also feel free to just come talk to me!

7. Summary Meta-Intro • Answers to some important questions: • What are the prerequisites for this course? • How can I do well in this course? • What are the course logistics? • How do we stay awake in the late afternoon… ? • What will we learn in this class?

8. What is AI? • Class Exercise #0, part A: • On the 3x5 card you’ve been given, write down what your definition of AI • You may also want to copy your definition to your notes, because you’ll be turning the card in (anonymously, no worries!) • We will collect these definitions in 3 minutes!

9. Intelligent behavior Computer Humans Found on the Web … • AI is the simulation of intelligent human processes • AI is the reproduction of the methods or results of human reasoning or intuition • AI is the study of mental faculties through the use computational methods • Using computational models to simulate intelligent behavior • Machines to emulate humans

10. Why AI? • Cognitive Science: As a way to understand how natural minds and mental phenomena work • e.g., visual perception, memory, learning, language, etc. • Philosophy: As a way to explore some basic and interesting (and important) philosophical questions • e.g., the mind body problem, what is consciousness, etc. • Engineering: To get machines to do a wider variety of useful things • e.g., understand spoken natural language, recognize individual people in visual scenes, find the best travel plan for your vacation, etc.

11. AI Characterizations Discipline that systematizes and automates intellectual tasks to create machines that:

12. #1: Act Like Humans • Behaviorist approach • Not interested in how you get results, just the similarity to what human results • Exemplified by the Turing Test (Alan Turing, 1950).

13. Turing Test • Interrogator interacts with a computer and a person via a teletype. • Computer passes the Turing test if interrogator cannot determine which is which. • Loebner contest: Modern version of Turing Test, held annually, with a \$100,000 prize. http://www.loebner.net/Prizef/loebner-prize.html • Participants include a set of humans and a set of computers and a set of judges. • Scoring: Rank from least human to most human. • Highest median rank wins \$2000. • If better than a human, win \$100,000. (Nobody yet…)

14. #2: Think Like Humans • Exemplified by • General Problem Solver (Newell and Simon) • Neural networks • Reinforcement learning • How the computer performs functions does matter • Comparison of the traces of the reasoning steps • Cognitive science  testable theories of the workings of the human mind • But: • some early research conflated algorithm performance => like human (and vice-versa) • Do we want to duplicate human imperfections?

15. #3: Thinking rationally • Exemplified by "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 • Direct line through mathematics and philosophy to modern AI • Problems: • Not easy to translate informal real world problem into formal terms (problem formulation is difficult) • While may be able to solve the problem in principal (i.e. decidable), in practice, may not get the answer in a reasonable amount of time (computationally intractable)

16. #4: Acting Rationally • Rational behavior: do the right thing • Always make the best decision given what is available (knowledge, time, resources) • Perfect knowledge, unlimited resources  logical reasoning (#3) • Imperfect knowledge, limited resources  (limited) rationality • Connection to economics, operational research, and control theory • But ignores role of consciousness, emotions, fear of dying on intelligence

17. AI Characterizations Discipline that systematizes and automates intellectual tasks to create machines that:

18. What is AI? • Class Exercise #0, part B: • Hopefully you have received the AI definition from another student in the course. • Break into groups of 4 people • In your groups: • Start by giving a quick introduction: name, year, etc. • On the additional blank card each group has been given, write each person in the groups name and email • Each person has a card; read the card to the group, and the group should decide the category:

19. Bits of History • 1956: The name “Artificial Intelligence” was coined by John McCarthy. (Would “computational rationality” have been better?) • Early period (50’s to late 60’s):Basic principles and generality • General problem solving • Theorem proving • Games • Formal calculus

20. Bits of History • 1969-1971: Shakey the robot (Fikes, Hart, Nilsson) • Logic-based planning (STRIPS) • Motion planning (visibility graph) • Inductive learning (PLANEX) • Computer vision

21. Bits of History • Knowledge-is-Power period (late 60’s to mid 80’s): • Focus on narrow tasks require expertise • Encoding of expertise in rule form:If: the car has off-highway tires and 4-wheel drive and high ground clearanceThen: the car can traverse difficult terrain (0.8) • Knowledge engineering • 5th generation computer project • CYC system (Lenat)

22. Bits of History • AI becomes an industry (80’s – present): • Expert systems: Digital Equipment, Teknowledge, Intellicorp, Du Pont, oil industry, … • Lisp machines: LMI, Symbolics, … • Constraint programming: ILOG • Robotics: Machine Intelligence Corporation, Adept, GMF (Fanuc), ABB, … • Speech understanding • Information Retrieval – Google, …

23. Predictions and Reality … (1/3) • In the 60’s, a famous AI professor from MIT said: “At the end of the summer, we will have developed an electronic eye” • As of 2002, there is still no general computer vision system capable of understanding complex dynamic scenes • But computer systems routinely perform road traffic monitoring, facial recognition, some medical image analysis, part inspection, etc…

24. Predictions and Reality … (2/3) • In 1958, Herbert Simon (CMU) predicted that within 10 years a computer would be Chess champion • This prediction became true in 1998 • Today, computers have won over world champions in several games, including Checkers, Othello, and Chess, but still do not do well in Go

25. Predictions and Reality … (3/3) • In the 70’s, many believed that computer-controlled robots would soon be everywhere from manufacturing plants to home • Today, some industries (automobile, electronics) are highly robotized, but home robots are still a thing of the future • But robots have rolled on Mars, others are performing brain and heart surgery, and humanoid robots are operational and available for rent (see: http://world.honda.com/news/2001/c011112.html)

26. State of the Art • Drive safely along a curving mountain road • Drive safely along US 1 • Buy a week’s worth of groceries on the web • Buy a week’s worth of groceries at your local Giant • Play a decent game of bridge • Write an intentionally funny story • Give competent legal advice in a specialized area of law • Translate spoken English into spoken Swedish in real time • Converse successfully with another person for an hour • Play a decent game of ping-pong • Unload any dishwasher and put everything away • Explore the Martian landscape • Explore downtown Washington

27. Why is AI Hard? • Simple syntactic manipulation is not enough • Machine Translation • Big project in 1957 following Sputnik launch • Translation of Russian documents • ‘The spirit is willing but the flesh is weak’ • ‘The vodka is strong but the meat is rotten’

28. Eliza • ELIZA: A program that simulated a psychotherapist interacting with a patient • Coded at MIT during 1964-1966 by Joel Weizenbaum. • First script was DOCTOR. • The script was a simple collection of syntactic patterns not unlike regular expressions • Each pattern had an associated reply which might include bits of the input (after simple transformations (my  your) • Weizenbaum was shocked at reactions: • Psychiatrists thought it had potential. • People unequivocally anthropomorphized. • Many thought it solved the NL problem. • Try typing Meta-x doctor in Emacs. • Recent Loebner Prize winner, http://www.alicebot.org/

29. Colby’s PARRY H: Good afternoon. My name is Doctor Jones. P: Pleased to meet you. H: How are you? P: All right I guess. H: Could you tell me your name? P: Pat Smith. H: I though you might have some problems. P: I've been upset lately. H: By what? P: People get on my nerves sometimes. H: Tell me more. P: Do you know anything about bookies? ... • Kenneth Colby modeled a paranoid using the same techniques circa 1968. • PARRY has basic emotions. If it gets angry, its replies become more hostile. • In a Turing test, expert psychiatrists were unable to distinguish PARRY’s ramblings from those of real paranoids.

30. Parry meets Eliza… P: I don't understand your motives. E: Why don't you understand my motives? P: Shouldn't I be? E: Why do you say that just now? P: That's just the way it is. E: Suppose that's the way it were not. P: So? E: I am not sure I understand you fully. P: You harp on the same questions too much. E: What does that similarity suggest to you? P: I don't get you. E: Do you wish that you did get I? Please tell me *this* isn't AI!

31. Why is AI Hard? Computational intractability • AI goal defined before notion of NP-completeness • people thought to solve larger problems we simply need larger/faster computers • didn’t understand the notion of exponential growth ALL of the algorithms we will study will be computational intractable (NP-complete as best) in the WORST case … How does the fact that we are dealing with the REAL WORLD make solving these computationally challenging problems feasible IN PRACTICE?

32. CMSC 421 • We will focus on the rational agents (“engineering”) paradigm • Make computers act more intelligently • Three major components: • representation • reasoning • learning

33. 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 13,14,15,16,17) • Learning (chapters 18,20,21)

34. Learning Goals for Class • You will learn a bunch of tools that are useful for building useful, adaptive software… to solve fun and challenging problems • These tools will be useful for you whether you go into AI research (basics that anyone should know) or any other discipline (oh, hey, that looks like the planning problems we studied way back in cmsc421) • Help you separate hype from what’s easily achievable using existing tools (and avoid reinventing them!)