74.419 Artificial Intelligence Course Introduction and ROASS Document

1. 74.419 Artificial IntelligenceCourse Introduction and ROASS Document Instructor & Course Info Course Topics and approximate Schedule Assignments and Grade Breakdown The usual Stuff (including ‘How to fail this course’) Students introduce themselves

2. 74.419 Artificial Intelligence- Course Info - Time: Mon, Wed, Fri 2:30-3:20 pm Prerequisites: 74.319 Introduction to Artificial Intelligence Web page: http://www.cs.umanitoba.ca/~cs419 News Group: local.cs419 Instructor: Dr. Christel Kemke Textbook: Russell and Norvig: Artificial Intelligence - A Modern Approach, Prentice Hall, 1995 / 2003

3. Instructor Info Dr. Christel Kemke524 Machray Hall / 412 Engineering 2 Phone: (204) 474-8674 e-Mail: ckemke@cs.umanitoba.ca Web: www.cs.umanitoba.ca/~ckemke Office Hours:Tuesday 1:30 - 2:30 pm Thursday 1:30 - 2:30 pm by appointment

4. Course Objectives Acquire essential and advanced knowledge and skills in Artificial Intelligence concepts and methods Learning basic and advanced knowledge Experience through exercises and projects Skills scientific, research and applied Preparation for work and advanced studies

5. Class Structure Classes will comprise of • Lectures with Notes • Online Videos, Movies (PBS, SRI) • Some Labs (KR, maybe NLP) • StudentPresentations ("project presentations day", conference style) • Occasional in-class Exercises

6. Topics Outline • The Dream – Flakey and the others • General Introduction to Agents • SRI Movie • Knowledge Representation & Reasoning • Warming up with Propositional Calculus • First-Order Predicate Logic including Semantics ! • Representation with Frames, Inheritance Hierarchies, ... • Description Logics • Ontology - where elephants sometimes have 3 legs • Allen’s Time Logic (if there is time ...) • Exotic Logics (like Deontic Logic) - Do we have to go to class? Or can we go to class? Or can we not go?

7. Topics Outline 2 • Planning • Introduction with Shakey - The first "Real Robot" • Planning as Search – STRIPS • ABSTRIPS • Partial-Order Planning • Hierarchical Plan Decomposition • Situation Calculus • Standard Problems (everywhere in this section) • Special Topics or maybe more videos?

8. Topics Outline 3 • Natural Language Processing • a short introduction to Speech Recognition - is speech nothing but 'hot air'? • Overview of Natural Language Processing (NLP) - what did you say? • Syntax, Grammar - "What's up" - is this really a real sentence? • Syntactic Sentence Analysis, or Parsing • a little bit of Semantics - what do you mean? • something about Discourse and Dialogue - or "Could you please close the door?" • Videos and demos

9. Topics Outline 4 – ‘Free Style’ (optional, depending on time) Neural Networks • General NN Model & Processing • NN Architectures • Learning Paradigms for Neural Networks • some example demos, maybe a video Evolutionary Algorithms • General Principles of Evolutionary Computing • more videos than theory

10. The Standard Textbook Stuart Russell and Peter Norvig, Artificial Intelligence – A Modern Approach, Prentice Hall, 1995 & 2003 available in The Bookstore, ~ 95 CAD

11. Textbook: Table of Contents I. ARTIFICIAL INTELLIGENCE. 1. Introduction. 2. Intelligent Agents. II. PROBLEM-SOLVING. 3. Solving Problems by Searching. 4. Informed Search and Exploration. 5. Constraint Satisfaction Problems. 6. Adversarial Search.

12. III. KNOWLEDGE AND REASONING. 7. Logical Agents. 8. First-Order Logic. 9.Inference in First-Order Logic.10. Knowledge Representation. In Addition: Description Logics Non-Standard Logics Semantics of FOPL

13. IV. PLANNING. 11. Planning. 12. Planning and Acting in the Real World. Focus: Situation Calculus Partial Order Planning Hierarchical Planning

14. V. UNCERTAIN KNOWLEDGE AND REASONING. 13. Uncertainty. 14. Probabilistic Reasoning Systems. 15. Probabilistic Reasoning Over Time. 16. Making Simple Decisions. 17. Making Complex Decisions. VI. LEARNING. 18. Learning from Observations. 19. Knowledge in Learning. 20. Statistical Learning Methods. 21. Reinforcement Learning.

15. VII. COMMUNICATING, PERCEIVING, AND ACTING. 22. Communication. 23. Probabilistic Language Processing. 24. Perception.25. Robotics. Focus: Deterministic Natural Language Processing (Parsing, a little bit Semantics)

16. VIII. CONCLUSIONS. 26. Philosophical Foundations. 27. AI: Present and Future.

17. Second AI Textbook Nils J. Nilsson, Artificial Intelligence – A New Synthesis, Morgan Kaufman, 1998

18. Another AI Textbook George Luger and William Stubblefield: Artificial Intelligence,Addison-Wesley, 1998 and 2001 (CS 319 textbook)

19. Reference Book - NLP Daniel Jurafsky / James Martin, Speechand Language Processing, Prentice Hall, 2000

20. Reference Book - KR and Logic Richard A. Frost, Introduction to Knowledge-Base Systems, Collins, 1986 too old to be shown; excellent book.

21. Assignments 3 Standard Homework Assignments • Knowledge Representation • Planning • Natural Language Processing 1 Individual Research Report • Essay • Presentation • Program 1 Group Project

22. Group Project Group Project(with typically 3 Students) Design and Implementation of an Intelligent Agent System with Knowledge Base (KB), Planning Module, and Natural Language Interface (NLI), or an equivalent project. examples: • a Household Robot • a Mars Rover • a Scheduling / Planning System with NLI Students need to write a proposal for the project and a final report, plus give a presentation with demo.

23. Grade Breakdown Homework 30% Individual Research 10% Project 10% Final Exam 50% ---------- 100% Occasionally, Bonus Points are issued for exceptional efforts beyond the requirements.

24. Course Schedule (approximately) Introduction & Agents week 1-2 Knowledge Representation week 3-4 KR Lab week 5 Natural Language Proc. week 6-7 Planning week 8-9 Free Style week 10-11 Group Project Presentation week 12 Exam preparation last week may swap

25. Deadline Policy Assignments are to be submitted before the due date. Unless otherwise specified, they have to be dropped into the 419 slot, left of the entrance to the Cargill Lab. If electronic submissions are requested, they have to be sent to cs419@cs.umanitoba.ca . Extensions to a deadline can be granted only by the instructor (that's me, Dr. Kemke). In general, no late assignment will be accepted after the marked assignments have been returned. KEEP COPIES OF SUBMITTED ASSIGNMENTS!

26. Class Communication, Notes, Attendance • Class Notes will in general be provided via the course web page. • Non-web material will be made accessible on-line, per handout in class, or in a folder on reserve for 74.419 in the Science Library. • Class attendance is not checked but students are responsible for knowing the contents of the classes. Officially, students are required to attend classes. • You can use the news group to discuss course related questions and problems with your 419 class mates. • Otherwise, questions should be addressed to me personally or via e-mail.

27. Illness and other problems In case of longer times of illness or other problems like bereavement, which considerably influence class attendance and course performance, students are advised to contact the instructor in order to find arrangements for continuing successfully with the course. In case students encounter other substantial course-related problems, they are also advised to contact the instructor or TA.

28. Misuse of Computer Facilities, Plagiarism, and Cheating • These serious offenses will carry sanctions. Copying of assignments or parts thereof from anywhere without appropriate references, cheating on exams, or misusing facilities will result in punishment ranging from course failure to prosecution. • Please see section 7 of the General Academic Regulations and Requirements in the U of M General Calendar for more information.

29. Final Exam Time and location of the final exam will be announced by the Student Records Office. It is your own responsibility to make yourself aware of the posted exam schedules. You are obligated to make yourself available for the writing of the final exam.

30. How to Fail this Course or Get a Bad Grade • A good starting point is not to attend classes on a regular basis. • Do not look at the Course Notes either. Just forget about the whole course web site. • Never ever ask or talk to fellow students or the instructor about the course contents. If you missed a class (or more) or if you can't grasp something, just hide and play cool.

31. How to Fail this Course or Get a Bad Grade • Don't cooperate with your project partners in the group project. Tell them you have so many other things to do, you just don't have time to meet and work with them. • Do not come to the presentation of your group project. Or ask the instructor for a last minute change of the schedule. (The best time is the morning of the day when your presentation is scheduled.)

32. How to Fail this Course or Get a Bad Grade The safest way to fail the course is: Do not attend the final exam. Go on holidays during exam time, or hide in a safe place. Wait until exam time is over. After Christmas, you get in touch with the Faculty / Department / Instructor and ask for special permission to take the exam now. Of course, you have no evidence of having been terribly ill, or proof of other serious problems, which would excuse that you missed the exam.

33. Course Partner Every student is asked to have a course partner! Course Partners have a mutual commitment to help each other with course related questions and problems. They have to inform each other about the class contents, in case one of them missed a class (for good reasons, not just to dump it on the other one). In case both course partners have to miss a class, they are asked to contact other students or the instructor, to inform themselves about the missed class. Course partners are also supposed to help each other and to cooperate - within in reasonable limits - with the goal to understand the course material and expand their knowledge.