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74.419 Artificial Intelligence Course Introduction and ROASS Document

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  1. 74.419 Artificial IntelligenceCourse Introduction and ROASS Document 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/Norvig: Artificial Intelligence

  3. Instructor Info Dr. Christel Kemke562 Machray HallPhone: (204) 474-8674 e-Mail: ckemke@cs.umanitoba.ca Office Hours:Tuesday 1:00-3:00pm Mon, Wed after class by appointment

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

  5. Course Objectives Essential and Advanced Knowledge and Skills in Artificial Intelligence Learning basic and advanced knowledge Experience through tasks and projects Skills scientific, research and applied Preparation for work and advanced studies (You will get a bit of LISP, too.)

  6. Class Structure Classes will comprise of • Lectures with Notes • Online Videos, Movies (PBS, SRI) • Student Project Presentations (one-day ‘conference’) • Occasional in-class Exercises

  7. Topics Outline • The Dream – Flakey and other Agents • SRI Movie • General Introduction to Agents • Knowledge Representation & Reasoning • Review Propositional Calculus • First-Order Predicate Logic including Semantics ! • Representation with Frames, Inheritance Hierarchies, ... • A tiny bit of Description Logics • Ontology - where elephant sometimes have 3 legs • Allen’s Time Logic • Exotic Logics (like Deontic Logic) - who likes Hawaii and can we go there or do we have to go there or can we not go there?

  8. 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?

  9. Topics Outline 3 • Natural Language Processing • a short Introduction to Speech Recognition (nothing but 'hot air') • Overview of Natural Language Processing (NLP) • Syntax, Grammar - "What's up" could really be a real sentence, and find this out here: • Syntactic Sentence Analysis, or Parsing (Chartparser, Earley-Algorithm) • a little bit of Semantics (What does that mean?) • something about Discourse and Dialogue ("I will get a cold!" or "Could you please close the door?") • Command Talk video and some demos

  10. Topics Outline 4 – ‘Free Style’ (optional part) 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)

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

  12. Russell and Norvig 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.

  13. III. KNOWLEDGE AND REASONING. 7. Logical Agents. 8. First-Order Logic. 9. Inference in First-Order Logic. 10. Knowledge Representation. IV. PLANNING. 11. Planning. 12. Planning and Acting in the Real World.

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

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

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

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

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

  20. Assignments 3 Homework Assignments • Knowledge Representation • Planning • Natural Language Processing Group Project (3 Students) Design and Implementation of an Intelligent Agent System with Knowledge Base, Planning Module, and simple Natural Language Interface, modeling • a Household Robot or • a Mars Roveror • your own Agent (if confirmed by the instructor)

  21. Grade Breakdown Project 20% Homework (all 3 together) 30% Final Exam 50% 100% In addition, you can get Bonus Points for exceptional efforts, good in-class participation, work beyond requirements. These can be a few percent, which are calculated on top of the 100% scale.

  22. Course Schedule (approximately) Introduction week 1 8-12 Sept Knowledge Represent. week 2-5 Planning week 6-7 Lab week 8 27-31 Oct Natural Language Proc. week 9-10 Free Style week 11 Group Project Present. week 12 Exam preparation last week3-5 Dec

  23. Deadline Policy Assignments are to be submitted before the due date. Unless otherwise specified, they have to be dropped into the slot for CS 419 outside the Cargill Lab. Electronic submissions (program code) has to be sent to cs419@cs.umanitoba.ca (except final project). Extensions to a deadline can be granted only by the instructor (Dr. Kemke). In general, no late assignment will be accepted after the marked assignments have been returned. KEEP COPIES OF SUBMITTED ASSIGNMENTS!

  24. 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 or in a Course Folder in the Library • Class attendance is not checked but students are responsible for knowing the contents of the classes • Course news group will be set up • Questions can be sent via e-mail to me

  25. 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 it and play cool.

  26. 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 the 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.)

  27. How to Fail this Course or Get a Bad Grade • Do not attend the exam preparation. • The safest way to fail the course is: Go on holidays during exam time, or hide in a safe place. Wait there until exam time is over. After Christmas, get in touch with the Faculty/ Department/ Instructor and ask for special permission to take the exam now.

  28. Course Partner Every student is asked to have a course partner! Course Partners have a mutual commitment to inform each other about the class contents, in case one of them has missed a class (for acceptable reasons). In case both course partners have to miss a class, they are asked to contact other students, and if necessary the instructor, to inform themselves on the class contents.

  29. 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.

  30. 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.

  31. 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. • Advice for preparing the final exam will be given at an appropriate time in class.