Instructor & Course InfoCourse Topics and approximate ScheduleAssignments and Grade BreakdownThe usual Stuff (including ‘How to fail this course’)Students introduce themselves

74.419 Artificial Intelligence Course Introduction and ROASS Document

Time: Mon, Wed, Fri 2:30-3:20 pmPrerequisites: 74.319 Introduction to Artificial IntelligenceWeb page: http://www.cs.umanitoba.ca/~cs419News Group: local.cs419Instructor: Dr. Christel KemkeTextbook: Russell and Norvig: Artificial Intelligence - A Modern Approach, Prentice Hall, 1995 / 2003

74.419 Artificial Intelligence - Course Info -

Dr. Christel Kemke524 Machray Hall / 412 Engineering 2 Phone: (204) 474-8674e-Mail: ckemke@cs.umanitoba.caWeb: www.cs.umanitoba.ca/~ckemkeOffice Hours: Tuesday 1:30 - 2:30 pm

Thursday 1:30 - 2:30 pm by appointment

Instructor Info

Course Objectives Acquire essential and advanced knowledge and skills in Artificial Intelligence concepts and methodsLearning basic and advanced knowledgeExperience through exercises and projectsSkills scientific, research and applied Preparation for work and advanced studies

Class Structure

Classes will comprise of

• Lectures with Notes

• Online Videos, Movies (PBS, SRI)

• Some Labs (KR, maybe NLP)

• Student Presentations ("project presentations day", conference style)

• Occasional in-class Exercises

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?

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?

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

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

The Standard Textbook

Stuart Russell and Peter Norvig, Artificial Intelligence – A Modern Approach, Prentice Hall, 1995 & 2003

available in The Bookstore, ~ 95 CAD

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.

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 LogicsSemantics of FOPL

IV. PLANNING. 11. Planning. 12. Planning and Acting in the Real World.

Focus: Situation CalculusPartial Order PlanningHierarchical Planning

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.

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)

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

Second AI Textbook

Nils J. Nilsson, Artificial Intelligence – A New Synthesis, Morgan Kaufman, 1998

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

Reference Book - NLP

Daniel Jurafsky / James Martin, Speech and Language Processing, Prentice Hall, 2000

Reference Book - KR and Logic

Richard A. Frost, Introduction to Knowledge-Base Systems, Collins, 1986

too old to be shown; excellent book.

Assignments3 Standard Homework Assignments• Knowledge Representation • Planning • Natural Language Processing

1 Individual Research Report• Essay• Presentation• Program

1 Group Project

Group ProjectGroup 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.

Grade Breakdown

Homework 30%Individual Research 10%Project 10%Final Exam 50% ---------- 100%

Occasionally, Bonus Points are issued for exceptional efforts beyond the requirements.

Course Schedule (approximately)

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

may swap

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!

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.

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.

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.

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.

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.

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.)

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.

Course PartnerEvery 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.