Table of Contents

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T-622-ARTI, Introduction to Artificial Intelligence, 2008-1

Course Head: Kristinn R. Thórisson
Instructors: Kristinn R. Thórisson, Hannes H. Vilhjálmsson, Ari K. Jónsson, Yngvi Björnsson
Teaching Assistants: Gylfi Guðmundsson, Eric Nivel
Classroom: K4, Kringlan 1

Readings for each class are listed on the lecture notes page for each class.
Use the internal RU website for the course to return assignments.

Readings

Supplementary Readings (Ítarefni)

Assignments

The bulk of the projects in the course will center around a simple creature in a virtual environment. (The system used in 2006 year can be found here: CADIAHexapd – feel free to familiarize yourself with it).

Thought Questions: weekly assignments for each topic where you write a question about the reading material. The topic of the TQs is the coming week's lecture topics, unless otherwise noted.
(Please note that submission of all Thought Question assignments during the course is a prerequisite for taking the final test and thus a prerequisite for finishing the course.)

Assignment ID Description Material ResourcesAssignedDueWeight
Program SubsumptionPROG:SubsumpSubsumption architecture with perception & motion control CADIARover page, Brooks, Subsumption Arch. Mo Feb 11 Mo Feb 18 5%
Program Planning PROG:Plan Th Feb 21 We Mar 05 5%
Homework Learning HW:Learn R&N Ch. 18 and 20.5 We Feb 27 We Mar 04 4%
Homework Logic HW:Logic R&N Ch. 7 and 8 Th Jan 24 Mo Feb 4 3%
Program Search PROG:Search R&N Ch. 3 and 4 We Feb 06 We Feb 13 4%
Final Project Final Proj. Agent for final competition CADIARover page and Everything We Mar 12 Th Mar 27 26%
Total 47%

Course Outline

W#DateTOPIC (assignment)EmphasisLecture Notes / MaterialWhoDue
JANUARY
1 Mo Jan 07Agents, Perception, Action Overview Agents I, R&N Ch 1, 2 KRTh
Mo Jan 07Agents, Perception, Action Agents II, R&N Ch 2 KRTh
We Jan 09 No Class
Th Jan 10Schools of AI Classical & Behavior-based AI KRTh Course Notes, R&N Ch 2 AKJ
2 Mo Jan 14Perception I Overview, Low-Level Vision Perception I, R&N Ch 24 KRTh TQs:Perc
Mo Jan 14Perception II Low-Level Vision Perception I, R&N Ch 24 KRTh
We Jan 16 LAB Vision, Perc. in VR
Ch 24: Exercises 24.1,
24.4,24.8,24.9,24.10
TA
Th Jan 17Perception III Speech perc., Hearing Perception II, R&N Ch 24 KRTh
3 Mo Jan 21Knowledge & Reasoning I Propositional Logic R&N Ch 7.1 - 7.5 HHV TQs:Knowl
We Jan 23 Knowledge & Reasoning II First-Order Logic R&N Ch 8.1 - 8.3 HHV
Th Jan 24 LAB (HW:Logic) Knowledge & Reasoning Powerloom
Worksheet
HHV
4 Mo Jan 28Reactive Architectures I Subsumption Architecture I,
Garlan & Shaw, Brooks
KRTh TQs:Arch
Mo Jan 28Reactive Architectures II Subsumption Architecture II,
Garlan & Shaw, Brooks
KRTh
We Jan 30 LAB Subsumption Excercise I TA
Th Jan 31Search I Overview of Search YB
FEBRUARY
5 Mo Feb 04Search II Solving problems R&N Ch 3.2 - 3.5 YB HW:Logic
Mo Feb 04Search III (HW:Search) Solving problems R&N Ch 3.2 - 3.5 YB
We Feb 06 LAB Search YB
Th Feb 07Perception III Speech perc., Hearing Perception II, R&N Ch 24 KRTh
6 Mo Feb 11Robotics Robotics, R&N Ch 25 KRTh,EN
Mo Feb 11Robotics
(PROG1:Subsump)
Robotics, R&N Ch 25 KRTh
We Feb 13 LAB PROG:Subsump, HMMs TA PROG:Search
Th Feb 14Planning I Overview of Planning R&N Ch 11 AKJ
7 Mo Feb 18Planning II Classical Planners R&N Ch 12 AKJ
Mo Feb 18Planning III Dynamic Planning and Acting R&N Ch 13 AKJ
We Feb 20 LAB Planning (preps. for PROG:Plan) TA PROG:Subsump
Th Feb 21Machine Learning I Learning Methods R&N Ch 18 YB
8 Mo Feb 25Machine Learning II Decision Trees R&N Ch 18, 20.5 YB
Mo Feb 25Machine Learning III
(HW:Learn)
Neural Networks R&N Ch 20.5 YB
We Feb 27 LAB Learning, HW:Learn YB
Th Feb 28Fuzzy Logic Fuzzy Logic KRTh
MARCH
9 Mo Mar 03Final Project design KRTh,EN
Mo Mar 03Natural Language Overview of NLP Systems and Problems Skim R&N Ch 22 - 23 HHV
Tu Mar 04 Assignment due HW:Learning YB HW:Learning
We Mar 05 LAB PROG:Subsump Presentation TA
Th Mar 06Natural Language Conversation System: Rea Skim R&N Ch 22 - 23 HHV TQs:Plan
10 Mo Mar 10History of AI The First 50 Years R&N Ch 1, 27 & Schools of thought KRTh TQs:History
Mo Mar 10History of AI The Next 50 Years R&N Ch 1, 27 & Future of AI KRTh
We Mar 12 LAB
(PROG/HW:Plan)
PROG:Plan, Final proj.: Arch & Design TA,KRTh
Th Mar 13Uncertainty R&N 14,17, Bayesian Networks AKJ TQs:Uncert
11 Mo Mar 17Final Project
Mo Mar 17Final Project PROG:Plan (midnight)
We Mar 19Easter Holliday - no class
Th Mar 20Easter Holliday - no class Review All All
12 Mo Mar 24Easter Holliday - no class Work on final project All All
Mo Mar 24Final Proj. Work on final project All All
We Mar 26 LAB Work on final project TA
Th Mar 27Final Proj. Work on final project All All
13 Mo Mar 31Final Proj. Work on final project All All
Mo Mar 24Final Proj. Work on final project All TAs
We Apr 02 FINAL COMPETITION Final Project Competition NOTE: Prepare for as
long a session as is needed
All Final Proj. (11:00 am)

*NB:This proposed schedule is subject to change at any time.

Grading

Part of CourseTotal Weight
Thought questions & Class Discussion Participation 10%
Programming assignments (x3) 14%
Homework assignments (x2) 7%
Final project 26%
Final written exam 43%
Total 100%