Instructor
Dr. Jay Modi
Department of Computer Science
Drexel University
Office: University Crossings 106
Tel: 1 215 895 1518
Email: pmodi AT cs drexel edu
Office Hours: Wed 4:00-5:00 or by appt
Teaching Assistant:
TBA
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Announcements
- 2/28/07: Assignment 2 and 3 are out. Assignment due dates are Mar 7 and Mar 14 respectively. Download here: [PDF]
- 1/31/07: Assignment 1 is out. Assignment due date is Feb 14. Download here: [PDF]
- 1/03/07: I will miss our first class on 1/10/07 due to travel to an academic conference. I sincerely apologize for my absence.
Course Overview
This course is about the theory and practice of constructing systems
(machines) that can be considered intelligent. The course will cover
both theoretical aspects of AI through readings and practical
aspects of AI through programming and implementation.
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Prerequisites
- An introductory knowledge of
Artificial Intelligence (e.g. CS380 or CS510). A basic competency in
fundamentals of AI including agents, logic, search methods, heuristics
and programming skills will be assumed.
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Textbook
Russell and Norvig,
Artificial Intelligence: A Modern Approach
, the Prentice Hall Series in Artificial Intelligence. ISBN 0-13-790395-2.
Second Edition (green color)
We will use this book but will also cover many topics not covered in
the textbook. Supplemental readings in the form of research papers
will be a significant part of the course.
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Coursework and Grading
Grading will consist of a midterm exam, final exam and several homework
assignments. The midterm and final will be written, in class and cover
topics from readings and lectures.
Grading Criteria
- Assignment 1: 20%
- Assignment 2: 20%
- Assignment 3: 20%
- Midterm Exam: 20%
- Final Exam: 20%
- Class Participation (xtra credit): 5%
Schedule
Note: This schedule is tentative and can change.
Segment I: Reasoning with Logical Inference (Single agent and Multi-agent)
- [Week 1,2,3] Review of Basic AI Concepts
- Agents and Multiagent Systems (Chap 1 & 2)
- The Uses of Plans. Martha Pollack. Artificial Intelligence, 57(1):43-69, 1992. [PS]
- On Acting Together. H.J. Levesque, P.R. Cohen, and J.T.H Nunes. National Conference on Artificial Intelligence, 1990. [PDF]
- Review of Constraint Satisfaction Problems (Chap 5)
- [Week 4] Distributed Constraint Reasoning
- Algorithms for Distributed Constraint Satisfaction: A
Review. M. Yokoo, K. Hirayama. Autonomous Agents and Multi-Agent Systems,
Vol.3, No.2, pp.189--212,
2000.
[PDF]
Segment II: Reasoning with Uncertainty (Single agent and Multi-agent)
- [Week 5] Probability Theory (Chap 13)
- Graphical Models, M. I. Jordan, Stat. Science, Vol. 19, No. 1,
140-155, 2004. [PDF] .
- [Week 6,Week 8] MDPs/POMDPs (Chap 17)
- Planning and Acting in Partially Observable Stochastic Domains. Leslie Pack Kaelbling, Michael L. Littman and Anthony R. Cassandra, Artificial Intelligence, Vol. 101, 1998. [PDF]
Segment III: Learning (Single agent and Multi-agent)
- [Week 8-9] Reinforcement Learning (Chap 21)
- Reinforcement Learning: A Survey. Leslie Pack Kaelbling, Michael L. Littman, and Andrew W. Moore, Journal of Artificial Intelligence Research, Volume 4, 1996. [PS]
- [Week 10] Multi-Agent Learning
- If Multi-Agent Learning is the Answer, What is the Question? Y. Shoham, R. Powers and T. Grenager. Special issue of the AIJ (Journal of Artificial Intelligence) [PDF]
Lecture Notes and Handouts
- Jan 10: No lecture notes.
- Jan 17: Intro to Agents and Multiagent Systems [PDF]
- Jan 24: Teamwork and Multiagent Systems [PDF]
- Jan 31: Distributed Constraint Reasoning [PDF]
- Jan 31: Assignment 1 is out. [PDF]
- Feb 7: Probability and Decision Theory. [PDF]
- Feb 7: Practice DCSP for assignment 1 are available here: [tar.gz]
- Feb 14: Markov Decision Processes, Value Iteration, Policy Iteration. [PDF]
- Feb 28: Partially Observable MDPs [PDF]
- Feb 28: Assignment 2 and 3 are out. [PDF]
- Mar 7: Machine Learning, Reinforcement Learning [PDF]
- Mar 14: Game Theory, Machine Learning [PDF]
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