This is a second course in artificial intelligence that covers selected topics in this area such as: reasoning about action and planning, uncertain and fuzzy reasoning, knowledge representation, automated reasoning, non-monotonic reasoning and answer set programming, ontologies and description logic, local search methods, Markov decision processes, autonomous agents and multi-agent systems, machine learning, reasoning about beliefs and goals, and expert systems.
Knowledge-based systems that use symbolic representations of knowledge and automated reasoning are well established and their performance is often quite good. However it can be very costly to have experts input the knowlege that they use. Recently, given the availability of labelled training data, there has been a lot of work on using maching learning techniques to build AI applications, which often achieve with very good performance. However, this has lead to concerns about the safety of such systems and their inability to explain their behavior. Given this, there is much interest in understanding how to relate and combine knowledge-based systems techniques and maching learning methods. The course will cover a broad range of current symbolic knowledge representation and reasoning techniques, including probabilistic methods, and also discuss associated mchine learning techniques, in particular, reinforcement learning.
Prof. Yves Lespérance
Office: LAS 3052A
Email: lesperan@eecs.yorku.ca
Tuesday and Thursday from 10:00 to 11:30 in LSB 101 (Life Sciences Building).
Tuesdays and Thursdays from 12:00 to 13:00.
Russell, S.J. and Norvig, P.,
Artificial Intelligence: A Modern Approach, 4th edition
Prentice Hall, 2020. ISBN 978-0134610993.
Authors' web site,
Publisher's web site.
(If you already have the 3rd edition, you can use that instead of the 4th.)
Ronald J. Brachman and Hector J. Levesque,
Knowledge Representation and Reasoning,
Elsevier/Morgan Kaufmann 2004. ISBN 1-55860-932-6.
Publisher's web site,
York U Library e-copy (access with Passport York).
General prerequisites; LE/EECS3401 3.00 Introduction to Artificial Intelligence and Logic Programming. You should know first-order logic. You must know either Prolog or Java, preferably both.
| Assignments (2 @ 10% (each) | 20% |
| Midterm Test | 20% |
| Project Proposal | 5% |
| Project Presentation | 10% |
| Project Report | 20% |
| Final Exam | 25% |
| Total | 100% |
It is important that you look at the departmental guidelines on academic honesty. You must cite all sources that you use in answering assignment and test questions, and in your project report. You may not collaborate with others in course assignments, tests, and project unless indicated, and all collaborators must be listed.
Other good AI textbooks:
On First-Order Logic:
Enderton, H.B., A Mathematical Introduction to Logic. Academic Press, New York, 1972.
Tourlakis, G., Mathematical Logic. Wiley, 2008. York U Library e-copy (access with Passport York).
On knowledge representation:
Baral, C. Knowledge representation, reasoning, and declarative problem solving. Cambridge University Press, Cambridge/New York, 2003.
Genesereth, M.R. and Nilsson, N.J. Logical foundations of artificial intelligence. Morgan Kaufmann, Los Altos, CA, 1987.
Van Harmelen, F., Lifschiltz, V., and Porter, B. Handbook of Knowledge Representation. Elsevier, Amsterdam, 2008.
On description logic:
Baader, F., Calvanese, D., McGuiness, D., Nardi, D., Patel-Schneider, P. The Description Logic Handbook, 2nd Edition. Cambridge Univ. Press, Cambridge UK, 2007.
On reasoning about action:
Reiter, R., Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems, MIT Press, 2001. York U Library e-copy (access with Passport York), Book home page.
On AI Planning:
Geffner, H. and Bonet, B. A Concise Introduction to Models and Methods for Automated Planning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2013. York U Library e-copy (access with Passport York).
Haslum, P., Lipovetzky, N., Magazzeni, D., and Muise, C. An Introduction to the Planning Domain Definition Language. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2019. York U Library e-copy (access with Passport York).
Hoffmann, J. Everything you always wanted to know about planning - (but were afraid to ask). In Bach, J. and Edelkamp, S., editors, KI 2011: Advances in Artificial Intelligence, 34th Annual German Conference on AI, Berlin, Germany, October 4-7, 2011. Proceedings, volume 7006 of Lecture Notes in Computer Science, pages 1–13. Springer, 2011.
Ghallab, M. Nau, D, and Traverso, M. Automated Planning: Theory & Practice, Morgan Kaufmann, 2004.
On Machine Learning:
Murphy, K.P. Machine Learning: A Probabilistic Perspective. Adaptive computation and machine learning. MIT Press, 2012. York U Library e-copy (access with Passport York).
Mitchell, T.M. Machine Learning. McGraw-Hill, 1997.
Goodfellow, I., Bengio, Y., and Courville, A. Deep Learning. Adaptive computation and machine learning. MIT Press, 2016.
On Reinforcement Learning:
Sutton, R. S. and Barto, A. G. Reinforcement Learning - An Introduction, 2nd Edition. Adaptive computation and machine learning. MIT Press, 2018. York U Library e-copy (access with Passport York).
R. Toro Icarte, T. Q. Klassen, R. Valenzano, and S. A. McIlraith. Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning, J. of AI Research, 73, 173-208, 2022. See Toro Icarte's web page.
On Prolog:
Clocksin, W.F. and Mellish, C.S., Programming in Prolog, (5th edition), Springer Verlag, New York, 2004. York U Library e-copy (access with Passport York).
Bratko, I. Prolog Programming for Artificial Intelligence 4th Edition, Pearson Education Canada, 2012.
Sterling, L.S. and Shapiro, E.Y. The Art of Prolog, Second Edition, MIT Press, 1994.
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Running SWI-Prolog in the Prism Lab |