LFU:online

The seminar is an introduction to Artificial Intelligence. In the seminar we will practice concepts introduced during the associated lecture.

Various logics (i.e. Propositional and Predicate logic), search methods, knowledge engineering techniques, problem-solving methods, planning, agents, learning techniques and Semantic Web.

Bi-weekly exercise sheets covering different topics of Artificial Intelligence

The grade will be based on points gained by doing exercises, which will be presented by the students during the sessions. All the students have to present at least once.

• Görz et. al. (eds.): Handbuch der Künstlichen Intelligenz, 2000
• A. Turing. "Computing Machinery and Intelligence", Mind LIX (236): 433–460, Ocotober, 1950.
• A. Newell, H.A. Simon, “Human Problem Solving” Englewood Cliffs, N.J.: Prentice Hall, 1972
• J. Weizenbaum. "ELIZA - A Computer Program For the Study of Natural Language Communication Between Man And Machine", Communications of the ACM 9 (1): p. 36–45, 1966.
• A. Newell and H. Simon "GPS, a program that simulates human thought" In: Computation & intelligence: collected readings, pp. 415 - 428, 1995.
• R. J. Brachman “On the Epistemological Status of Semantic Networks” In: N.V. Findler (ed.): Associative Networks: Representation and Use of Knowledge by Computers. New York: Academic Press, 1979, 3-50.
• D. Fensel “Problem-Solving Methods: Understanding, Description, Development and Reuse”,, Springer LNAI 1791, 2000
• E.A. Feigenbaum. “The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering,” Proceedings of the International Joint Conference on Artificial Intelligence, Cambridge, MA, 1977
• W.J. Clancey. “Heuristic Classification”, Artificial Intelligence, 27:289-350, 1985
• A. Robinson and A. Voronkov, Handbook of Automated Reasoning, Volume I, 2001, MIT Press, Chapter 2: Resolution Theorem Proving
• J. D. Ullman, Principles of Database and Knowledge-Base Systems, Volume I, 1988, Computer Science Press, Chapter 3: Logic as a Data Model (Logic Programming & Datalog)
• G. Schreiber, H. Akkermans, A. Anjewierden, R. de Hoog, N. Shadbolt, W. Van de Velde and B. Wielinga. Knowledge Engineering and Management: The CommonKADS Methodology, MIT Press, ISBN 0262193000. 2000.
• S. Russell and P. Norvig. “AI: A Modern Approach” (2^nd Edition), Prentice Hall, 2002
• G. Malik; D.S. Nau, P. Traverso (2004), Automated Planning: Theory and Practice, Morgan Kaufmann, ISBN 1-55860-856-7
• M. Wooldridge, M. Jennings; „Intelligent Agents: Theory and Practice“; The Knowledge Engineering Review 10.
• J.R. Quinlan, “Induction of decision trees”. Machine Learning 1 (1), pp. 81-106, 1986.
• J.R. Quinlan, “C4.5: Programs for Machine Learning” Morgan Kaufmann, 1993.
• D. Fensel and M. Wiese: Refinement of Rule Sets with JoJo. European Conference on Machine Learning, 1993, pp. 378-383.
• N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications. 1994.
• B. Ganter, G. Stumme, R. Wille (Hg.): Formal Concept Analysis: Foundations and Applications. Springer, 2005, ISBN 3-540-27891-5.
• U. Priss: Formal Concept Analysis in Information Science. Annual Review of Information Science and Technology 40, 2006, pp. 521-543.
• S.I. Gallant (1990): Perceptron-based learning algorithms. IEEE Transactions on Neural Networks 1 (2), pp. 179-191.
• D. Fensel. Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce, 2nd Edition, Springer 2003.
• G. Antoniou and F. van Harmelen. A Semantic Web Primer, (2nd edition), The MIT Press 2008.
• T. Berners-Lee. Weaving the Web, HarperCollins 2000
• T.R. Gruber, Toward principles for the design of ontologies used or knowledge sharing? , Int. J. Hum.-Comput. Stud., vol. 43, no. 5-6, 1995

Basic knowledge of algorithmics and programming languages (preferrably Java)

Attendance is mandatory