Intelligent Systems

Lecture

Abstract

The lecture is an introduction to Intelligent Systems, the study of constructing intelligent systems based on intelligent agents. In the lecture we will introduce various logics (i.e. Propositional and Predicate logic), search methods, knowledge engineering techniques, problem-solving methods, planning, agents, learning techniques and Semantic Web- in short, all science areas that contribute towards making the general vision of Intelligent Systems a reality.

1 Introduction. This introductory lecture gives an overview of the course and introduces the subject of Artificial Intelligence (AI), in particular, knowledge-based systems. It presents popular AI systems, AI subdomains, relevant individuals and research directions.

2 Propositional Logic. This lecture explains logics and deduction, and introduces propositional logic. A detailed overview of propositional logic syntax, semantics and inference is provided.

3 Predicate Logic. This lecture explains restrictions of the propositional logic, motivates and introduces predicate logic. A detailed overview of predicate logic syntax, semantics and inference is provided.

4 Reasoning. This lecture provides foundations of reasoning, in particular, it covers Theorem Proving and Resolution, Description Logics, and Logic Programming.

5 Search Methods. The lecture presents search methods. Specifically, the mechanisms of uninformed search (depth-first search, breadth-first search), and informed search (best-first search, hill climbing, A* search) are described.

6 CommonKADS. This lecture focuses on CommonKADS methodology applicable to ontology engineering. It overviews knowledge model components, template knowledge models, knowledge model construction, and knowledge elicitation techniques.

7 Problem-Solving Methods. This lecture elaborates upon the utilization of knowledge in reasoning and introduces general problem solvers. The key topics are problem solving methods and approaches of development of knowledge-based systems towards problem solving methods.

8 Planning. This lecture considers planning as state-space search, for which there are several options: forward, backward, and heuristic. It also introduces partial-order planning as a more flexible approach. In all cases there are three implicit concerns considered: representation, algorithm, complexity and decidability.

9 Software Agents. This lecture introduces agents, including definitions, properties, environments, agents as intentional systems, abstract architecture, multi-agent systems. A large example is given from Robocop.

10 Rule Learning. This lecture primarily addresses the domain of decision tree learning. The material addresses rule learning tasks, rule learning approach (specialization, i.e. the ID3 Algorithm; generalization, i.e. the RELAX Algorithm), combining specialization and generalization (the JoJo algorithm), refinement of rule sets with JoJo, as well as an extension – the C4.5 Algorithm.

11 Inductive Logic Programming. This lecture introduces Inductive Logic Programming (ILP) as a combination of inductive learning and logic programming. The contents of the lecture comprise model theory of ILP, a generic ILP algorithm, proof theory of ILP, ILP systems and applications.

12 Formal Concept Analysis. This lecture introduces formal concept analysis: introduction and definitions, attribute exploration, and many-valued context. A larger example showcases the transformation from data to concept representation.

13 Neural Networks. The lecture presents neural networks: (artificial) neural networks, neural network structures, learning and generalization, expressiveness of multi-layer perceptrons. The given application illustrations comprise prediction of hazards, such as breast cancer reoccurrence.

14 Semantic Web and Services. This lecture presents a combination of the Semantic Web and services and their co-evolution. Core technology pillars are considered: URI, RDF, RDFS, OWL, SPARQL, and Semantic Web Services, WSMO, WSML, SEE, WSMX, as well as their applications.

Slides

Wikipedia

• http://en.wikipedia.org/wiki/Artificial_intelligence
• http://en.wikipedia.org/wiki/Turing_test
• http://en.wikipedia.org/wiki/General_Problem_Solver
• http://en.wikipedia.org/wiki/Propositional_calculus
• http://en.wikipedia.org/wiki/First-order_logic
• http://en.wikipedia.org/wiki/Logic_programming
• http://en.wikipedia.org/wiki/Description_logic
• http://en.wikipedia.org/wiki/Theorem_proving
• http://en.wikipedia.org/wiki/Depth-first_search
• http://en.wikipedia.org/wiki/Breadth-first_search
• http://en.wikipedia.org/wiki/Best-first_search
• http://en.wikipedia.org/wiki/A*_search_algorithm
• http://en.wikipedia.org/wiki/Hill_climbing
• http://en.wikipedia.org/wiki/Knowledge_engineering
• http://en.wikipedia.org/wiki/Expert_system
• http://en.wikipedia.org/wiki/Problem_solving
• http://en.wikipedia.org/wiki/Automated_planning_and_scheduling

Links

• http://www.springer.com/computer/foundations/book/978-1-85233-319-5
• http://www.iep.utm.edu/p/prop-log.htm
• http://linkeddata.org/
• http://classics.mit.edu/Aristotle/interpretation.html
• http://www.wsmo.org/
• http://www.wsmo.org/wsml/
• http://www.w3.org/Submission/WSMX/
• http://www.wsmo.org/papers/publications/wsmf.paper.pdf

Further reading

• 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

Tutorial

Abstract

The aim of this seminar (PS) is to further explore the topics discussed in the Intelligent Systems lectures by answering questions and solving problems directly related to various logics (i.e. Propositional and Predicate logic), search methods, knowledge engineering techniques, problem-solving methods, planning, agents, learning techniques and Semantic Web, etc. The seminar follows closely the lecture's material.

Tutorial slides