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Introduction to Artificial Intelligence PDF

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Copyright'2009,W.Ertel 1 Slides for the book Introduction to Artificial Intelligence Wolfgang Ertel Springer-Verlag, 2011 www.hs-weingarten.de/~ertel/aibook www.springer.com last update: October 31, 2013 Contents References 3 1 Introduction 13 2 Propositional Logic 39 3 First-order Predicate Logic 76 4 Limitations of Logic 141 5 Logic Programming with PROLOG 161 6 Search, Games and Problem Solving 190 7 Reasoning with Uncertainty 246 8 Machine Learning and Data Mining 348 9 Neural Networks 509 10 Reinforcement Learning 588 Bibliography The RoboCup Soccer Simulator. http://sserver.sourceforge.net 3 Alpaydin, E.: Introduction to Machine Learning. MIT Press, 2004 8.8, 9.6 Anderson, J./Pellionisz, A./Rosenfeld, E.: Neurocomputing (vol. 2): direc- tions for research. Cambridge, MA, USA: MIT Press, 1990 9.8 Anderson, J./Rosenfeld, E.: Neurocomputing: Foundations of Research. Cam- bridge, MA: MIT Press, 1988, Sammlung von Originalarbeiten (document), 1, 9.8 Bartak, R.: Online Guide to Constraint Programming. http://kti.ms. mff.cuni.cz/\protect\unhbox\voidb@x\penalty\@M\{}bartak/ Copyright'2011,W.Ertel 4 constraints, 1998 5.8 Barto, A. G./Mahadevan, S.: Recent advances in hierarchical reinforcement learning. Discrete Event Systems, Special issue on reinforcement learning, 13 2003, 41–77 10.1 Bellman, R.E.: Dynamic Programming. Princeton University Press, 1957 10 Berrondo, M.: Fallgruben fu¨r Kopffu¨ssler. Fischer Taschenbuch Nr. 8703, 1989 2.4 Bibel, W.: Deduktion: Automatisierung der Logik. Volume 6.2, Handbuch der Informatik. Oldenbourg, 1992 3.5, 3.5, 3.9 Billard, A. et al.: Robot Programming by Demonstration. In Siciliano, B./ Khatib, O., editors: Handbook of Robotics. Springer, 2008, 1371– 1394 10.2 Bl¨asius, K.H./Bu¨rckert, H.-J.: Deduktionssysteme. Oldenbourg, 1992 3.5, 3.9 Copyright'2009,W.Ertel 5 Bratko, I.: PROLOG: Programmierung fu¨r Ku¨nstliche Intelligenz. Addison- Wesley, 1986 5, 5.8 Burges, C. J.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Min. Knowl. Discov. 2 1998, Nr. 2, 121–167 9.6 Chang, C. L./Lee, R. C.: Symbolic Logic and Mechanical Theorem Proving. Orlando, Florida: Academic Press, 1973 3.9 Clocksin, W. F./Mellish, C. S.: Programming in Prolog. 4th edition. Berlin, Heidelberg, New York: Springer, 1994 5, 5.8 Dassow, J.: Logik fu¨r Informatiker. Teubner Verlag, 2005 3.9 Diaz, D.: GNU PROLOG. Universit¨at Paris, 2004, Aufl. 1.7, fu¨r GNU Prolog version 1.2.18, http://gnu-prolog.inria.fr 5.1, 5.8 D.J. Newman, S. Hettich, C.L. Blake/Merz, C.J.: UCI Repository of ma- chine learning databases. http://www.ics.uci.edu/\protect\unhbox\ voidb@x\penalty\@M\{}mlearn/MLRepository.html, 1998 8.8 Copyright'2011,W.Ertel 6 Duda, R.O./Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, 1973, Klassiker zur Bayes-Decision-Theorie (document) Duda, R.O./Hart, P.E./Stork, D.G.: Pattern Classification. Wiley, 2001, Neuauflage des Klassikers Duda/Hart 7.6, 8.6, 15, 8.8 Eder, E.: Relative Complexities of First Order Calculi. Vieweg Verlag, 1991 3.3 Ertel, W./Schumann, J./Suttner, Ch.: Learning Heuristics for a Theorem Prover using Back Propagation. In Retti, J./Leidlmair, K., editors: 5. ¨ Osterreichische Artificial-Intelligence-Tagung. Berlin, Heidelberg: Informatik- Fachberichte 208, Springer-Verlag, 1989, 87–95 4.1, 4, 8 Fischer, B./Schumann, J.: SETHEO Goes Software Engineering: Application of ATP to Software Reuse. In Conference on Automated Deduction (CADE 97). Springer, 1997, http://ase.arc.nasa.gov/people/schumann/ publications/papers/cade97-reuse.html, 65–68 3.8 Freuder, E.: In Pursuit of the Holy Grail. Constraints, 2 1997, Nr. 1, 57–61 5.7 Copyright'2009,W.Ertel 7 G¨orz, G./Rollinger, C.-R./Schneeberger, J., editors: Handbuch der Ku¨n- stlichen Intelligenz. Oldenbourg Verlag, 2003 3.9 Jensen, F.V.: Bayesian networks and decision graphs. Springer-Verlag, 2001 7.5, 7.5, 7.6, 8.4, 9 Kaelbling, L.P./Littman, M.L./Moore, A.P.: Reinforcement Learn- ing: A Survey. Journal of Artificial Intelligence Research, 4 1996, 237–285, www-2.cs.cmu.edu/afs/cs/project/jair/pub/volume4/ kaelbling96a.pdf 10.2 Kalman, J.A.: Automated Reasoning with OTTER. Rinton Press, 2001, www-unix.mcs.anl.gov/AR/otter/index.html 3.6 Lauer, M./Riedmiller, M.: Generalisation in Reinforcement Learning and the Use of Obse rvation-Based Learning. In Kokai, Gabriella/ Zeidler, Jens, editors: Proceedings of the FGML Workshop 2002. 2002, http://amy.informatik.uos.de/riedmiller/publications/ lauer.riedml.fgml02.ps.gz, 100–107 2 Copyright'2011,W.Ertel 8 Letz, R. et al.: SETHEO: A High-Performance Theorem Prover. Journal of Auto- mated Reasoning, 1992, Nr. 8, 183–212, www4.informatik.tu-muenchen. de/\protect\unhbox\voidb@x\penalty\@M\{}letz/setheo 3.6 Melancon, G./Dutour, I./Bousque-Melou, G.: Random Generation of Dags for Graph Drawing. Dutch Research Center for Mathematical and Computer Science (CWI), 2000 (INS-R0005). – Technical report, http://ftp.cwi.nl/ CWIreports/INS/INS-R0005.pdf 10 Minsky, M./Papert, S.: Perceptrons. MIT Press, Cambridge, MA, 1969 3 Mitchell, T.: Machine Learning. McGraw Hill, 1997, www-2.cs.cmu.edu/ \protect\unhbox\voidb@x\penalty\@M\{}tom/mlbook.html 2, 8, 8.8, 10.2 Nipkow, T./Paulson, L.C./Wenzel, M.: Isabelle/HOL — A Proof Assistant for Higher-Order Logic. Volume 2283, LNCS. Springer, 2002, www.cl.cam. ac.uk/Research/HVG/Isabelle 3.6, 4.1 Palm, G.: On Associative Memory. Biological Cybernetics, 36 1980, 19–31 2 Copyright'2009,W.Ertel 9 Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Networks of Plausible Inference. Morgan Kaufmann, 1988 7.5, 7.6 Rich, E.: Artificial Intelligence. McGraw-Hill, 1983 1.1, 1 Ritter, H./Martinez, T./Schulten, K.: Neuronale Netze. Addison Wesley, 1991 9.8 Rojas, R.: Theorie der neuronalen Netze. Springer, 1993 9.8 Rumelhart, D./McClelland, J.: Parallel Distributed Processing. Volume 1, MIT Press, 1986 (document), 3, 4, 5 Rumelhart, D.E./Hinton, G.E./R.J., Williams: Learning Internal Represen- tations by Error Propagation. in Rumelhart/McClelland, 1986 3, 5 Schramm, M.: Indifferenz, Unabh¨angigkeit und maximale Entropie: Eine wahrscheinlichkeitstheoretische Semantik fu¨r Nicht-Monotones Schließen. Mu¨nchen: CS-Press, 1996, Dissertationen zur Informatik 4 5 Copyright'2011,W.Ertel 10 Schulz, S.: E – A Brainiac Theorem Prover. Journal of AI Communications, 15 2002, Nr. 2/3, 111–126, www4.informatik.tu-muenchen.de/\protect\ unhbox\voidb@x\penalty\@M\{}schulz/WORK/eprover.html 3.6, 3.7 Schumann, J.: Automated Theorem Proving in Software Engineering. Springer Verlag, 2001 3.8 Sch¨olkopf, S./Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002 9.6 Sejnowski, T.J./Rosenberg, C.R.: NETtalk: a parallel network that learns to read aloud. The John Hopkins University Electrical Engineering and Com- puter Science Technical Report, 1986 (JHU/EECS-86/01). – Technical report, Wiederabdruck in Anderson/Rosenfeld S. 661-672 7 Siekmann, J./Benzmu¨ller, Ch.: Omega: Computer Supported Mathemat- ics. In KI 2004: Advances in Artificial Intelligence. Springer Verlag, 2004, LNAI 3238, www.ags.uni-sb.de/\protect\unhbox\voidb@x\penalty\ @M\{}omega, 3–28 4.1

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learning. Discrete Event Systems, Special issue on reinforcement learning, 13. 2003, 41–77 10.1 In Siciliano, B./. Khatib, O., editors: . 460, Deutsche¨Ubersetzung mit dem Titel Kann eine Maschine denken in. Zimmerli/Wolf 11.
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