Institute for Web Science and Technologies · Universität Koblenz
Institute WeST
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Seminar Formale Argumentation

[zur Übersicht]

Wintersemester 2017 / 2018

Das Forschungsgebiet Wissensrepräsentation ist ein Teilgebiet der Künstlichen Intelligenz und beschäftigt sich mit der logischen Formalisierung von Informationen und Schlussfolgerungsprozessen. Ein relativ neuer und vielversprechender Ansatz zur Wissensrepräsentation sind formale Modelle der Argumentation, die auf die Repräsentation von Argumenten, d. h. plausible Schlussfolgerungsketten für bestimmte Sachverhalte, und deren Interaktion abzielen. Formale Modelle der Argumentation sind angelehnt an das menschliche Schlussfolgerungsverhalten und sind analytisch interessante und vielstudierte Ansätze in der Künstlichen Intelligenz.

Dieses Seminar knüpft an das Kapitel "Formale Argumentation" aus der Veranstaltung "Künstliche Intelligenz 1" an. Neben den schon dort vorgestellten abstrakten Argumentationssystemen werden weitere aktuelle Forschungsthemen diskutiert, wie strukturierte Argumentation, algorithmische Fragestellungen, und Argumentation unter Unsicherheit. 

Seminarthemen

  1. Abstrakte Argumentation [18, 8, 14]
  2. Komplexität Abstrakter Argumentation [19, 20]
  3. Algorithmen für Abstrakte Argumentation [15, 40, 33]
  4. Ordinale Semantiken für Abstrakte Argumentation [1, 2, 12]
  5. Bipolare Argumentation [3, 16, 25]
  6. Regelbasierte Argumentation [5, 6, 7]
  7. Deduktive Argumentation [9, 10, 11]
  8. Abstrakte Dialektische Systeme [13, 38, 35]
  9. Fuzzy Argumentation [30, 17, 41]
  10. Probabilistische Argumentation [21, 28, 29]
  11. Der Equational Approach zur Argumentation [22, 23, 24]
  12. Strategische Argumentation [26, 39, 27]
  13. Argumentatives Maschinelles Lernen [4, 32, 36]
  14. Argument Mining [31, 37, 34]

Literatur

  1. Leila Amgoud and Jonathan Ben-Naim. Ranking-based semantics for argumentation frameworks. In Weiru Liu, V S Subrahmanian, and Jef Wijsen, editors, Proceedings of the 7th International Conference on Scalable Uncertainty Management (SUM 2013), volume 8078 of Lecture Notes In Artificial Intelligence, Washington, DC, USA, September 2013.
  2. Leila Amgoud and Jonathan Ben-Naim. Argumentation-based ranking logics. In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015.
  3. Leila Amgoud, Claudette Cayrol, Marie-Christine Lagasquie-Schiex, and P. Livet. On Bipolarity in Argumentation Frameworks. International Journal of Intelligent Systems, 23(10):1062– 1093, 2008.
  4. Leila Amgoud and Mathieu Serrurier. An argumentation framework for concept learning. In Proceedings Of the 11th International Workshop on Non-Monotonic Reasoning, NMR’06, Lake District, UK, May 2006.
  5. Ofer Arieli and Christian Straßer. Dynamic derivations for sequent-based logical argumentation. In Computational Models of Argument - Proceedings of COMMA 2014, Atholl Palace Hotel, Scottish Highlands, UK, September 9-12, 2014, pages 89–100, 2014.
  6. Ofer Arieli and Christian Straßer. Sequent-based logical argumentation. Argument & Computation, 6(1):73–99, 2015.
  7. Ofer Arieli and Christian Straßer. Deductive argumentation by enhanced sequent calculi and dynamic derivations. Electr. Notes Theor. Comput. Sci., 323:21–37, 2016.
  8. Pietro Baroni, Martin Caminada, and Massimiliano Giacomin. An introduction to argumentation semantics. The Knowledge Engineering Review, 26(4):365–410, 2011.
  9. P. Besnard and A. Hunter. A logic-based theory of deductive arguments. Artificial Intelligence, 128(1-2):203–235, 2001.
  10. P. Besnard and A. Hunter. Knowledgebase compilation for efficient logical argumentation. In Proceedings of the 10th International Conference on Knowledge Representation (KR’06), pages 123–133. AAAI Press, 2006.
  11. Philippe Besnard, Anthony Hunter, and Stefan Woltran. Encoding Deductive Argumentation in Quantified Boolean Formulae. Artificial Intelligence, 173(15):1406–1423, August 2009.
  12. Elise Bonzon, Jerome Delobelle, Sebastien Konieczny, and Nicolas Maudet. Argumentation ranking semantics based on propagation. In Proceedings of the 6th International Conference on Computational Models of Argument (COMMA-2016), 2016.
  13. Gerhard Brewka, Stefan Ellmauthaler, Hannes Strass, Johannes Peter Wallner, and Stefan Woltran. Abstract dialectical frameworks revisited. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI’13), 2013.
  14. Gerhard Brewka, Sylwia Polberg, and Stefan Woltran. Generalizations of dung frameworks and their role in formal argumentation. IEEE Intelligent Systems, 29(1), 2014.
  15. Günther Charwat, Wolfgang Dvorak, Sarah Alice Gaggl, Johannes Peter Wallner, and Stefan Woltran. Methods for solving reasoning problems in abstract argumentation - a survey. Artificial Intelligence, 220:28–63, 2015.
  16. Andrea Cohen, Sebastian Gottifredi, Alejandro Javier Garc ́ıa, and Guillermo Ricardo Simari. A survey of different approaches to support in argumentation systems. Knowledge Eng. Review, 29(5):513–550, 2014.
  17. Celia da Costa Pereira, Mauro Dragoni, Andrea G.B. Tettamanzi, and Serena Villata. Fuzzy labeling for abstract argumentation: An empirical evaluation. In Proceedings of the 10th International Conference on Scalable Uncertainty Management (SUM’16), 2016.
  18. Phan Minh Dung. On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and n-Person Games. Artificial Intelligence, 77(2):321– 358, 1995.
  19. Paul E. Dunne. The computational complexity of ideal semantics. Artificial Intelligence, 173(18):1559–1591, December 2009.
  20. Wolfgang Dvorak. Computational Aspects of Abstract Argumentation. PhD thesis, Technische Universität Wien, 2012.
  21. Bettina Fazzinga, Sergio Flesca, and Francesco Parisi. On the complexity of probabilistic abstract argumentation. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI’13), 2013.
  22. Dov Gabbay. Equational approach to argumentation networks. Argument and Computation, 3(2–3):87–142, 2012.
  23. Dov Gabbay and Odinaldo Rodrigues. Probabilistic argumentation: An equational approach. Logica Universalis, 9(3):345–382, 2015.
  24. Dov Gabbay and Odinaldo Rodrigues. Degrees of ̈ın”, o ̈ut ̈and u ̈ndecided ̈ın argumentation networks. In Proceedings of the 6th International Conference on Computational Models of Argument (COMMA’16), 2016.
  25. Dov M. Gabbay. Logical foundations for bipolar and tripolar argumentation networks: preliminary results. Journal of Logic and Computation, 26(1), 2016.
  26. Guido Governatori, Francesco Olivieri, Simone Scannapieco, Antonino Rotolo, and Matteo Cristani. Strategic argumentation is np-complete. CoRR, abs/1312.4287, 2013.
  27. Emmanuel Hadoux and Anthony Hunter. Strategic sequences of arguments for persuasion using decision trees. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17), 2017.
  28. Anthony Hunter. Probabilistic qualification of attack in abstract argumentation. International Journal of Approximate Reasoning, 55(2):607–638, 2014.
  29. Anthony Hunter and Matthias Thimm. Optimization of dialectical outcomes in dialogical argumentation. International Journal of Approximate Reasoning, 78:73–102, July 2016.
  30. J. Janssen, M. D. Cock, and D. Vermeir. Fuzzy argumentation frameworks. In Procedings of the 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU’08), pages 513–520, 2008.
  31. Marco Lippi and Paolo Torroni. Argumentation mining: State of the art and emerging trends. ACM Trans. Internet Technol., 16(2):10:1–10:25, March 2016.
  32. Martin Mozina, Jure Zabkar, and Ivan Bratko. Argument based machine learning. Artificial Intelligence, 171:922–937, 2007.
  33. Samer Nofal, Katie Atkinson, and Paul E. Dunne. Looking-ahead in backtracking algorithms for abstract argumentation. International Journal of Approximate Reasoning, 78, 2016.
  34. Andreas Peldszus and Manfred Stede. From argument diagrams to argumentation mining in texts: A survey. Int. J. Cogn. Inform. Nat. Intell., 7(1):1–31, January 2013.
  35. Sylwia Polberg. Understanding the abstract dialectical framework. In Proceedings of the 15th European Conference on Logics in Artificial Intelligence (JELIA’16), 2016.
  36. Regis Riveret and Guido Governatori. On learning attacks in probabilistic abstract argumentation. In Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016), 2016.
  37. Patrick Saint-Dizier. Argument mining: the bottleneck of knowledge and language resources. In Nicoletta Calzolari (Conference Chair), Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, and Stelios Piperidis, editors, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Paris, France, may 2016. European Language Resources Association (ELRA).
  38. Hannes Strass and Johannes Peter Wallner. Analyzing the computational complexity of abstract dialectical frameworks via approximation fixpoint theory. In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR’14), 2014.
  39. Matthias Thimm. Strategic argumentation in multi-agent systems. Künstliche Intelligenz, Special Issue on Multi-Agent Decision Making, 28(3):159–168, June 2014.
  40. Matthias Thimm and Serena Villata, editors. System Descriptions of the First International Competition on Computational Models of Argumentation (ICCMA’15), October 2015. arXiv:1510.05373.
  41. Jiachao Wu, Hengfei Li, Nir Oren, and Timothy J. Norman. Gödel fuzzy argumentation frameworks. In Proceedings of the 6th International Conference on Computational Models of Argument (COMMA’16), 2016.

Termine

Vorbesprechung: 18. Juli, 2017 16:15 (Raum E.428)

Seminartermin(e): TBA

Teilnahme

Um an dem Seminar teilzunehmen ist eine Teilnahme an der Vorbesprechung am 18.07.2017 16:15 (Raum E.428) erforderlich. Wenn Sie Interesse haben an dem Seminar teilzunehmen, kündigen Sie dies bitte vorab in einer informellen E-Mail an Matthias Thimm an.

Von den Teilnehmern wird eine Präsentation (ca. 30 Minuten) über eines der oben genannten Themen erwartet. Im Anschluss an das Seminar ist eine Ausarbeitung über das Seminarthema (ca. 12 Seiten) abzugeben.

Bitte beachten Sie bei der Erstellung der Präsentation und der Ausarbeitung folgende allgemeine Richtlinien (nur in Englisch verfügbar): pdf

Lehrende

Prof. Dr. Matthias Thimm
  • thimm@uni-koblenz.de
  • Alumnus
  • B 108
  • +49 261 287-2715