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Seminar Formal Argumentation

Seminar Winter Semester 2018/19 

Dr. Tjitze Rienstra

Institute for Web Science and Technologies, University of Koblenz-Landau 

Overview 

The research field Knowledge Representation is a branch of Artificial Intelligence that deals with the logical formalization of information and inference processes. Formal models of argumentation are a relatively new and promising approach to knowledge representation. This approach is based on the representation of arguments (i.e., plausible chains of inference for specific conclusions) and interaction between such arguments. Formal models of argumentation are based on human every-day reasoning. They are analytically appealing and therefore form a popular approach to model commonsense reasoning in artificial intelligence. 

This seminar continues the topic “formal argumentation” from the course Artificial Intelligence 1 In addition to the abstract reasoning systems already presented, further current research top- ics are discussed, such as structured argumentation, algorithmic questions, and reasoning under uncertainty. 

Topics 

  1. Abstract Argumentation [18, 8, 14]
  2. 
Complexity of Abstract Argumentation [19, 20]

  3. Algorithms for Abstract Argumentation [15, 40, 33]

  4. Ordinal Semantics for Abstract Argumentation [1, 2, 12]
  5. Bipolar Argumentation [3, 16, 25]
  6. 
Rule-based Argumentation [5, 6, 7]

  7. Deductive Argumentation [9, 10, 11]

  8. Abstract Dialectical Systems [13, 38, 35]
  9. 
Fuzzy Argumentation [30, 17, 41]

  10. Probabilistic Argumentation [21, 28, 29]
  11. 
The Equational Approach to Argumentation [22, 23, 24]
  12. Strategic Argumentation [26, 39, 27]

  13. Argumentative Machine Learning [4, 32, 36]
  14. 
Argument Mining [31, 37, 34] 

Participation 

Introductory Meeting: 07.08.2018, 10:00-11:00 (Room B016) 

To participate in this seminar you must attend the introductory meeting (07.08.2018, 10:00-11:00, Room B016). If you are interested in participating, please send a short message to Tjitze Rienstra. The participants of this seminar are expected to give a presentation (about 30 minutes) on one of the topics mentioned above. After the seminar, participants are expected to write a paper (about 12 pages) on the topic of the seminar. Selection and assignment of topics will be done following the introductory meeting. 

References 

    .    [1]  Leila Amgoud and Jonathan Ben-Naim. Ranking-based semantics for argumentation frame- works. 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 Proceed- ings 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 Bipolar- ity 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 argumenta- tion. 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 & Com- putation, 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 argumen- tation 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]  Gu ̈nther Charwat, Wolfgang Dvoˇr ́ak, 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 Nonmono- tonic 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 Dvor ́ak. Computational Aspects of Abstract Argumentation. PhD thesis, Technis- che Universit ̈at 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 & 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 ”in”, ”out” and ”undecided” in argumenta- tion 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: pre- liminary 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, 78:265–282, 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 argumen- tation. 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 Lan- guage 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. Ku ̈nstliche 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 Interna- tional Competition on Computational Models of Argumentation (ICCMA’15), October 2015. arXiv:1510.05373. 

    .    [41]  Jiachao Wu, Hengfei Li, Nir Oren, and Timothy J. Norman. G ̈odel fuzzy argumentation frameworks. In Proceedings of the 6th International Conference on Computational Models of Argument (COMMA’16), 2016. 


Beteiligte: 

Dr. Tjitze Rienstra

rienstra@uni-koblenz.de