Dieses Bild zeigt Nico Potyka

Herr Dr.

Nico Potyka

Wissenschaftlicher Mitarbeiter
IPVS
Analytic Computing

Kontakt

+49 711 685-88106
+49 711 685-78106

Universitätsstraße 32
70569 Stuttgart
Deutschland
Raum: 02.303a

Fachgebiet

I am a postdoc at Professor Staab's Analytic Computing group, leading the focus group AI and Knowledge Graphs. My general research interest is in scalable and interpretable approaches to Artificial Intelligence. I am particularly interested in

  • Abstract Argumentation
  • Adversarial Machine Learning
  • Algorithms and Complexity
  • Applications in Multiagent Systems
  • Applications of Combinatorial and Numerical Optimization
  • Applications of Dynamical Systems
  • Automated Decision Making and Decision Support
  • Explainable AI
  • Information Extraction
  • Interpretable Machine Learning
  • Knowledge Graphs
  • Probabilistic Reasoning
  • Reasoning with Inconsistent Information
  1. 2021

    1. N. Potyka, „Interpreting Neural Networks as Gradual Argumentation Frameworks“, in AAAI Conference on Artificial Intelligence (AAAI), 2021, S. to appear. [Online]. Verfügbar unter: https://www.researchgate.net/publication/346933696_Interpreting_Neural_Networks_as_Gradual_Argumentation_Frameworks_Including_Proof_Appendix
  2. 2020

    1. I. Ibs und N. Potyka, „Explainable Automated Reasoning in Law using Probabilistic Epistemic Argumentation“, Workshop on Models of Legal Reasoning (MLR). 2020. [Online]. Verfügbar unter: https://www.researchgate.net/publication/344218713_Explainable_Automated_Reasoning_in_Law_using_Probabilistic_Epistemic_Argumentation
    2. N. Potyka, „Abstract Argumentation with Markov Networks“, in European Conference on Artificial Intelligence (ECAI), 2020, S. 865–872. [Online]. Verfügbar unter: https://www.researchgate.net/publication/338886145_Abstract_Argumentation_with_Markov_Networks
    3. N. Potyka, „Bipolar Abstract Argumentation with Dual Attacks and Supports.“, in International Conference on Principles of Knowledge Representation and Reasoning (KR), 2020, S. 677–686. [Online]. Verfügbar unter: https://www.researchgate.net/publication/342571778_Bipolar_Abstract_Argumentation_with_Dual_Attacks_and_Supports
    4. N. Potyka, „Foundations for Solving Classification Problems with Quantitative Abstract Argumentation“, International Workshop on Explainable and Interpretable Machine Learning (XI-ML). 2020. [Online]. Verfügbar unter: https://www.researchgate.net/publication/344739018_Foundations_for_Solving_Classification_Problems_with_Quantitative_Abstract_Argumentation
  3. 2019

    1. A. Hunter, S. Polberg, und N. Potyka, „Delegated updates in epistemic graphs for opponent modelling“, International Journal of Approximate Reasoning, Bd. 113, S. 207–244, 2019, doi: 10.1016/j.ijar.2019.07.006.
    2. N. Potyka, „A Polynomial-time Fragment of Epistemic Probabilistic Argumentation“, in International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), 2019, S. 2165–2167. [Online]. Verfügbar unter: http://dl.acm.org/citation.cfm?id=3332045
    3. N. Potyka, „A polynomial-time fragment of epistemic probabilistic argumentation“, International Journal of Approximate Reasoning, Bd. 115, S. 265–289, 2019, doi: 10.1016/j.ijar.2019.10.005.
    4. N. Potyka, „Extending Modular Semantics for Bipolar Weighted Argumentation“, in International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), 2019, S. 1722–1730. [Online]. Verfügbar unter: http://dl.acm.org/citation.cfm?id=3331903
    5. N. Potyka, „Extending Modular Semantics for Bipolar Weighted Argumentation (Extended Abstract)“, in German Conference on Artificial Intelligence (KI), 2019, Bd. 11793, S. 273–276. doi: 10.1007/978-3-030-30179-8\_23.
    6. N. Potyka, „Open-Mindedness of Gradual Argumentation Semantics“, in International Conference on Scalable Uncertainty Management (SUM), 2019, Bd. 11940, S. 236–249. doi: 10.1007/978-3-030-35514-2\_18.
    7. N. Potyka, S. Polberg, und A. Hunter, „Polynomial-Time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation“, in European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), 2019, Bd. 11726, S. 74–86. doi: 10.1007/978-3-030-29765-7\_7.
    8. N. Potyka, S. Polberg, und A. Hunter, „Polynomial-time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation (Technical Report)“, CoRR, Bd. abs/1906.05066, 2019, [Online]. Verfügbar unter: http://arxiv.org/abs/1906.05066
  4. 2018

    1. A. Hunter, S. Polberg, und N. Potyka, „Updating Belief in Arguments in Epistemic Graphs“, in International Conference on Principles of Knowledge Representation and Reasoning (KR), 2018, S. 138–147. [Online]. Verfügbar unter: https://aaai.org/ocs/index.php/KR/KR18/paper/view/17982
    2. N. Potyka, „A Polynomial-time Fragment of Epistemic Probabilistic Argumentation (Technical Report)“, CoRR, Bd. abs/1811.12083, 2018, [Online]. Verfügbar unter: http://arxiv.org/abs/1811.12083
    3. N. Potyka, „A Tutorial for Weighted Bipolar Argumentation with Continuous Dynamical Systems and the Java Library Attractor“, CoRR, Bd. abs/1811.12787, 2018, [Online]. Verfügbar unter: http://arxiv.org/abs/1811.12787
    4. N. Potyka, „Continuous Dynamical Systems for Weighted Bipolar Argumentation“, in International Conference on Principles of Knowledge Representation and Reasoning (KR), 2018, S. 148–157. [Online]. Verfügbar unter: https://aaai.org/ocs/index.php/KR/KR18/paper/view/17985
    5. N. Potyka, „Convergence and Open-Mindedness of Discrete and Continuous Semantics for Bipolar Weighted Argumentation (Technical Report)“, CoRR, Bd. abs/1809.07133, 2018, [Online]. Verfügbar unter: http://arxiv.org/abs/1809.07133
    6. N. Potyka, „Measuring Disagreement Among Knowledge Bases“, in International Conference on Scalable Uncertainty Management (SUM), 2018, Bd. 11142, S. 212--227. doi: 10.1007/978-3-030-00461-3\_15.
    7. N. Potyka, „Measuring Disagreement among Knowledge Bases“, in Workshop on Logics for Reasoning about Preferences, Uncertainty, and Vagueness (PRUV), 2018, Bd. 2157. [Online]. Verfügbar unter: http://ceur-ws.org/Vol-2157/paper3.pdf
  5. 2017

    1. C. Beierle, M. Finthammer, N. Potyka, J. Varghese, und G. Kern-Isberner, „A Framework for Versatile Knowledge and Belief Management Operations in a Probabilistic Conditional Logic“, FLAP, Bd. 4, Nr. 7, Art. Nr. 7, 2017, [Online]. Verfügbar unter: http://www.collegepublications.co.uk/downloads/ifcolog00016.pdf
    2. A. Hunter und N. Potyka, „Updating Probabilistic Epistemic States in Persuasion Dialogues“, in European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), 2017, Bd. 10369, S. 46–56. doi: 10.1007/978-3-319-61581-3\_5.
    3. R. Peñaloza und N. Potyka, „Towards Statistical Reasoning in Description Logics over Finite Domains“, in International Conference on Scalable Uncertainty Management (SUM), 2017, Bd. 10564, S. 280–294. doi: 10.1007/978-3-319-67582-4\_20.
    4. R. Peñaloza und N. Potyka, „Towards Statistical Reasoning in Description Logics over Finite Domains (Full Version)“, CoRR, Bd. abs/1706.03207, 2017, [Online]. Verfügbar unter: http://arxiv.org/abs/1706.03207
    5. N. Potyka und M. Thimm, „Inconsistency-tolerant reasoning over linear probabilistic knowledge bases“, International Journal of Approximate Reasoning, Bd. 88, S. 209–236, 2017, doi: 10.1016/j.ijar.2017.06.002.
  6. 2016

    1. R. Peñaloza und N. Potyka, „Probabilistic Reasoning in the Description Logic ALCP with the Principle of Maximum Entropy“, in International Conference on Scalable Uncertainty Management (SUM), 2016, Bd. 9858, S. 246–259. doi: 10.1007/978-3-319-45856-4\_17.
    2. R. Peñaloza und N. Potyka, „Probabilistic Reasoning in the Description Logic ALCP with the Principle of Maximum Entropy (Full Version)“, CoRR, Bd. abs/1606.09521, 2016, [Online]. Verfügbar unter: http://arxiv.org/abs/1606.09521
    3. N. Potyka, E. Mittermeier, und D. Marenke, „An overview of algorithmic approaches to compute optimum entropy distributions in the expert system shell MECore (extended version)“, Journal of Applied Logic, Bd. 19, S. 71–86, 2016, doi: 10.1016/j.jal.2016.05.003.
    4. N. Potyka, E. Acar, M. Thimm, und H. Stuckenschmidt, „Group Decision Making via Probabilistic Belief Merging“, in International Joint Conference on Artificial Intelligence (IJCAI), 2016, S. 3623–3629. [Online]. Verfügbar unter: http://www.ijcai.org/Abstract/16/510
    5. N. Potyka, „Relationships Between Semantics for Relational Probabilistic Conditional Logics“, in Computational Models of Rationality, Essays dedicated to Gabriele Kern-Isberner on the occasion of her 60th birthday, 2016, S. 332–347.
    6. N. Potyka, „Solving Reasoning Problems for Probabilistic Conditional Logics with Consistent and Inconsistent Information“, FernUniversität in Hagen, 2016. [Online]. Verfügbar unter: https://nbn-resolving.org/urn:nbn:de:hbz:708-30620
    7. N. Potyka, D. Gómez-Ramirez, und K.-U. Kühnberger, „Towards a Computational Framework for Function-Driven Concept Invention“, in International Conference on Artificial General Intelligence (AGI), 2016, Bd. 9782, S. 212–222. doi: 10.1007/978-3-319-41649-6\_21.
  7. 2015

    1. C. Beierle, F. Brons, und N. Potyka, „A Software System Using a SAT Solver for Reasoning Under Complete, Stable, Preferred, and Grounded Argumentation Semantics“, in German Conference on Artificial Intelligence (KI), 2015, Bd. 9324, S. 241–248. doi: 10.1007/978-3-319-24489-1\_19.
    2. C. Beierle, G. Kern-Isberner, M. Finthammer, und N. Potyka, „Extending and Completing Probabilistic Knowledge and Beliefs Without Bias“, Künstliche Intelligenz (KI), Bd. 29, Nr. 3, Art. Nr. 3, 2015, doi: 10.1007/s13218-015-0380-1.
    3. C. Beierle, N. Potyka, J. Baudisch, und M. Finthammer, „Towards Lifted Inference Under Maximum Entropy for Probabilistic Relational FO-PCL Knowledge Bases“, in European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), 2015, Bd. 9161, S. 506--516. doi: 10.1007/978-3-319-20807-7\_46.
    4. N. Potyka, C. Beierle, und G. Kern-Isberner, „A concept for the evolution of relational probabilistic belief states and the computation of their changes under optimum entropy semantics“, Journal of Applied Logic, Bd. 13, Nr. 4, Art. Nr. 4, 2015, doi: 10.1016/j.jal.2015.01.001.
    5. N. Potyka und M. Thimm, „Probabilistic Reasoning with Inconsistent Beliefs Using Inconsistency Measures“, in International Joint Conference on Artificial Intelligence (IJCAI), 2015, S. 3156–3163. [Online]. Verfügbar unter: http://ijcai.org/Abstract/15/445
    6. N. Potyka, „Reasoning over Linear Probabilistic Knowledge Bases with Priorities“, in International Conference on Scalable Uncertainty Management (SUM), 2015, Bd. 9310, S. 121–136. doi: 10.1007/978-3-319-23540-0\_9.
  8. 2014

    1. N. Potyka und M. Thimm, „Consolidation of Probabilistic Knowledge Bases by Inconsistency Minimization“, in European Conference on Artificial Intelligence (ECAI), 2014, Bd. 263, S. 729–734. doi: 10.3233/978-1-61499-419-0-729.
    2. N. Potyka, „Linear Programs for Measuring Inconsistency in Probabilistic Logics“, in International Conference on Principles of Knowledge Representation and Reasoning (KR), 2014. [Online]. Verfügbar unter: http://www.aaai.org/ocs/index.php/KR/KR14/paper/view/7963
  9. 2013

    1. C. Beierle, M. Finthammer, N. Potyka, J. Varghese, und G. Kern-Isberner, „A Case Study on the Application of Probabilistic Conditional Modelling and Reasoning to Clinical Patient Data in Neurosurgery“, in European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), 2013, Bd. 7958, S. 49–60. doi: 10.1007/978-3-642-39091-3\_5.
    2. N. Potyka, C. Beierle, und G. Kern-Isberner, „Changes of Relational Probabilistic Belief States and Their Computation under Optimum Entropy Semantics“, in German Conference on Artificial Intelligence (KI), 2013, Bd. 8077, S. 176–187. doi: 10.1007/978-3-642-40942-4\_16.
    3. N. Potyka, C. Beierle, und G. Kern-Isberner, „On the Problem of Reversing Relational Inductive Knowledge Representation“, in European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), 2013, Bd. 7958, S. 485–496. doi: 10.1007/978-3-642-39091-3\_41.
    4. N. Potyka, „Some Notes on the Factorization of Probabilistic Logical Models under Maximum Entropy Semantics“, in Florida Artificial Intelligence Research Society Conference (FLAIRS), 2013. [Online]. Verfügbar unter: http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS13/paper/view/5910
    5. J. Varghese, C. Beierle, N. Potyka, und G. Kern-Isberner, „Using probabilistic logic and the principle of maximum entropy for the analysis of clinical brain tumor data“, in IEEE International Symposium on Computer-Based Medical Systems (CBMS), 2013, S. 401–404. doi: 10.1109/CBMS.2013.6627826.
  10. 2012

    1. N. Potyka und C. Beierle, „An Approach to Learning Relational Probabilistic FO-PCL Knowledge Bases“, in International Conference on Scalable Uncertainty Management (SUM), 2012, Bd. 7520, S. 625–632. doi: 10.1007/978-3-642-33362-0\_52.
    2. N. Potyka, „Towards a General Framework for Maximum Entropy Reasoning“, in Florida Artificial Intelligence Research Society Conference (FLAIRS), 2012. [Online]. Verfügbar unter: http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS12/paper/view/4379

University of Stuttgart

  • Knowledge Graphs (SS 21)
  • Introduction to Artificial Intelligence (WS 20)
  • Commonsense Reasoning (SS 20)
  • Wissensgraphen (SS 20)

University of Osnabrück

  • Introduction to Artificial Intelligence and Logic Programming (SS 17, 18, 19)
  • Methods of Artificial Intelligence (WS 16, 17, 18, 19)
  • Basic Methods of Probabilistic Reasoning (SS 16, 17, 18, WS 19)
  • Rational Reasoning in MultiAgent Systems (WS 16, 17, 18)
  • Selected Topics in Nature-inspired Algorithms (WS 17, SS 18, 19)
  • Time Series Analysis and Forecasting (WS 19)
  • Selected Topics in Constraint Programming (SS 17)
  • Conceptual Spaces - Applications and Learning (SS 17)
  • Study Project: Legal expert systems for technical domains (SS 17, WS 17)

Short CV

Research Visits

  • University of Dresden, Automata Theory (4/2019)
  • University College London, Artificial Intelligence (03/2018)
  • University College London, Artificial Intelligence (02/2017)
  • University of Mannheim, Artificial Intelligence (12/2015)
  • University of Bolzano, KRDB Research Center for Knowledge and Data (11/2015)

Invited Talks

  • University of Koblenz: Weighted Argumentation for Web Science (2019)
  • University of Dresden: Probabilistic Reasoning with Conflicting Information (2019)
  • University of Mannheim: Probabilistic Reasoning with Consistent and Inconsistent Information (2015)

Tutorials

  • German Conference on Artificial Intelligence (KI 2020): Explainable and Computationally Efficient Decision Making with Quantitative Abstract Argumentation Frameworks
  • German Conference on Artificial Intelligence (KI 2019): Modeling and Solving Weighted Argumentation Problems
  • Workshop on Ontologies, Uncertainty, and Inconsistency Handling 2018: Knowledge Representation and Reasoning with Nilsson-style Probabilistic Logics
  • German Conference on Artificial Intelligence (KI 2017): Knowledge Representation and Reasoning with Nilsson-style Probabilistic Logics

Awards

Selected Publications

  • Attractor: Java library for weighted abstract argumentation with (continuous) dynamical systems.
  • Log4KR: Java library for (probabilistic) logical reasoning (part of KReator project).
  • ProBabble: Java library for probabilistic abstract argumentation in polynomial time.
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