Wissenschaftliche Artikel

Fichte, J. K., Le Berre, D., Hecher, M., & Szeider, S. (2023). The silent (r)evolution of SAT. Communications of the ACM, 66(6), 64–72. https://doi.org/10.1145/3560469 ( reposiTUm)
Fichte, J. K., Hecher, M., Morak, M., & Woltran, S. (2021). DynASP2.5: Dynamic Programming on Tree Decompositions in Action. Algorithms, 14(3), 81. https://doi.org/10.3390/a14030081 ( reposiTUm)
FICHTE, J. K., HECHER, M., THIER, P., & WOLTRAN, S. (2021). Exploiting Database Management Systems and Treewidth for Counting. Theory and Practice of Logic Programming, 22(1), 128–157. https://doi.org/10.1017/s147106842100003x ( reposiTUm)
Abseher, M., Bliem, B., Charwat, G., Dusberger, F., & Woltran, S. (2020). Computing Secure Sets in Graphs using Answer Set Programming. Journal of Logic and Computation, 30(4), 837–862. https://doi.org/10.1093/logcom/exv060 ( reposiTUm)
Gaggl, S. A., Linsbichler, T., Maratea, M., & Woltran, S. (2020). Design and results of the Second International Competition on Computational Models of Argumentation. Artificial Intelligence, 279(103193), 103193. https://doi.org/10.1016/j.artint.2019.103193 ( reposiTUm)
Dvořák, W., & Woltran, S. (2020). Complexity of abstract argumentation under a claim-centric view. Artificial Intelligence, 285(103290), 103290. https://doi.org/10.1016/j.artint.2020.103290 ( reposiTUm)
Bliem, B., Morak, M., Moldovan, M., & Woltran, S. (2020). The Impact of Treewidth on Grounding and Solving of Answer Set Programs. Artificial Intelligence, 67, 35–80. https://doi.org/10.1613/jair.1.11515 ( reposiTUm)
Kolaitis, P. G., Pichler, R., Sallinger, E., & Savenkov, V. (2020). On the Language of Nested Tuple Generating Dependencies. ACM Transactions on Database Systems, 45(2), 1–59. https://doi.org/10.1145/3369554 ( reposiTUm)
Kronegger, M., Ordyniak, S., & Pfandler, A. (2019). Backdoors to planning. Artificial Intelligence, 269, 49–75. https://doi.org/10.1016/j.artint.2018.10.002 ( reposiTUm)
Baumann, R., Dvorak, W., Linsbichler, T., & Woltran, S. (2019). A general notion of equivalence for abstract argumentation. Artificial Intelligence, 275, 379–410. https://doi.org/10.1016/j.artint.2019.06.006 ( reposiTUm)
Charwat, G., & Woltran, S. (2019). Expansion-based QBF Solving on Tree Decompositions. Fundamenta Informaticae, 167(1–2), 59–92. https://doi.org/10.3233/fi-2019-1810 ( reposiTUm)
Ganian, R., Kronegger, M., Pfandler, A., & Popa, A. (2019). Parameterized Complexity of Asynchronous Border Minimization. Algorithmica, 81(1), 201–223. https://doi.org/10.1007/s00453-018-0442-5 ( reposiTUm)
Woltran, S., & Schaub, T. (2018). Answer set programming unleashed! Kuenstliche Intelligenz, 32(2–3), 105–108. https://doi.org/10.1007/s13218-018-0550-z ( reposiTUm)
Abseher, M., Hecher, M., Moldovan, M., Woltran, S., & Bliem, B. (2018). Dynamic Programming on Tree Decompositions with {D-FLAT}. Kuenstliche Intelligenz, 32(2–3), 191–192. https://doi.org/10.1007/s13218-018-0531-2 ( reposiTUm)
Delgrande, J. P., Peppas, P., & Woltran, S. (2018). General Belief Revision. Journal of the ACM, 65(5), 1–34. https://doi.org/10.1145/3203409 ( reposiTUm)
Diller, M., Haret, A., Linsbichler, T., Rümmele, S., & Woltran, S. (2018). An extension-based approach to belief revision in abstract argumentation. International Journal of Approximate Reasoning, 93, 395–423. https://doi.org/10.1016/j.ijar.2017.11.013 ( reposiTUm)
Bliem, B., & Woltran, S. (2018). Complexity of Secure Sets. Algorithmica, 80(10), 2909–2940. https://doi.org/10.1007/s00453-017-0358-5 ( reposiTUm)
Bliem, B., & Woltran, S. (2018). Equivalence between answer-set programs under (partially) fixed input. Annals of Mathematics and Artificial Intelligence, 83(3–4), 277–295. https://doi.org/10.1007/s10472-017-9567-5 ( reposiTUm)
Bliem, B., & Woltran, S. (2018). Defensive alliances in graphs of bounded treewidth. Discrete Applied Mathematics, 251, 334–339. https://doi.org/10.1016/j.dam.2018.04.001 ( reposiTUm)
Creignou, N., Pichler, R., & Woltran, S. (2018). Do Hard SAT-Related Reasoning Tasks Become Easier in the Krom Fragment? Logical Methods in Computer Science, 14(4), 1–25. https://doi.org/10.23638/LMCS-14(4:10)2018 ( reposiTUm)
Maly, J., & Woltran, S. (2017). Ranking Specific Sets of Objects. Datenbank-Spektrum: Zeitschrift Für Datenbanktechnologien Und Information Retrieval, 17(3), 255–265. https://doi.org/10.1007/s13222-017-0264-7 ( reposiTUm)
Egly, U., Kronegger, M., Lonsing, F., & Pfandler, A. (2017). Conformant planning as a case study of incremental QBF solving. Annals of Mathematics and Artificial Intelligence, 80(1), 21–45. http://hdl.handle.net/20.500.12708/147186 ( reposiTUm)
Abseher, M., Musliu, N., & Woltran, S. (2017). Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning. Journal of Artificial Intelligence Research, 58, 829–858. https://doi.org/10.1613/jair.5312 ( reposiTUm)
GONÇALVES, R., KNORR, M., LEITE, J., & WOLTRAN, S. (2017). When you must forget: Beyond strong persistence when forgetting in answer set programming. Theory and Practice of Logic Programming, 17(5–6), 837–854. https://doi.org/10.1017/s1471068417000382 ( reposiTUm)
Bliem, B., Charwat, G., Hecher, M., & Woltran, S. (2016). D-FLAT^2: Subset Minimization in Dynamic Programming on Tree Decompositions Made Easy. Fundamenta Informaticae, 147(1), 27–61. http://hdl.handle.net/20.500.12708/149519 ( reposiTUm)
Abseher, M., Gebser, M., Musliu, N., Schaub, T., & Woltran, S. (2016). Shift Design with Answer Set Programming. Fundamenta Informaticae, 147(1), 1–25. http://hdl.handle.net/20.500.12708/149520 ( reposiTUm)
Bourhis, P., Manna, M., Morak, M., & Pieris, A. (2016). Guarded-Based Disjunctive Tuple-Generating Dependencies. ACM Transactions on Database Systems, 41(4), 1–45. https://doi.org/10.1145/2976736 ( reposiTUm)
BICHLER, M., MORAK, M., & WOLTRAN, S. (2016). The Power of Non-Ground Rules in Answer Set Programming. Theory and Practice of Logic Programming, 16(5–6), 552–569. https://doi.org/10.1017/s1471068416000338 ( reposiTUm)
Fichte, J. K., & Szeider, S. (2015). Backdoors to tractable answer-set programming. Artificial Intelligence, 220, 64–103. https://doi.org/10.1016/j.artint.2014.12.001 ( reposiTUm)
Fichte, J. K., & Szeider, S. (2015). Backdoors to Normality for Disjunctive Logic Programs. ACM Transactions on Computational Logic, 17(1), 1–23. https://doi.org/10.1145/2818646 ( reposiTUm)
FICHTE, J. K., TRUSZCZYŃSKI, M., & WOLTRAN, S. (2015). Dual-normal logic programs - the forgotten class. Theory and Practice of Logic Programming, 15(4–5), 495–510. https://doi.org/10.1017/s1471068415000186 ( reposiTUm)
GAGGL, S. A., MANTHEY, N., RONCA, A., WALLNER, J. P., & WOLTRAN, S. (2015). Improved answer-set programming encodings for abstract argumentation. Theory and Practice of Logic Programming, 15(4–5), 434–448. https://doi.org/10.1017/s1471068415000149 ( reposiTUm)
Charwat, G., Dvořák, W., Gaggl, S. A., Wallner, J. P., & Woltran, S. (2015). Methods for solving reasoning problems in abstract argumentation - A survey. Artificial Intelligence, 220, 28–63. https://doi.org/10.1016/j.artint.2014.11.008 ( reposiTUm)

Beiträge in Tagungsbänden

Bernreiter, M., Dvořák, W., Rapberger, A., & Woltran, S. (2023). The Effect of Preferences in Abstract Argumentation under a Claim-Centric View. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23) (pp. 6253–6261). AAAI Press. https://doi.org/10.1609/AAAI.V37I5.25770 ( reposiTUm)
Bernreiter, M., Maly, J., & Woltran, S. (2021). Choice Logics and Their Computational Properties. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. IJCAI 2021 - 30th International Joint Conference on Artificial Intelligence, Montreal, Canada, Canada. https://doi.org/10.24963/ijcai.2021/247 ( reposiTUm)
Dvořák, W., Ulbricht, M., & Woltran, S. (2021). Recursion in Abstract Argumentation is Hard  ---  On the Complexity of Semantics Based on Weak Admissibility. In 35th AAAI Conference on Artificial Intelligence (pp. 6288–6295). http://hdl.handle.net/20.500.12708/58530 ( reposiTUm)
Fichte, J., Hecher, M., & Meier, A. (2021). Knowledge-Base Degrees of Inconsistency: Complexity and Counting. In Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, {IAAI} 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2021, Virtual Event, February 2-9, 2021} (pp. 6349–6357). http://hdl.handle.net/20.500.12708/58550 ( reposiTUm)
Fandinno, J., & Hecher, M. (2021). Treewidth-Aware Complexity in {ASP:} Not all Positive Cycles are Equally Hard. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021 (pp. 6312–6320). http://hdl.handle.net/20.500.12708/58549 ( reposiTUm)
Fichte, J., Hecher, M., & Roland, V. (2021). Parallel Model Counting with CUDA: Algorithm Engineering for Efficient Hardware Utilization. In 27th International Conference on Principles and Practice of Constraint Programming, {CP} 2021, Montpellier, France (Virtual Conference), October 25-29, 2021 (pp. 24:1-24:20). https://doi.org/10.4230/LIPIcs.CP.2021.24 ( reposiTUm)
Fichte, J., Hecher, M., Mahmood, Y., & Meier, A. (2021). Decomposition-Guided Reductions for Argumentation and Treewidth. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. IJCAI 2021 - 30th International Joint Conference on Artificial Intelligence, Montreal, Canada, Canada. https://doi.org/10.24963/ijcai.2021/259 ( reposiTUm)
Fichte, J., Hecher, M., McCreesh, C., & Shahab, A. (2021). Complications for Computational Experiments from Modern Processors. In 27th International Conference on Principles and Practice of Constraint Programming, {CP} 2021, Montpellier, France (Virtual Conference), October 25-29, 2021 (pp. 25:1-25:21). https://doi.org/10.4230/LIPIcs.CP.2021.25 ( reposiTUm)
Dvořák, W., König, M., & Woltran, S. (2021). On the Complexity of Preferred Semantics in Argumentation Frameworks with Bounded Cycle Length. In Proceedings of the Eighteenth International Conference on Principles of Knowledge Representation and Reasoning. KR 2021 - 18th International Conference on Principles of Knowledge Representation and Reasoning, virtual event, Unknown. https://doi.org/10.24963/kr.2021/67 ( reposiTUm)
Eiter, T., Hecher, M., & Kiesel, R. (2021). Treewidth-Aware Cycle Breaking for Algebraic Answer Set Counting. In Proceedings of the 37th International Conference on Logic Programming (ICLP 2021). 37th International Conference on Logic Programming (ICLP 2021), Unknown. http://hdl.handle.net/20.500.12708/58728 ( reposiTUm)
Eiter, T., Hecher, M., & Kiesel, R. (2021). Treewidth-Aware Cycle Breaking for Algebraic Answer Set Counting. In Proceedings of the Eighteenth International Conference on Principles of Knowledge Representation and Reasoning. KR 2021 - 18th International Conference on Principles of Knowledge Representation and Reasoning, virtual event, Unknown. https://doi.org/10.24963/kr.2021/26 ( reposiTUm)
Dvořák, W., König, M., & Woltran, S. (2021). Graph-Classes of Argumentation Frameworks with Collective Attacks. In Logics in Artificial Intelligence (pp. 3–17). https://doi.org/10.1007/978-3-030-75775-5_1 ( reposiTUm)
Fichte, J. K., Hecher, M., & Kieler, M. F. I. (2020). Treewidth-Aware Quantifier Elimination and Expansion for QCSP. In Principles and Practice of Constraint Programming 26th International Conference, CP 2020, Louvain-la-Neuve, Belgium, September 7–11, 2020, Proceedings (pp. 248–266). https://doi.org/10.1007/978-3-030-58475-7_15 ( reposiTUm)
Fichte, J. K., Hecher, M., & Pfandler, A. (2020). Lower Bounds for QBFs of Bounded Treewidth. In Proceedings of the 35th Annual ACM/IEEE Symposium on Logic in Computer Science. LICS 2020, Saarbrücken, Germany. https://doi.org/10.1145/3373718.3394756 ( reposiTUm)
Fichte, J. K., Hecher, M., & Szeider, S. (2020). A Time Leap Challenge for SAT-Solving. In Principles and Practice of Constraint Programming 26th International Conference, CP 2020, Louvain-la-Neuve, Belgium, September 7–11, 2020, Proceedings (pp. 267–285). https://doi.org/10.1007/978-3-030-58475-7_16 ( reposiTUm)
Fichte, J. K., Hecher, M., & Szeider, S. (2020). Breaking Symmetries with RootClique and LexTopSort. In Principles and Practice of Constraint Programming 26th International Conference, CP 2020, Louvain-la-Neuve, Belgium, September 7–11, 2020, Proceedings (pp. 286–303). https://doi.org/10.1007/978-3-030-58475-7_17 ( reposiTUm)
Hecher, M. (2020). Treewidth-aware Reductions of Normal ASP to SAT - Is Normal ASP Harder than SAT after All? In Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning. KR 2020 - 17th International Conference on Principles of Knowledge Representation and Reasoning, Rhodos, online, Greece. https://doi.org/10.24963/kr.2020/49 ( reposiTUm)
Saribatur, Z. G., Wallner, J. P., & Woltran, S. (2020). Explaining Non-Acceptability in Abstract Argumentation. In G. De Giacomo (Ed.), Proceedings ECAI (pp. 881–888). IOS Press. http://hdl.handle.net/20.500.12708/58147 ( reposiTUm)
Hecher, M., Morak, M., & Woltran, S. (2020). Structural Decompositions of Epistemic Logic Programs. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 2830–2837). https://doi.org/10.1609/aaai.v34i03.5672 ( reposiTUm)
Hecher, M., Morak, M., & Woltran, S. (2020). Structural Decompositions of Epistemic Logic Programs. In International Conference on Logic Programming 2020 Workshop Proceedings. ICLP 2020, Rende, Italy. CEUR-WS.org. http://hdl.handle.net/20.500.12708/58264 ( reposiTUm)
FICHTE, J. K., HECHER, M., THIER, P., & WOLTRAN, S. (2020). Exploiting Database Management Systems and Treewidth for Counting. In Theory and Practice of Logic Programming (pp. 128–157). https://doi.org/10.1017/s147106842100003x ( reposiTUm)
Gonçalves, R., Janhunen, T., Knorr, M., Leite, J., & Woltran, S. (2020). Forgetting in Modular Answer Set Programming. In Proceedings of the 18th INTERNATIONAL WORKSHOP ON NON-MONOTONIC REASONING (pp. 189–197). http://hdl.handle.net/20.500.12708/58278 ( reposiTUm)
Hecher, M., Thier, P., & Woltran, S. (2020). Taming High Treewidth with Abstraction, Nested Dynamic Programming, and Database Technology. In Theory and Applications of Satisfiability Testing – SAT 2020 (pp. 343–360). https://doi.org/10.1007/978-3-030-51825-7_25 ( reposiTUm)
Bernreiter, M., Maly, J., & Woltran, S. (2020). Encoding Choice Logics in ASP. In International Conference on Logic Programming 2020 Workshop Proceedings co-located with 36th International Conference on Logic Programming (ICLP 2020), Rende, Italy, September 18-19, 2020 (pp. 1–14). http://hdl.handle.net/20.500.12708/58290 ( reposiTUm)
Maly, J. (2020). Lifting Preferences over Alternatives to Preferences over Sets of Alternatives: The Complexity of Recognizing Desirable Families of Sets. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 2152–2159). https://doi.org/10.1609/aaai.v34i02.5590 ( reposiTUm)
Woltran, S. (2020). Computational Argumentation - Formal Models and Complexity Results. In Proceedings of the 35th Italian Conference on Computational Logic - {CILC} 2020, Rende, Italy, October 13-15, 2020 (pp. 1–2). http://hdl.handle.net/20.500.12708/55556 ( reposiTUm)
Fandinno, J., & Hecher, M. (2020). Treewidth-Aware Complexity in ASP:Not all Positive Cycles are Equally Hard. In 18th INTERNATIONAL WORKSHOP ONNON-MONOTONIC REASONING (pp. 48–57). http://hdl.handle.net/20.500.12708/55559 ( reposiTUm)
Fichte, J., & Hecher, M. (2020). Counting with Bounded Treewidth: Meta Algorithm and Runtime Guarantees. In 18th INTERNATIONAL WORKSHOP ON NON-MONOTONIC REASONING (pp. 9–18). http://hdl.handle.net/20.500.12708/55560 ( reposiTUm)
Everardo, F., Hecher, M., & Shukla, A. (2020). An Approximate Model Counter for ASP. In 18th INTERNATIONAL WORKSHOP ON NON-MONOTONIC REASONING (pp. 208–216). http://hdl.handle.net/20.500.12708/55561 ( reposiTUm)
Fichte, J. K., Hecher, M., & Schidler, A. (2020). Solving the Steiner Tree Problem with few Terminals. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). ICTAI 2020 - 32th International Conference on Tools with Artificial Intelligence, online conference, Unknown. https://doi.org/10.1109/ictai50040.2020.00054 ( reposiTUm)
Faber, W., Morak, M., & Woltran, S. (2019). Strong Equivalence for Epistemic Logic Programs Made Easy. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 2809–2816). AAAI Press. https://doi.org/10.1609/aaai.v33i01.33012809 ( reposiTUm)
Haret, A., & Woltran, S. (2019). Belief Revision Operators with Varying Attitudes Towards Initial Beliefs. In S. Kraus (Ed.), Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI} 2019 (pp. 1726–1733). ijcai.org. https://doi.org/10.24963/ijcai.2019/239 ( reposiTUm)
Dvořák, W., & Woltran, S. (2019). Complexity of Abstract Argumentation under a Claim-Centric View. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 2801–2808). AAAI Press. https://doi.org/10.1609/aaai.v33i01.33012801 ( reposiTUm)
Gonçalves, R., Janhunen, T., Knorr, M., Leite, J., & Woltran, S. (2019). Forgetting in Modular Answer Set Programming. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 2843–2850). AAAI Press. https://doi.org/10.1609/aaai.v33i01.33012843 ( reposiTUm)
Gangl, C., Lackner, M., Maly, J., & Woltran, S. (2019). Aggregating Expert Opinions in Support of Medical Diagnostic Decision-Making. In Knowledge Representation for Health Care/ProHealth, KR4HC 2019 (pp. 56–62). http://hdl.handle.net/20.500.12708/57907 ( reposiTUm)
Hecher, M. (2019). Answer Set Solving exploiting Treewidth and its Limits. In CP 2019 (pp. 1–7). http://hdl.handle.net/20.500.12708/58010 ( reposiTUm)
Fichte, J., Hecher, M., & Zisser, M. (2019). gpusat2 - An Improved GPU Model Counter. In Pragmatics of SAT 2019 @ SAT 2019 (pp. 1–17). http://hdl.handle.net/20.500.12708/58011 ( reposiTUm)
Fichte, J., Hecher, M., & Philipp, T. (2019). Inconsistency Proofs for ASP: The ASP-DRUPE Format. In Aspocp 2019 @ Lpnmr 2019 (pp. 1–15). http://hdl.handle.net/20.500.12708/57764 ( reposiTUm)
ALVIANO, M., DODARO, C., FICHTE, J. K., HECHER, M., PHILIPP, T., & RATH, J. (2019). Inconsistency Proofs for ASP: The ASP - DRUPE Format. In Theory and Practice of Logic Programming (pp. 891–907). TPLP. https://doi.org/10.1017/s1471068419000255 ( reposiTUm)
Fichte, J. K., Hecher, M., & Meier, A. (2019). Counting Complexity for Reasoning in Abstract Argumentation. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 2827–2834). https://doi.org/10.1609/aaai.v33i01.33012827 ( reposiTUm)
Fichte, J., & Hecher, M. (2019). Treewidth and Counting Projected Answer Sets. In M. Balduccini, Y. Lierler, & S. Woltran (Eds.), Logic Programming and Nonmonotonic Reasoning: 15th International Conference, LPNMR 2019 (pp. 105–119). Springer. https://doi.org/10.1007/978-3-030-20528-7_9 ( reposiTUm)
Fichte, J., Hecher, M., & Zisser, M. (2019). An Improved GPU-Based SAT Model Counter. In T. Schiex & S. de Givry (Eds.), Principles and Practice of Constraint Programming: 25th International Conference, CP 2019 (pp. 491–509). Springer. https://doi.org/10.1007/978-3-030-30048-7_29 ( reposiTUm)
Fichte, J., Hecher, M., Lodha, N., & Szeider, S. (2018). An SMT Approach to Fractional Hypertree Width. In J. Hooker (Ed.), Principles and Practice of Constraint Programming, 24th International Conference, CP 2018 (pp. 109–127). Springer-Verlag. https://doi.org/10.1007/978-3-319-98334-9_8 ( reposiTUm)
Dvorak, W., Pührer, J., Wallner, J. P., Woltran, S., & Diller, M. (2018). Application of ASP in formal argumentation. In 2nd Workshop on Trends and Applications of Answer Set Programming (TAASP 2018) (pp. 1–11). http://hdl.handle.net/20.500.12708/57477 ( reposiTUm)
Lackner, M., & Skowron, P. (2018). A Quantitative Analysis of Multi-Winner Rules. In Proceedings of the 7th International Workshop on Computational Social Choice (COMSOC 2018) (pp. 1–29). Computing Research Repository (CoRR). http://hdl.handle.net/20.500.12708/57490 ( reposiTUm)
Hecher, M., & Fichte, J. (2018). Exploiting Treewidth for Counting Projected Answer Sets. In 17th International Workshop on Non-Monotonic Reasoning (NMR) (pp. 1–10). AAAI Press. http://hdl.handle.net/20.500.12708/57582 ( reposiTUm)
Haret, A., & Wallner, J. P. (2018). Manipulation of Semantic Aggregation Procedures for Propositional Knowledge Bases and Argumentation Frameworks. In E. Fermé & S. Villata (Eds.), Proceedings of the 17th International Workshop on Non-monotonic Reasoning (p. 10). http://hdl.handle.net/20.500.12708/57432 ( reposiTUm)
Woltran, S., Goncalves, R., Janhunen, T., Knorr, M., & Leite, J. (2018). Variable Elimination for DLP-Functions. In AAAI Publications, Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (pp. 643–644). AAAI. http://hdl.handle.net/20.500.12708/57297 ( reposiTUm)
Fichte, J. K., Hecher, M., Morak, M., & Woltran, S. (2018). Exploiting Treewidth for Projected Model Counting and Its Limits. In Theory and Applications of Satisfiability Testing – SAT 2018 (pp. 165–184). Springer. https://doi.org/10.1007/978-3-319-94144-8_11 ( reposiTUm)
Dvorak, W., Woltran, S., & Gressler, A. (2018). Evaluating SETAFs via Answer-Set Programming. In 2nd International Workshop on Systems and Algorithms for Formal Argumentation (pp. 10–21). CEUR-WS.org. http://hdl.handle.net/20.500.12708/57479 ( reposiTUm)
Fichte, J., Hecher, M., Woltran, S., & Zisser, M. (2018). Weighted Model Counting on the GPU by Exploiting Small Treewidth. In Y. Azar, H. Bast, & G. Herman (Eds.), 26th Annual European Symposium on Algorithms, {ESA} 2018 (pp. 28:2-28:16). Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik. https://doi.org/10.4230/LIPIcs.ESA.2018.28 ( reposiTUm)
Maly, J., Woltran, S., & Truszczynski, M. (2018). Preference Orders on Families of Sets - When Can Impossibility Results Be Avoided? In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. IJCAI - International Joint Conference on Artificial Intelligence, Stockholm, Sweden. ijcai.org. https://doi.org/10.24963/ijcai.2018/60 ( reposiTUm)
Csar, T., Lackner, M., & Pichler, R. (2018). Computing the Schulze Method for Large-Scale Preference Data Sets. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. IJCAI - International Joint Conference on Artificial Intelligence, Stockholm, Sweden. ijcai.org. https://doi.org/10.24963/ijcai.2018/25 ( reposiTUm)
Bichler, M., Morak, M., & Woltran, S. (2018). Single-Shot Epistemic Logic Program Solving. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. IJCAI - International Joint Conference on Artificial Intelligence, Stockholm, Sweden. ijcai.org. https://doi.org/10.24963/ijcai.2018/237 ( reposiTUm)
Lackner, M., & Skowron, P. (2018). Approval-Based Multi-Winner Rules and Strategic Voting. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. IJCAI - International Joint Conference on Artificial Intelligence, Stockholm, Sweden. ijcai.org. https://doi.org/10.24963/ijcai.2018/47 ( reposiTUm)
Maly, J., & Woltran, S. (2018). A New Logic for Jointly Representing Hard and Soft Constraints. In Second Workshop on Logics for Reasoning about Preferences, Uncertainty, and Vagueness (pp. 1–4). http://hdl.handle.net/20.500.12708/57539 ( reposiTUm)
Bliem, B. (2018). ASP Programs with Groundings of Small Treewidth. In F. Ferrarotti & S. Woltran (Eds.), Foundations of Information and Knowledge Systems: 10th International Symposium, FoIKS 2018 (pp. 97–113). LNCS. https://doi.org/10.1007/978-3-319-90050-6_6 ( reposiTUm)
Fichte, J. K., Hecher, M., & Schindler, I. (2018). Default Logic and Bounded Treewidth. In Language and Automata Theory and Applications (pp. 130–142). Springer. https://doi.org/10.1007/978-3-319-77313-1_10 ( reposiTUm)
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Präsentationen

Dvorak, W., König, M., Ulbricht, M., & Woltran, S. (2021). A Reduct-Driven Study of Argumentation Frameworks With Collective Attacks. NMR 2021 - 19th International Workshop on Non-Monotonic Reasoning, Hanoi, Vietnam, Viet Nam. http://hdl.handle.net/20.500.12708/87225 ( reposiTUm)
Everardo, F., Hecher, M., & Shukla, A. (2020). Extending XORRO with Approximate Model Counting. Workshop ASPOCP 2020, University of Calabria, Rende, Italy. http://hdl.handle.net/20.500.12708/87108 ( reposiTUm)
Hecher, M. (2020). Treewidth-Aware Reductions of normal ASP to SAT - Is normal ASP harder than SAT after all? 4th Workshop on Trends and Applications of Answer Set Programming (TAASP 2020), Klagenfurt, Austria. http://hdl.handle.net/20.500.12708/87107 ( reposiTUm)
Hecher, M., Thier, P., & Woltran, S. (2020). Taming High Treewidth with Abstraction, Nested Dynamic Programming, and Database Technology. DPSW 2020 - Declarative Problem Solving Workshop, Santiago de Compostela, online, Spain. http://hdl.handle.net/20.500.12708/87109 ( reposiTUm)
Woltran, S. (2016). Dynamic Programming on Tree Decompositions in Practice. 8th European Starting AI Researcher Symposium, Den Haag, Netherlands (the). http://hdl.handle.net/20.500.12708/86435 ( reposiTUm)

Berichte

Fichte, J., Kronegger, M., & Woltran, S. (2016). Multiparametric View on Answer Set Programming (DBAI-TR-2016-99). http://hdl.handle.net/20.500.12708/39080 ( reposiTUm)
Bichler, M., Bliem, B., Moldovan, M., Morak, M., & Woltran, S. (2016). Treewidth-Preserving Modeling in ASP (DBAI-TR-2016-97). http://hdl.handle.net/20.500.12708/39078 ( reposiTUm)
Fichte, J., Hecher, M., Morak, M., & Woltran, S. (2016). Answer Set Solving using Tree Decompositions and Dynamic Programming -- The DynASP2 System (DBAI-TR-2016-101). http://hdl.handle.net/20.500.12708/39081 ( reposiTUm)
Abseher, M., Musliu, N., & Woltran, S. (2016). htd -- A Free, Open-Source Framework for Tree Decompositions and Beyond (DBAI-TR-2016-96). http://hdl.handle.net/20.500.12708/39077 ( reposiTUm)
Fichte, J., & Szeider, S. (2016). Backdoor Trees for Answer Set Programming (DBAI-TR-2016-98). http://hdl.handle.net/20.500.12708/39079 ( reposiTUm)
Charwat, G., & Woltran, S. (2016). BDD-based Dynamic Programming on Tree Decompositions (DBAI-TR-2016-95, DBAI). http://hdl.handle.net/20.500.12708/39076 ( reposiTUm)
Abseher, M., Musliu, N., & Woltran, S. (2016). Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning (DBAI-TR-2016-94). http://hdl.handle.net/20.500.12708/39075 ( reposiTUm)
Bliem, B., Charwat, G., Hecher, M., & Woltran, S. (2015). D-FLAT^2: Subset Minimization in Dynamic Programming on Tree Decompositions Made Easy (DBAI-TR-2015-93). http://hdl.handle.net/20.500.12708/38634 ( reposiTUm)