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2020 – today
- 2023
[c106]Ari Conati, Andreas Niskanen, Matti Järvisalo:
SAT-based Judgment Aggregation. AAMAS 2023: 1412-1420- [c105]Christoph Jabs, Jeremias Berg, Hannes Ihalainen, Matti Järvisalo:
Preprocessing in SAT-Based Multi-Objective Combinatorial Optimization. CP 2023: 18:1-18:20 - [c104]Hannes Ihalainen, Jeremias Berg, Matti Järvisalo:
Unifying Core-Guided and Implicit Hitting Set Based Optimization. IJCAI 2023: 1935-1943 - [c103]Isabelle Kuhlmann, Andreas Niskanen, Matti Järvisalo:
Computing MUS-Based Inconsistency Measures. JELIA 2023: 745-755 - [c102]Matti Järvisalo, Tuomo Lehtonen, Andreas Niskanen:
Design of ICCMA 2023, 5th International Competition on Computational Models of Argumentation: A Preliminary Report (invited paper). Arg&App@KR 2023: 4-10 - [c101]Daphne Odekerken
, Tuomo Lehtonen, AnneMarie Borg, Johannes Peter Wallner, Matti Järvisalo:
Argumentative Reasoning in ASPIC+ under Incomplete Information. KR 2023: 531-541 - [i14]Céline Hocquette, Andreas Niskanen, Matti Järvisalo, Andrew Cropper:
Learning MDL logic programs from noisy data. CoRR abs/2308.09393 (2023) - [i13]Tuukka Korhonen, Matti Järvisalo:
SharpSAT-TD in Model Counting Competitions 2021-2023. CoRR abs/2308.15819 (2023) - 2022
- [c100]Hannes Ihalainen, Jeremias Berg, Matti Järvisalo:
Clause Redundancy and Preprocessing in Maximum Satisfiability. IJCAR 2022: 75-94 - [c99]Tuomo Lehtonen, Johannes Peter Wallner, Matti Järvisalo:
Algorithms for Reasoning in a Default Logic Instantiation of Assumption-Based Argumentation. COMMA 2022: 236-247 - [c98]Tuomo Lehtonen, Johannes Peter Wallner, Matti Järvisalo:
Computing Stable Conclusions under the Weakest-Link Principle in the ASPIC+ Argumentation Formalism. KR 2022 - [c97]Andreas Niskanen, Jere Mustonen, Jeremias Berg, Matti Järvisalo:
Computing Smallest MUSes of Quantified Boolean Formulas. LPNMR 2022: 301-314 - [c96]Christoph Jabs, Jeremias Berg, Andreas Niskanen, Matti Järvisalo:
MaxSAT-Based Bi-Objective Boolean Optimization. SAT 2022: 12:1-12:23 - [c95]Pavel Smirnov, Jeremias Berg, Matti Järvisalo:
Improvements to the Implicit Hitting Set Approach to Pseudo-Boolean Optimization. SAT 2022: 13:1-13:18 - [c94]Andreas Niskanen, Jeremias Berg, Matti Järvisalo:
Incremental Maximum Satisfiability. SAT 2022: 14:1-14:19 - 2021
- [j31]Dorothea Baumeister, Matti Järvisalo, Daniel Neugebauer, Andreas Niskanen, Jörg Rothe:
Acceptance in incomplete argumentation frameworks. Artif. Intell. 295: 103470 (2021) - [j30]Nils Froleyks, Marijn Heule, Markus Iser, Matti Järvisalo, Martin Suda:
SAT Competition 2020. Artif. Intell. 301: 103572 (2021) - [j29]Tuomo Lehtonen, Johannes Peter Wallner, Matti Järvisalo:
Declarative Algorithms and Complexity Results for Assumption-Based Argumentation. J. Artif. Intell. Res. 71: 265-318 (2021) - [j28]Tuomo Lehtonen, Johannes Peter Wallner, Matti Järvisalo:
Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation. Theory Pract. Log. Program. 21(6): 717-734 (2021) - [c93]Tuukka Korhonen, Matti Järvisalo:
Integrating Tree Decompositions into Decision Heuristics of Propositional Model Counters (Short Paper). CP 2021: 8:1-8:11 - [c92]Hannes Ihalainen, Jeremias Berg, Matti Järvisalo:
Refined Core Relaxation for Core-Guided MaxSAT Solving. CP 2021: 28:1-28:19 - [c91]Andreas Niskanen, Jeremias Berg, Matti Järvisalo:
Enabling Incrementality in the Implicit Hitting Set Approach to MaxSAT Under Changing Weights. CP 2021: 44:1-44:19 - [c90]Pavel Smirnov, Jeremias Berg, Matti Järvisalo:
Pseudo-Boolean Optimization by Implicit Hitting Sets. CP 2021: 51:1-51:20 - [c89]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Maximal ancestral graph structure learning via exact search. UAI 2021: 1237-1247 - [p2]Armin Biere, Matti Järvisalo, Benjamin Kiesl:
Preprocessing in SAT Solving. Handbook of Satisfiability 2021: 391-435 - [p1]Fahiem Bacchus, Matti Järvisalo, Ruben Martins:
Maximum Satisfiabiliy. Handbook of Satisfiability 2021: 929-991 - [i12]Tuomo Lehtonen, Johannes Peter Wallner, Matti Järvisalo:
Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation. CoRR abs/2108.04192 (2021) - 2020
- [j27]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Discovering causal graphs with cycles and latent confounders: An exact branch-and-bound approach. Int. J. Approx. Reason. 117: 29-49 (2020) - [c88]Tuukka Korhonen, Matti Järvisalo:
Finding Most Compatible Phylogenetic Trees over Multi-State Characters. AAAI 2020: 1544-1551 - [c87]Andreas Niskanen, Daniel Neugebauer, Matti Järvisalo, Jörg Rothe:
Deciding Acceptance in Incomplete Argumentation Frameworks. AAAI 2020: 2942-2949 - [c86]Marcus Leivo, Jeremias Berg, Matti Järvisalo:
Preprocessing in Incomplete MaxSAT Solving. ECAI 2020: 347-354 - [c85]Andreas Niskanen, Matti Järvisalo:
Strong Refinements for Hard Problems in Argumentation Dynamics. ECAI 2020: 841-848 - [c84]Andreas Niskanen, Matti Järvisalo:
Algorithms for Dynamic Argumentation Frameworks: An Incremental SAT-Based Approach. ECAI 2020: 849-856 - [c83]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Learning Chordal Markov Networks via Stochastic Local Search. ECAI 2020: 2632-2639 - [c82]Andreas Niskanen, Daniel Neugebauer, Matti Järvisalo:
Controllability of Control Argumentation Frameworks. IJCAI 2020: 1855-1861 - [c81]Tuomo Lehtonen, Johannes Peter Wallner, Matti Järvisalo:
An Answer Set Programming Approach to Argumentative Reasoning in the ASPIC+ Framework. KR 2020: 636-646 - [c80]Andreas Niskanen, Matti Järvisalo:
Smallest Explanations and Diagnoses of Rejection in Abstract Argumentation. KR 2020: 667-671 - [c79]Andreas Niskanen, Matti Järvisalo:
µ-toksia: An Efficient Abstract Argumentation Reasoner. KR 2020: 800-804 - [c78]Jarkko Savela, Emilia Oikarinen, Matti Järvisalo:
Finding Periodic Apartments via Boolean Satisfiability and Orderly Generation. LPAR 2020: 465-482 - [c77]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Learning Optimal Cyclic Causal Graphs from Interventional Data. PGM 2020: 365-376
2010 – 2019
- 2019
- [j26]Jukka M. Toivanen, Matti Järvisalo, Olli Alm, Dan Ventura, Martti Vainio, Hannu Toivonen:
Towards transformational creation of novel songs. Connect. Sci. 31(1): 4-32 (2019) - [j25]Andreas Niskanen, Johannes Peter Wallner, Matti Järvisalo:
Synthesizing Argumentation Frameworks from Examples. J. Artif. Intell. Res. 66: 503-554 (2019) - [j24]Tuukka Korhonen, Jeremias Berg, Matti Järvisalo:
Solving Graph Problems via Potential Maximal Cliques: An Experimental Evaluation of the Bouchitté-Todinca Algorithm. ACM J. Exp. Algorithmics 24(1): 1.9:1-1.9:19 (2019) - [j23]Fahiem Bacchus, Matti Järvisalo, Ruben Martins:
MaxSAT Evaluation 2018: New Developments and Detailed Results. J. Satisf. Boolean Model. Comput. 11(1): 99-131 (2019) - [j22]Marijn J. H. Heule, Matti Järvisalo, Martin Suda:
SAT Competition 2018. J. Satisf. Boolean Model. Comput. 11(1): 133-154 (2019) - [c76]Tuomo Lehtonen, Johannes Peter Wallner, Matti Järvisalo:
Reasoning over Assumption-Based Argumentation Frameworks via Direct Answer Set Programming Encodings. AAAI 2019: 2938-2945 - [c75]Bernhard Bliem, Matti Järvisalo:
Centrality Heuristics for Exact Model Counting. ICTAI 2019: 59-63 - [c74]Tuukka Korhonen, Jeremias Berg, Matti Järvisalo:
Enumerating Potential Maximal Cliques via SAT and ASP. IJCAI 2019: 1116-1122 - [c73]Wolfgang Dvorák, Matti Järvisalo, Thomas Linsbichler, Andreas Niskanen, Stefan Woltran:
Preprocessing Argumentation Frameworks via Replacement Patterns. JELIA 2019: 116-132 - [c72]Jeremias Berg, Matti Järvisalo:
Unifying Reasoning and Core-Guided Search for Maximum Satisfiability. JELIA 2019: 287-303 - [e2]Daniel Le Berre, Matti Järvisalo:
Proceedings of Pragmatics of SAT 2015, Austin, Texas, USA, September 23, 2015 / Pragmatics of SAT 2018, Oxford, UK, July 7, 2018. EPiC Series in Computing 59, EasyChair 2019 [contents] - 2018
- [j21]Brandon M. Malone, Kustaa Kangas, Matti Järvisalo, Mikko Koivisto, Petri Myllymäki:
Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction. Mach. Learn. 107(1): 247-283 (2018) - [j20]Mario Alviano, Carmine Dodaro, Matti Järvisalo, Marco Maratea, Alessandro Previti:
Cautious reasoning in ASP via minimal models and unsatisfiable cores. Theory Pract. Log. Program. 18(3-4): 319-336 (2018) - [c71]Alessandro Previti, Carlos Mencía, Matti Järvisalo, João Marques-Silva:
Premise Set Caching for Enumerating Minimal Correction Subsets. AAAI 2018: 6633-6640 - [c70]Matti Järvisalo:
SAT for Argumentation. SAFA@COMMA 2018: 1-3 - [c69]Tuomo Lehtonen, Andreas Niskanen, Matti Järvisalo:
SAT-Based Approaches to Adjusting, Repairing, and Computing Largest Extensions of Argumentation Frameworks. COMMA 2018: 193-204 - [c68]Fahiem Bacchus, Antti Hyttinen, Matti Järvisalo, Paul Saikko:
Reduced Cost Fixing for Maximum Satisfiability. IJCAI 2018: 5209-5213 - [c67]Paul Saikko, Carmine Dodaro, Mario Alviano, Matti Järvisalo:
A Hybrid Approach to Optimization in Answer Set Programming. KR 2018: 32-41 - [c66]Andreas Niskanen, Johannes Peter Wallner, Matti Järvisalo:
Extension Enforcement under Grounded Semantics in Abstract Argumentation. KR 2018: 178-183 - [c65]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Learning Optimal Causal Graphs with Exact Search. PGM 2018: 344-355 - [c64]Alessandro Previti, Matti Järvisalo:
A preference-based approach to backbone computation with application to argumentation. SAC 2018: 896-902 - [c63]Jeremias Berg, Antti Hyttinen, Matti Järvisalo:
Applications of MaxSAT in Data Analysis. POS@SAT 2018: 50-64 - [i11]Mario Alviano, Carmine Dodaro, Matti Järvisalo, Marco Maratea, Alessandro Previti:
Cautious reasoning in ASP via minimal models and unsatisfiable cores. CoRR abs/1804.08480 (2018) - 2017
- [j19]Jeremias Berg, Matti Järvisalo:
Cost-optimal constrained correlation clustering via weighted partial Maximum Satisfiability. Artif. Intell. 244: 110-142 (2017) - [j18]Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks:
A constraint optimization approach to causal discovery from subsampled time series data. Int. J. Approx. Reason. 90: 208-225 (2017) - [j17]James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett:
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets and Complexity. J. Artif. Intell. Res. 58: 185-229 (2017) - [j16]Johannes Peter Wallner, Andreas Niskanen, Matti Järvisalo:
Complexity Results and Algorithms for Extension Enforcement in Abstract Argumentation. J. Artif. Intell. Res. 60: 1-40 (2017) - [c62]Tomás Balyo, Marijn J. H. Heule, Matti Järvisalo:
SAT Competition 2016: Recent Developments. AAAI 2017: 5061-5063 - [c61]Jeremias Berg, Emilia Oikarinen, Matti Järvisalo, Kai Puolamäki:
Minimum-Width Confidence Bands via Constraint Optimization. CP 2017: 443-459 - [c60]Fahiem Bacchus, Antti Hyttinen, Matti Järvisalo, Paul Saikko:
Reduced Cost Fixing in MaxSAT. CP 2017: 641-651 - [c59]Jeremias Berg, Matti Järvisalo:
Weight-Aware Core Extraction in SAT-Based MaxSAT Solving. CP 2017: 652-670 - [c58]Tuomo Lehtonen, Johannes Peter Wallner, Matti Järvisalo:
From Structured to Abstract Argumentation: Assumption-Based Acceptance via AF Reasoning. ECSQARU 2017: 57-68 - [c57]Alessandro Previti, Alexey Ignatiev, Matti Järvisalo, João Marques-Silva:
On Computing Generalized Backbones. ICTAI 2017: 1050-1056 - [c56]Antti Hyttinen, Paul Saikko, Matti Järvisalo:
A Core-Guided Approach to Learning Optimal Causal Graphs. IJCAI 2017: 645-651 - [c55]James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett:
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets and Complexity (Extended Abstract). IJCAI 2017: 4990-4994 - [c54]Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Learning Chordal Markov Networks via Branch and Bound. NIPS 2017: 1847-1857 - [c53]Alessandro Previti, Carlos Mencía, Matti Järvisalo, João Marques-Silva:
Improving MCS Enumeration via Caching. SAT 2017: 184-194 - [c52]Tuukka Korhonen, Jeremias Berg, Paul Saikko, Matti Järvisalo:
MaxPre: An Extended MaxSAT Preprocessor. SAT 2017: 449-456 - [i10]Brandon M. Malone, Kustaa Kangas, Matti Järvisalo, Mikko Koivisto, Petri Myllymäki:
AS-ASL: Algorithm Selection with Auto-sklearn. OASC 2017: 19-22 - 2016
- [j15]Danny Dolev, Keijo Heljanko, Matti Järvisalo, Janne H. Korhonen, Christoph Lenzen, Joel Rybicki, Jukka Suomela, Siert Wieringa:
Synchronous counting and computational algorithm design. J. Comput. Syst. Sci. 82(2): 310-332 (2016) - [j14]Magnus Find, Mika Göös, Matti Järvisalo, Petteri Kaski, Mikko Koivisto, Janne H. Korhonen:
Separating OR, SUM, and XOR circuits. J. Comput. Syst. Sci. 82(5): 793-801 (2016) - [c51]Johannes Peter Wallner, Andreas Niskanen, Matti Järvisalo:
Complexity Results and Algorithms for Extension Enforcement in Abstract Argumentation. AAAI 2016: 1088-1094 - [c50]Jeremias Berg, Matti Järvisalo:
Impact of SAT-Based Preprocessing on Core-Guided MaxSAT Solving. CP 2016: 66-85 - [c49]Andreas Niskanen, Johannes Peter Wallner, Matti Järvisalo:
Synthesizing Argumentation Frameworks from Examples. ECAI 2016: 551-559 - [c48]Jeremias Berg, Paul Saikko, Matti Järvisalo:
Subsumed Label Elimination for Maximum Satisfiability. ECAI 2016: 630-638 - [c47]Andreas Niskanen, Johannes Peter Wallner, Matti Järvisalo:
Optimal Status Enforcement in Abstract Argumentation. IJCAI 2016: 1216-1222 - [c46]Matti Järvisalo:
Boolean Satifiability and Beyond: Algorithms, Analysis, and AI Applications. IJCAI 2016: 4066-4069 - [c45]Andreas Niskanen, Johannes Peter Wallner, Matti Järvisalo:
Pakota: A System for Enforcement in Abstract Argumentation. JELIA 2016: 385-400 - [c44]Paul Saikko, Johannes Peter Wallner, Matti Järvisalo:
Implicit Hitting Set Algorithms for Reasoning Beyond NP. KR 2016: 104-113 - [c43]Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks:
Causal Discovery from Subsampled Time Series Data by Constraint Optimization. Probabilistic Graphical Models 2016: 216-227 - [c42]Paul Saikko, Jeremias Berg, Matti Järvisalo:
LMHS: A SAT-IP Hybrid MaxSAT Solver. SAT 2016: 539-546 - [i9]Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks:
Causal Discovery from Subsampled Time Series Data by Constraint Optimization. CoRR abs/1602.07970 (2016) - [i8]James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett:
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets, and Complexity. CoRR abs/1605.04071 (2016) - 2015
- [j13]Adrian Balint, Anton Belov, Matti Järvisalo, Carsten Sinz:
Overview and analysis of the SAT Challenge 2012 solver competition. Artif. Intell. 223: 120-155 (2015) - [j12]Lauri Hella, Matti Järvisalo, Antti Kuusisto, Juhana Laurinharju, Tuomo Lempiäinen, Kerkko Luosto, Jukka Suomela, Jonni Virtema:
Weak models of distributed computing, with connections to modal logic. Distributed Comput. 28(1): 31-53 (2015) - [j11]Marijn Heule, Matti Järvisalo, Florian Lonsing, Martina Seidl, Armin Biere:
Clause Elimination for SAT and QSAT. J. Artif. Intell. Res. 53: 127-168 (2015) - [c41]Paul Saikko, Brandon M. Malone, Matti Järvisalo:
MaxSAT-Based Cutting Planes for Learning Graphical Models. CPAIOR 2015: 347-356 - [c40]Jeremias Berg, Paul Saikko, Matti Järvisalo:
Re-using Auxiliary Variables for MaxSAT Preprocessing. ICTAI 2015: 813-820 - [c39]Jeremias Berg, Paul Saikko, Matti Järvisalo:
Improving the Effectiveness of SAT-Based Preprocessing for MaxSAT. IJCAI 2015: 239-245 - [c38]Wolfgang Dvorák, Matti Järvisalo, Johannes Peter Wallner, Stefan Woltran:
Complexity-Sensitive Decision Procedures for Abstract Argumentation (Extended Abstract). IJCAI 2015: 4173-4177 - [c37]Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo:
Do-calculus when the True Graph Is Unknown. UAI 2015: 395-404 - [c36]Brandon M. Malone, Matti Järvisalo, Petri Myllymäki:
Impact of Learning Strategies on the Quality of Bayesian Networks: An Empirical Evaluation. UAI 2015: 562-571 - [c35]Dag Sonntag, Matti Järvisalo, José M. Peña, Antti Hyttinen:
Learning Optimal Chain Graphs with Answer Set Programming. UAI 2015: 822-831 - 2014
- [j10]Wolfgang Dvorák, Matti Järvisalo, Johannes Peter Wallner, Stefan Woltran:
Complexity-sensitive decision procedures for abstract argumentation. Artif. Intell. 206: 53-78 (2014) - [c34]Kerstin Bunte, Matti Järvisalo, Jeremias Berg, Petri Myllymäki, Jaakko Peltonen, Samuel Kaski:
Optimal Neighborhood Preserving Visualization by Maximum Satisfiability. AAAI 2014: 1694-1700 - [c33]Brandon M. Malone, Kustaa Kangas, Matti Järvisalo, Mikko Koivisto, Petri Myllymäki:
Predicting the Hardness of Learning Bayesian Networks. AAAI 2014: 2460-2466 - [c32]Jeremias Berg, Matti Järvisalo, Brandon M. Malone:
Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability. AISTATS 2014: 86-95 - [c31]Jeremias Berg, Matti Järvisalo:
SAT-Based Approaches to Treewidth Computation: An Evaluation. ICTAI 2014: 328-335 - [c30]Emilia Oikarinen, Matti Järvisalo:
Answer Set Solver Backdoors. JELIA 2014: 674-683 - [c29]Matti Järvisalo, Janne H. Korhonen:
Conditional Lower Bounds for Failed Literals and Related Techniques. SAT 2014: 75-84 - [c28]Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo:
Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. UAI 2014: 340-349 - 2013
- [c27]Marijn Heule, Matti Järvisalo, Armin Biere:
Revisiting Hyper Binary Resolution. CPAIOR 2013: 77-93 - [c26]Jukka M. Toivanen, Matti Järvisalo, Hannu Toivonen:
Harnessing Constraint Programming for Poetry Composition. ICCC 2013: 160-167 - [c25]Jeremias Berg, Matti Järvisalo:
Optimal Correlation Clustering via MaxSAT. ICDM Workshops 2013: 750-757 - [c24]Anton Belov, Matti Järvisalo, João Marques-Silva:
Formula Preprocessing in MUS Extraction. TACAS 2013: 108-123 - [c23]Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo:
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. UAI 2013 - [e1]Matti Järvisalo, Allen Van Gelder:
Theory and Applications of Satisfiability Testing - SAT 2013 - 16th International Conference, Helsinki, Finland, July 8-12, 2013. Proceedings. Lecture Notes in Computer Science 7962, Springer 2013, ISBN 978-3-642-39070-8 [contents] - [i7]Magnus Find, Mika Göös, Matti Järvisalo, Petteri Kaski, Mikko Koivisto, Janne H. Korhonen:
Separating OR, SUM, and XOR Circuits. CoRR abs/1304.0513 (2013) - [i6]Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo:
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. CoRR abs/1309.6836 (2013) - 2012
- [j9]Matti Järvisalo, Daniel Le Berre, Olivier Roussel, Laurent Simon:
The International SAT Solver Competitions. AI Mag. 33(1): 89-92 (2012) - [j8]Matti Järvisalo, Armin Biere, Marijn Heule:
Simulating Circuit-Level Simplifications on CNF. J. Autom. Reason. 49(4): 583-619 (2012) - [c22]Matti Järvisalo, Marijn Heule, Armin Biere:
Inprocessing Rules. IJCAR 2012: 355-370 - [c21]Matti Järvisalo, Arie Matsliah, Jakob Nordström, Stanislav Zivný:
Relating Proof Complexity Measures and Practical Hardness of SAT. CP 2012: 316-331 - [c20]Wolfgang Dvorák, Matti Järvisalo, Johannes Peter Wallner, Stefan Woltran:
Complexity-Sensitive Decision Procedures for Abstract Argumentation. KR 2012 - [c19]Lauri Hella, Matti Järvisalo, Antti Kuusisto, Juhana Laurinharju, Tuomo Lempiäinen, Kerkko Luosto, Jukka Suomela, Jonni Virtema:
Weak models of distributed computing, with connections to modal logic. PODC 2012: 185-194 - [c18]Matti Järvisalo, Petteri Kaski,