1 code implementation • 26 Apr 2024 • Hannes Ihalainen, Andy Oertel, Yong Kiam Tan, Jeremias Berg, Matti Järvisalo, Jakob Nordström
For SAT, this is largely a solved problem thanks to the use of proof logging, meaning that solvers emit machine-verifiable proofs of (un)satisfiability to certify correctness.
1 code implementation • 29 Jan 2024 • Céline Hocquette, Andreas Niskanen, Rolf Morel, Matti Järvisalo, Andrew Cropper
A major challenge in inductive logic programming is learning big rules.
1 code implementation • 30 Aug 2023 • Tuukka Korhonen, Matti Järvisalo
We describe SharpSAT-TD, our submission to the unweighted and weighted tracks of the Model Counting Competition in 2021-2023, which has won in total $6$ first places in different tracks of the competition.
1 code implementation • 18 Aug 2023 • Céline Hocquette, Andreas Niskanen, Matti Järvisalo, Andrew Cropper
Many inductive logic programming approaches struggle to learn programs from noisy data.
1 code implementation • 9 Aug 2021 • Tuomo Lehtonen, Johannes P. Wallner, Matti Järvisalo
In this work, we harness recent advances in incremental ASP solving for developing effective algorithms for reasoning tasks in the logic programming fragment of ABA that are presumably hard for the second level of the polynomial hierarchy, including skeptical reasoning under preferred semantics as well as preferential reasoning.
no code implementations • NeurIPS 2017 • Kari Rantanen, Antti Hyttinen, Matti Järvisalo
We present a new algorithmic approach for the task of finding a chordal Markov network structure that maximizes a given scoring function.
no code implementations • 13 May 2016 • James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett
The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research.
no code implementations • 25 Feb 2016 • Antti Hyttinen, Sergey Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system.