no code implementations • NeurIPS 2013 • Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik Nyman, Johan Pensar
We investigate the problem of learning the structure of a Markov network from data.
no code implementations • 15 Jan 2014 • Tomi Janhunen, Emilia Oikarinen, Hans Tompits, Stefan Woltran
Practically all programming languages allow the programmer to split a program into several modules which brings along several advantages in software development.
no code implementations • 19 Jul 2015 • Laura Koponen, Emilia Oikarinen, Tomi Janhunen, Laura Säilä
The supertree construction problem is about combining several phylogenetic trees with possibly conflicting information into a single tree that has all the leaves of the source trees as its leaves and the relationships between the leaves are as consistent with the source trees as possible.
no code implementations • 5 Aug 2016 • Bart Bogaerts, Tomi Janhunen, Shahab Tasharrofi
In this paper, we present a novel logic programming--based modeling paradigm that combines the best features of ASP and QBFs.
no code implementations • 13 Jul 2017 • Tomi Janhunen, Roland Kaminski, Max Ostrowski, Torsten Schaub, Sebastian Schellhorn, Philipp Wanko
The recent series 5 of the ASP system clingo provides generic means to enhance basic Answer Set Programming (ASP) with theory reasoning capabilities.
no code implementations • 3 Sep 2019 • Tomi Janhunen, Michael Sioutis
Allen's Interval Algebra constitutes a framework for reasoning about temporal information in a qualitative manner.
no code implementations • 7 Aug 2021 • Jukka Pajunen, Tomi Janhunen
However, in this work, we generalize the enumeration of optimal answer sets beyond strictly optimal ones, giving rise to the idea of answer set enumeration in the order of optimality (ASEO).
1 code implementation • 23 Jun 2022 • Susana Hahn, Tomi Janhunen, Roland Kaminski, Javier Romero, Nicolas Rühling, Torsten Schaub
We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes.
no code implementations • 3 Sep 2022 • Reijo Jaakkola, Tomi Janhunen, Antti Kuusisto, Masood Feyzbakhsh Rankooh, Miikka Vilander
We conceptualize explainability in terms of logic and formula size, giving a number of related definitions of explainability in a very general setting.
no code implementations • 8 Jun 2023 • Masood Feyzbakhsh Rankooh, Tomi Janhunen
We establish a novel relation between delete-free planning, an important task for the AI Planning community also known as relaxed planning, and logic programming.
no code implementations • 13 Jul 2023 • Reijo Jaakkola, Tomi Janhunen, Antti Kuusisto, Masood Feyzbakhsh Rankooh, Miikka Vilander
As an explanation of length k, we take a Boolean formula of length k that minimizes the error with respect to the target attribute to be explained.
no code implementations • 30 Aug 2023 • Tomi Janhunen
In answer set programming (ASP), answer sets capture solutions to search problems of interest and thus the efficient computation of answer sets is of utmost importance.
no code implementations • 8 Feb 2024 • Reijo Jaakkola, Tomi Janhunen, Antti Kuusisto, Masood Feyzbakhsh Rankooh, Miikka Vilander
We introduce a method for computing immediately human interpretable yet accurate classifiers from tabular data.