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Found 7 papers, 3 papers with code
Learning big logical rules by joining small rules
A major challenge in inductive logic programming is learning big rules.
SharpSAT-TD in Model Counting Competitions 2021-2023
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.
Learning MDL logic programs from noisy data
Many inductive logic programming approaches struggle to learn programs from noisy data.
Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation
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.
Learning Chordal Markov Networks via Branch and Bound
We present a new algorithmic approach for the task of finding a chordal Markov network structure that maximizes a given scoring function.
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets, and Complexity
The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research.
Causal Discovery from Subsampled Time Series Data by Constraint Optimization
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.
