9 accepted papers
The implicit hitting set (IHS) approach offers a general framework for solving computationally hard combinatorial optimization problems declaratively. IHS iterates between a decision oracle used for extracting sources of inconsistency and an optimizer for computing so-called hitting sets (HSs) over
Maximum satisfiability (MaxSAT) is a viable approach to solving NP-hard combinatorial optimization problems through propositional encodings. Understanding how problem structure and encodings impact the behaviour of different MaxSAT solving algorithms is an important challenge. In this work, we ident
The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training data and background knowledge. The challenge is searching vast hypothesis spaces, which is exacerbated because many logically equivalent hypotheses exist. To address this challenge, we introduce a m
A major challenge in inductive logic programming is learning big rules. To address this challenge, we introduce an approach where we join small rules to learn big rules. We implement our approach in a constraint-driven system and use constraint solvers to efficiently join rules. Our experiments on m…
Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game…
Two of the most central algorithmic paradigms implemented in practical solvers for maximum satisfiability (MaxSAT) and other related declarative paradigms for NP-hard combinatorial optimization are the core-guided (CG) and implicit hitting set (IHS) approaches. We develop a general unifying algorith…
Generalizing Bayesian networks, maximal ancestral graphs (MAGs) are a theoretically appealing model class for dealing with unobserved variables. Despite significant advances in developing practical exact algorithms for learning score-optimal Bayesian networks, practical exact algorithms for learning…
Control argumentation frameworks (CAFs) allow for modeling uncertainties inherent in various argumentative settings. We establish a complete computational complexity map of the central computational problem of controllability in CAFs for five key semantics. We also develop Boolean satisfiability bas…
We present a new algorithmic approach for the task of finding a chordal Markov network structure that maximizes a given scoring function. The algorithm is based on branch and bound and integrates dynamic programming for both domain pruning and for obtaining strong bounds for search-space pruning. Em…