Search Results for author:
Found 13 papers, 1 papers with code
Interpretable classifiers for tabular data via discretization and feature selection
We introduce a method for computing immediately human interpretable yet accurate classifiers from tabular data.
Generalizing Level Ranking Constraints for Monotone and Convex Aggregates
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.
Short Boolean Formulas as Explanations in Practice
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.
Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs
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.
Explainability via Short Formulas: the Case of Propositional Logic with Implementation
We conceptualize explainability in terms of logic and formula size, giving a number of related definitions of explainability in a very general setting.
plingo: A system for probabilistic reasoning in clingo based on lpmln
We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes.
Solution Enumeration by Optimality in Answer Set Programming
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).
Allen's Interval Algebra Makes the Difference
Allen's Interval Algebra constitutes a framework for reasoning about temporal information in a qualitative manner.
Clingo goes Linear Constraints over Reals and Integers
The recent series 5 of the ASP system clingo provides generic means to enhance basic Answer Set Programming (ASP) with theory reasoning capabilities.
Stable-Unstable Semantics: Beyond NP with Normal Logic Programs
In this paper, we present a novel logic programming--based modeling paradigm that combines the best features of ASP and QBFs.
Optimizing Phylogenetic Supertrees Using Answer Set Programming
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.
Modularity Aspects of Disjunctive Stable Models
Practically all programming languages allow the programmer to split a program into several modules which brings along several advantages in software development.
Learning Chordal Markov Networks by Constraint Satisfaction
We investigate the problem of learning the structure of a Markov network from data.
