Konstantin Schekotihin — accepted AI-conference papers

2026

Learning to Solve and Optimize by Evolving Code

Veronika Semmelrock, Benedetta Strizzolo, Francesco Zuccato, Gerhard Friedrich, Patrick Rodler, Konstantin Schekotihin

IJCAI 2026

Combinatorial and optimization problems are fundamental to many industrial AI applications. Solving large-scale real-world instances of such problems typically requires careful problem formalization, specialized solvers, and expert-designed heuristics. Thus, experts need to specify not only *what* s

Cited by 0 Scholar ↗

2023

Learning to Break Symmetries for Efficient Optimization in Answer Set Programming

Alice Tarzariol, Martin Gebser, Konstantin Schekotihin, Mark Law

AAAI 2023technical

The ability to efficiently solve hard combinatorial optimization problems is a key prerequisite to various applications of declarative programming paradigms. Symmetries in solution candidates pose a significant challenge to modern optimization algorithms since the enumeration of such candidates migh…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗

2021

Lifting Symmetry Breaking Constraints with Inductive Logic Programming

Alice Tarzariol, Martin Gebser, Konstantin Schekotihin

IJCAI 2021poster

Efficient omission of symmetric solution candidates is essential for combinatorial problem solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such a…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗