46 citations · 157 across the 30 of their papers we have counts for
30 papers
Learning to Solve and Optimize by Evolving Code
Veronika Semmelrock, Benedetta Strizzolo, Francesco Zuccato +3
Combinatorial and optimization problems are fundamental to many industrial AI applications. Solving large-scale real-world instances of such problems typically requires careful pro…
Relevance-Based Compression of Cataract Surgery Videos
Natalia Mathá, Klaus Schoeffmann, Konstantin Schekotihin +3
In the last decade, the need for storing videos from cataract surgery has increased significantly. Hospitals continue to improve their imaging and recording devices (e.g., microsco…
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming
Pierre Tassel, Martin Gebser, Konstantin Schekotihin
Constraint Programming (CP) is a declarative programming paradigm that allows for modeling and solving combinatorial optimization problems, such as the Job-Shop Scheduling Problem…
Semiconductor Fab Scheduling with Self-Supervised and Reinforcement Learning
Pierre Tassel, Benjamin Kovács, Martin Gebser +3
Semiconductor manufacturing is a notoriously complex and costly multi-step process involving a long sequence of operations on expensive and quantity-limited equipment. Recent chip…
Specifying and Exploiting Non-Monotonic Domain-Specific Declarative Heuristics in Answer Set Programming
Richard Comploi-Taupe, Gerhard Friedrich, Konstantin Schekotihin +1
Domain-specific heuristics are an essential technique for solving combinatorial problems efficiently. Current approaches to integrate domain-specific heuristics with Answer Set Pro…
Efficient lifting of symmetry breaking constraints for complex combinatorial problems
Alice Tarzariol, Martin Gebser, Mark Law +1
Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for…