Valentin Mayer-Eichberger

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Pseudo-Boolean constraints are omnipresent in practical applications, and thus a significant effort has been devoted to the development of good SAT encoding techniques for them. Some of these encodings first construct a Binary Decision Diagram (BDD) for the constraint, and then encode the BDD into a propositional formula. These BDD-based approaches have(More)
In this paper, we present a new decomposi-tional approach for the extraction of propositional rules from feed-forward neural networks of binary threshold units. After decomposing the network into single units, we show how to extract rules describing a unit's behavior. This is done using a suitable search tree which allows the pruning of the search space.(More)
The management of Urban Wastewater Systems (UWS) requires a comprehensive understanding of the interactions of processes and substances in the system. This leads to complex numerical models which can be applied to predict management actions or understand misconduction of the system. Nevertheless, for the communication between stakeholders in the process of(More)
Linear constraints are the most common constraints occurring in combinatorial problems. For some problems which combine linear constraints with highly combinatorial constraints, the best solving method is translation to SAT. Translation of a single linear constraint to SAT is a well studied problem, particularly for cardinality and pseudo-Boolean(More)
We compare both pure SAT and hybrid CP/SAT models for solving car sequencing problems, and close 13 out of the 23 large open instances in CSPLib. Three features of these models are crucial to improving the state of the art in this domain. For quickly finding solutions, advanced CP heuristics are important and good propagation (either by a specialized(More)
Pseudo-Boolean constraints are omnipresent in practical applications, and therefore a significant effort has been devoted to the development of good SAT encoding techniques for these constraints. Some of these encodings first construct a Binary Decision Diagram (BDD) for the constraint, and then encode the BDD into a propositional formula. These BDD-based(More)
task: Construction and evaluation of a new decompositional extraction algorithm in the field of neural symbolic integration. Abstract Combining artificial neural networks and logic programming for machine learning tasks is the main objective of neural symbolic integration. One important step towards practical applications in this field is the development of(More)