Jip J. DekkerMonash University (Australia) · Data Science and AI
Jip J. Dekker
Doctor of Philosophy
About
11
Publications
1,319
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33
Citations
Introduction
Jip is a OPTIMA research fellow based at Monash University's Faculty of Information Technology, in the Department of Data Science and Artificial Intelligence. His research focuses on programming languages and tools to model and solve combinatorial optimisation problems. In particular, his research has involved the improved implementation and optimisation of the MiniZinc constraint modelling language and the creation of advanced interfaces to different types of solving technologies.
Skills and Expertise
Additional affiliations
October 2021 - present
Australian Research Council (ARC) Industrial Transformation Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA)
Position
- Research Fellow
Education
September 2017 - July 2021
September 2014 - February 2017
September 2011 - August 2014
Publications
Publications (11)
Despite the practical success of Artificial Intelligence (AI), current neural AI algorithms face two significant issues. First, the decisions made by neural architectures are often prone to bias and brittleness. Second, when a chain of reasoning is required, neural systems often perform poorly. Neuro-symbolic artificial intelligence is a promising...
Internal circadian phase assessment is increasingly acknowledged as a critical clinical tool for the diagnosis, monitoring, and treatment of circadian rhythm sleep−wake disorders and for investigating circadian timing in other medical disorders. The widespread use of in‐laboratory circadian phase assessments in routine practice has been limited, mo...
This paper presents new methods of encoding the multiplication of a binary encoded integer variable with a constant value for Boolean Satisfiability (SAT) solvers. This problem, known as the Single Constant Multiplication (SCM) problem, is widely studied for its application in hardware design, but its techniques are currently not employed for SAT e...
This paper presents new methods of encoding the multiplication of a binary encoded integer variable with a constant value for Boolean Satisfiability (SAT) solvers. This problem, known as the Single Constant Multiplication (SCM) problem, is widely studied for its application in hardware design, but this technique are currently not employed for SAT e...
Boolean satisfiability (SAT) solvers have dramatically improved their performance in the last twenty years, enabling them to solve large and complex problems. More recently MaxSAT solvers have appeared that efficiently solve optimisation problems based on SAT. This means that SAT solvers have become a competitive technology for tackling discrete op...
Constraint modelling languages are a prominent way to model and solve real
world problems. They are used in areas such as scheduling, supply chain management, and transportation, among many others. The rewriting process of a
constraint modelling language transforms a constraint model into a solver model,
the input required by the program that solve...
This paper describes the implementation of Nutmeg, a solver that hybridizes mixed integer linear programming and constraint programming using the branch-and-cut style of logic-based Benders decomposition known as branch-and-check. Given a high-level constraint programming model, Nutmeg automatically derives a mixed integer programming master proble...
The combination of large neighbourhood search (LNS) methods with complete search methods has proved to be very effective. By restricting the search to (small) areas around an existing solution, the complete method is often able to quickly improve its solutions. However, developing such a combined method can be time-consuming: While the model of a p...
A well-known and powerful constraint model reformulation is to compute the solutions to a model part, say a custom constraint predicate, and tabulate them within an extensional constraint that replaces that model part. Despite the possibility of achieving higher solving performance, this tabling reformulation is often not tried, because it is tedio...
Constraint models often describe complicated problems that contain sub-problems that could be solved in sub-models. Although pre-solving these sub-models might improve the performance of the model, manually splitting the model to accommodate the occurrences of these sub-models can be a great inconvenience for the modeler. This thesis introduces an...
Dealing with concurrency and parallelism is hard. Using scientific tools, models can be created giving insight into the nature of concurrent structures. Using these models one can sometimes even prove that various conditions hold within the model. To prevent human error when implementing a given model, one might consider a scheme where the source c...