How did I get to become a cell biologist? Or, more generally, why do things happen the way they do? The answer provided by the philosopher Democritus and later adopted by Jacques Monod is "everything existing in the universe is the fruit of chance and necessity." While I read Monod's book Chance and Necessity as an undergraduate student, little did I appreciate the accuracy of this citation and...

This paper presents intelligent domain splitting, an approach for searching in CSPs with ordered domains. The technique has a particular strength for finding all solutions. It represents the search space by aggregations obtained through the cooperation of two known clustering concepts, namely intervals and cross-products. Intelligent domain splitting searches for solutions by iteratively breaki...

To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a new (but equivalent) semantics based on scenarios. Using this semantics, we can compile stochastic const...

Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a P-Space task. The only solution approach to date compiles down SCSPs into classical CSPs. This allows the reuse of classical constraint solvers to solve SCSPs, but at the cost of increased space requirements and weak constraint propagation. This paper tries to...

Ambiguous Chance Constrained Programs: Algorithms and Applications Emre Erdoğan Chance constrained problems are optimization problems where one or more constraints ensure that the probability of one or more events occurring is less than a prescribed threshold. Although it is typically assumed that the distribution defining the chance constraints are known perfectly; in practice this assumption ...

In this thesis structure-preserving time integrators for mechanical systems whose configuration space is a Lie Group are derived from a Hamilton-Pontryagin (HP) variational principle. In addition to its attractive properties for degenerate mechanical systems, the HP viewpoint also affords a practical way to design discrete Lagrangians, which are the cornerstone of variational integration theory...

Constraint satisfaction networks have been shown to be a very useful tool for knowledge representation in Artificial Intelligence applications. These networks often utilize local constraint propagation techniques to achieve global consistency (consistent labelling in vision). Such methods have been used extensively in the context of image understanding and interpretation, as well as planning, n...

Hybrid cc is a constraint programming language suitable for modeling, controlling and simulating hybrid systems, i.e. systems with continuous and discrete state changes. The language extends the concurrent constraint programming framework with default reasoning and combinators for programming continuous behavior. The most important constraint systems used in Hybrid cc are nonlinear equations an...

An approach to reliable modeling, simulation and verification of hybrid systems is interval arithmetic, which guarantees that a set of intervals narrower than specified size encloses the solution. Interval-based computation of hybrid systems is often difficult, especially when the systems are described by nonlinear ordinary differential equations (ODEs) and nonlinear algebraic equations. We for...

In this paper we analyze a two-dimensional discrete model of nearestneighbour Lennard-Jones interactions under the microscopical constraint that points on a lattice triangle maintain their order. This can be understood as a microscopical non-interpenetration constraint and amounts to the positiveness of the determinant of the gradient of the piecewiseaffine interpolations of the discrete displa...