Synthesis of Recursive ADT Transformations from Reusable Templates

Recent work has proposed a promising approach to improving scalability of program synthesis by allowing the user to supply a syntactic template that constrains the space of potential programs. Unfortunately, creating templates often requires nontrivial effort from the user, which impedes the usability of the synthesizer. We present a solution to this problem in the context of recursive transformations on algebraic data-types. Our approach relies on polymorphic synthesis constructs: a small but powerful extension to the language of syntactic templates, which makes it possible to define a program space in a concise and highly reusable manner, while at the same time retains the scalability benefits of conventional templates. This approach enables end-users to reuse predefined templates from a library for a wide variety of problems with little effort. The paper also describes a novel optimization that further improves the performance and scalability of the system. We evaluated the approach on a set of benchmarks that most notably includes desugaring functions for lambda calculus, which force the synthesizer to discover Church encodings for pairs and boolean operations

Fuzzy-Analysis in a Generic Polymorphic Uncertainty Quantification Framework

In this thesis, a framework for generic uncertainty analysis is developed. The two basic uncertainty characteristics aleatoric and epistemic uncertainty are differentiated. Polymorphic uncertainty as the combination of these two characteristics is discussed. The main focus is on epistemic uncertainty, with fuzziness as an uncertainty model. Properties and classes of fuzzy quantities are discussed. Some information reduction measures to reduce a fuzzy quantity to a characteristic value, are briefly debated. Analysis approaches for aleatoric, epistemic and polymorphic uncertainty are discussed. For fuzzy analysis α-level-based and α-level-free methods are described. As a hybridization of both methods, non-flat α-level-optimization is proposed. For numerical uncertainty analysis, the framework PUQpy, which stands for “Polymorphic Uncertainty Quantification in Python” is introduced. The conception, structure, data structure, modules and design principles of PUQpy are documented. Sequential Weighted Sampling (SWS) is presented as an optimization algorithm for general purpose optimization, as well as for fuzzy analysis. Slice Sampling as a component of SWS is shown. Routines to update Pareto-fronts, which are required for optimization are benchmarked. Finally, PUQpy is used to analyze example problems as a proof of concept. In those problems analytical functions with uncertain parameters, characterized by fuzzy and polymorphic uncertainty, are examined

Multiplex dispensation order generation for pyrosequencing

This paper introduces the multiplex dispensation order generation problem, a real-life combinatorial problem that arises in the context of analyzing large numbers of short to medium length DNA sequences. The problem is modeled as a constraint optimization problem (COP). We present the COP, its constraint programming formulation, and a custom search procedure. We give some experimental data supporting our design decisions. One of the lessons learnt from this study is that the ease with which the relevant constraints are expressed can be a crucial factor in making design decisions in the COP model

Mechatronic Design: A Port-Based Approach

In this paper we consider the integrated design of a mechatronic system. After considering the different design steps it is shown that a port-based approach during all phases of the design supports a true mechatronic design philosophy. Port-based design enables use of consistent models of the system throughout the design process, multiple views in different domains and reusability of plant models, controller components and software processes. The ideas are illustrated with the conceptual and detailed design of a mobile robot