Algebraic and Logical Study of Constructive Processes in Knowledge Representation

Proefschrift voorgedragen tot het behalen van het doctoraat in de ingenieurswetenschappen doo

Mondiale inputstrategie als een Chinese puzzel

internationalisation: Global sourcing strategy as a Chinese puzzle Uitbesteden, leveranciersrelaties en internationalisering

Parameter synthesis for Markov models

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all probabilities in Markov models need to be known. However, system quantities such as failure rates or packet loss ratios, etc. are often not — or only partially — known. This motivates considering parametric models with transitions labeled with functions over parameters. Whereas traditional Markov chain analysis relies on a single, fixed set of probabilities, analysing parametric Markov models focuses on synthesising parameter values that establish a given safety or performance specification $\phi$. Examples are: what component failure rates ensure the probability of a system breakdown to be below 0.00000001?, or which failure rates maximise the performance, for instance the throughput, of the system? This paper presents various analysis algorithms for parametric discrete-time Markov chains and Markov decision processes. We focus on three problems: (a) do all parameter values within a given region satisfy $\phi$?, (b) which regions satisfy $\phi$ and which ones do not?, and (c) an approximate version of (b) focusing on covering a large fraction of all possible parameter values. We give a detailed account of the various algorithms, present a software tool realising these techniques, and report on an extensive experimental evaluation on benchmarks that span a wide range of applications

Utilizing the Topology Preserving Property of Self-Organizing Maps for Classification

The Kohonen Self-Organizing Map (SOM) is a popular algorithm for constructing a nearest neighbor codebook in pattern space. The algorithm utilizes a predefined ordering on the codebook to distribute the codes proportionally on the input manifold. In the end this ordering should reflect the structure of the input. Prototypical application of the SOM uses the codebook but neglects the ordering. We explore the practical possibilities for taking advantage of the ordering, concentrating mainly on classification tasks. We present three approaches: coding class boundaries with a duo of SOMs, construction of radial basis function networks with ordering information and using a SOM as a preprocessor for backpropagation networks. We obtain positive results on a number of real world data sets from the field of medical diagnosis, speech-- and image processing. From this we conclude the ordering property of SOMs contains useful information. However, it is still unclear how to profit from it in the b..