Towards distributed reasoning for behavioral optimization

We propose an architecture which supports the behavioral self-optimization of complex systems. In this architecture we bring together specification-based reasoning and the framework of ant colony optimization (ACO). By this we provide a foundation for distributed reasoning about different properties of the solution space represented by different viewpoint specifications. As a side-effect of reasoning we propagate the information about promising areas in the solution space to the current state. Consequently the system’s decisions can be improved by considering the long term values of certain behavioral trajectories (given a certain situational horizon). We consider this feature to be a contribution to autonomic computing1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration 1Red de Universidades con Carreras en Informática (RedUNCI

Towards distributed reasoning for behavioral optimization

We propose an architecture which supports the behavioral self-optimization of complex systems. In this architecture we bring together specification-based reasoning and the framework of ant colony optimization (ACO). By this we provide a foundation for distributed reasoning about different properties of the solution space represented by different viewpoint specifications. As a side-effect of reasoning we propagate the information about promising areas in the solution space to the current state. Consequently the system’s decisions can be improved by considering the long term values of certain behavioral trajectories (given a certain situational horizon). We consider this feature to be a contribution to autonomic computing1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration 1Red de Universidades con Carreras en Informática (RedUNCI

On Measures of Behavioral Distance between Business Processes

The desire to compute similarities or distances between business processes arises in numerous situations such as when comparing business processes with reference models or when integrating business processes. The objective of this paper is to develop an approach for measuring the distance between Business Processes Models (BPM) based on the behavior of the business process only while abstracting from any structural aspects of the actual model. Furthermore, the measure allows for assigning more weight to parts of a process which are executed more frequently and can thus be considered as more important. This is achieved by defining a probability distribution on the behavior allowing the computation of distance metrics from the field of statistics

Quantitative testing

We investigate the problem of specification based testing with dense sets of inputs and outputs, in particular with imprecision as they might occur due to errors in measurements, numerical instability or noisy channels. Using quantitative transition systems to describe implementations and specifications, we introduce implementation relations that capture a notion of correctness “up to ε”, allowing deviations of implementation from the specification of at most ε. These quantitative implementation relations are described as Hausdorff distances between certain sets of traces. They are conservative extensions of the well-known ioco relation. We develop an on-line and an off-line algorithm to generate test cases from a requirement specification, modeled as a quantitative transition system. Both algorithms are shown to be sound and complete with respect to the quantitative implementation relations introduced

Towards distributed reasoning for behavioral optimization

We propose an architecture which supports the behavioral self-optimization of complex systems. In this architecture we bring together specification-based reasoning and the framework of ant colony optimization (ACO). By this we provide a foundation for distributed reasoning about different properties of the solution space represented by different viewpoint specifications. As a side-effect of reasoning we propagate the information about promising areas in the solution space to the current state. Consequently the system’s decisions can be improved by considering the long term values of certain behavioral trajectories (given a certain situational horizon). We consider this feature to be a contribution to autonomic computing1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration 1Red de Universidades con Carreras en Informática (RedUNCI

Characteristic Logics for Behavioural Hemimetrics via Fuzzy Lax Extensions

In systems involving quantitative data, such as probabilistic, fuzzy, or metric systems, behavioural distances provide a more fine-grained comparison of states than two-valued notions of behavioural equivalence or behaviour inclusion. Like in the two-valued case, the wide variation found in system types creates a need for generic methods that apply to many system types at once. Approaches of this kind are emerging within the paradigm of universal coalgebra, based either on lifting pseudometrics along set functors or on lifting general real-valued (fuzzy) relations along functors by means of fuzzy lax extensions. An immediate benefit of the latter is that they allow bounding behavioural distance by means of fuzzy (bi-)simulations that need not themselves be hemi- or pseudometrics; this is analogous to classical simulations and bisimulations, which need not be preorders or equivalence relations, respectively. The known generic pseudometric liftings, specifically the generic Kantorovich and Wasserstein liftings, both can be extended to yield fuzzy lax extensions, using the fact that both are effectively given by a choice of quantitative modalities. Our central result then shows that in fact all fuzzy lax extensions are Kantorovich extensions for a suitable set of quantitative modalities, the so-called Moss modalities. For nonexpansive fuzzy lax extensions, this allows for the extraction of quantitative modal logics that characterize behavioural distance, i.e. satisfy a quantitative version of the Hennessy-Milner theorem; equivalently, we obtain expressiveness of a quantitative version of Moss' coalgebraic logic. All our results explicitly hold also for asymmetric distances (hemimetrics), i.e. notions of quantitative simulation