Adaptable transition systems

We present an essential model of adaptable transition systems inspired by white-box approaches to adaptation and based on foundational models of component based systems. The key feature of adaptable transition systems are control propositions, imposing a clear separation between ordinary, functional behaviours and adaptive ones. We instantiate our approach on interface automata yielding adaptable interface automata, but it may be instantiated on other foundational models of component-based systems as well. We discuss how control propositions can be exploited in the specification and analysis of adaptive systems, focusing on various notions proposed in the literature, like adaptability, control loops, and control synthesis

Adapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm

From formal and practical analysis, we identify new challenges that self-adaptive systems pose to the process of quality assurance. When tackling these, the effort spent on various tasks in the process of software engineering is naturally re-distributed. We claim that all steps related to testing need to become self-adaptive to match the capabilities of the self-adaptive system-under-test. Otherwise, the adaptive system's behavior might elude traditional variants of quality assurance. We thus propose the paradigm of scenario coevolution, which describes a pool of test cases and other constraints on system behavior that evolves in parallel to the (in part autonomous) development of behavior in the system-under-test. Scenario coevolution offers a simple structure for the organization of adaptive testing that allows for both human-controlled and autonomous intervention, supporting software engineering for adaptive systems on a procedural as well as technical level.Comment: 17 pages, published at ISOLA 201

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

A Conceptual Framework for Adapation

We present a white-box conceptual framework for adaptation. We called it CODA, for COntrol Data Adaptation, since it is based on the notion of control data. CODA promotes a neat separation between application and adaptation logic through a clear identification of the set of data that is relevant for the latter. The framework provides an original perspective from which we survey a representative set of approaches to adaptation ranging from programming languages and paradigms, to computational models and architectural solutions

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

ASCENS: Engineering Autonomic Service-Component Ensembles

Today’s developers often face the demanding task of developing software for ensembles: systems with massive numbers of nodes, operating in open and non-deterministic environments with complex interactions, and the need to dynamically adapt to new requirements, technologies or environmental conditions without redeployment and without interruption of the system’s functionality. Conventional development approaches and languages do not provide adequate support for the problems posed by this challenge. The goal of the ASCENS project is to develop a coherent, integrated set of methods and tools to build software for ensembles. To this end we research foundational issues that arise during the development of these kinds of systems, and we build mathematical models that address them. Based on these theories we design a family of languages for engineering ensembles, formal methods that can handle the size, complexity and adaptivity required by ensembles, and software-development methods that provide guidance for developers. In this paper we provide an overview of several research areas of ASCENS: the SOTA approach to ensemble engineering and the underlying formal model called GEM, formal notions of adaptation and awareness, the SCEL language, quantitative analysis of ensembles, and finally software-engineering methods for ensembles

Self-adaptive fitness in evolutionary processes

Most optimization algorithms or methods in artificial intelligence can be regarded as evolutionary processes. They start from (basically) random guesses and produce increasingly better results with respect to a given target function, which is defined by the process's designer. The value of the achieved results is communicated to the evolutionary process via a fitness function that is usually somewhat correlated with the target function but does not need to be exactly the same. When the values of the fitness function change purely for reasons intrinsic to the evolutionary process, i.e., even though the externally motivated goals (as represented by the target function) remain constant, we call that phenomenon self-adaptive fitness. We trace the phenomenon of self-adaptive fitness back to emergent goals in artificial chemistry systems, for which we develop a new variant based on neural networks. We perform an in-depth analysis of diversity-aware evolutionary algorithms as a prime example of how to effectively integrate self-adaptive fitness into evolutionary processes. We sketch the concept of productive fitness as a new tool to reason about the intrinsic goals of evolution. We introduce the pattern of scenario co-evolution, which we apply to a reinforcement learning agent competing against an evolutionary algorithm to improve performance and generate hard test cases and which we also consider as a more general pattern for software engineering based on a solid formal framework. Multiple connections to related topics in natural computing, quantum computing and artificial intelligence are discovered and may shape future research in the combined fields.Die meisten Optimierungsalgorithmen und die meisten Verfahren in Bereich künstlicher Intelligenz können als evolutionäre Prozesse aufgefasst werden. Diese beginnen mit (prinzipiell) zufällig geratenen Lösungskandidaten und erzeugen dann immer weiter verbesserte Ergebnisse für gegebene Zielfunktion, die der Designer des gesamten Prozesses definiert hat. Der Wert der erreichten Ergebnisse wird dem evolutionären Prozess durch eine Fitnessfunktion mitgeteilt, die normalerweise in gewissem Rahmen mit der Zielfunktion korreliert ist, aber auch nicht notwendigerweise mit dieser identisch sein muss. Wenn die Werte der Fitnessfunktion sich allein aus für den evolutionären Prozess intrinsischen Gründen ändern, d.h. auch dann, wenn die extern motivierten Ziele (repräsentiert durch die Zielfunktion) konstant bleiben, nennen wir dieses Phänomen selbst-adaptive Fitness. Wir verfolgen das Phänomen der selbst-adaptiven Fitness zurück bis zu künstlichen Chemiesystemen (artificial chemistry systems), für die wir eine neue Variante auf Basis neuronaler Netze entwickeln. Wir führen eine tiefgreifende Analyse diversitätsbewusster evolutionärer Algorithmen durch, welche wir als Paradebeispiel für die effektive Integration von selbst-adaptiver Fitness in evolutionäre Prozesse betrachten. Wir skizzieren das Konzept der produktiven Fitness als ein neues Werkzeug zur Untersuchung von intrinsischen Zielen der Evolution. Wir führen das Muster der Szenarien-Ko-Evolution (scenario co-evolution) ein und wenden es auf einen Agenten an, der mittels verstärkendem Lernen (reinforcement learning) mit einem evolutionären Algorithmus darum wetteifert, seine Leistung zu erhöhen bzw. härtere Testszenarien zu finden. Wir erkennen dieses Muster auch in einem generelleren Kontext als formale Methode in der Softwareentwicklung. Wir entdecken mehrere Verbindungen der besprochenen Phänomene zu Forschungsgebieten wie natural computing, quantum computing oder künstlicher Intelligenz, welche die zukünftige Forschung in den kombinierten Forschungsgebieten prägen könnten

Smart Infrastructure Visualization

Výpočetní síla přenosných zařízení v poslední době výrazně stoupla. Jednou z výhod, kterou to přináší, je možnost nasazení nových druhů distribuovaných systémů, jako například komponentových systémů založených na ensemblech (EBCS). Z praktických důvodů jsou zpravidla EBCS systémy před dokončením testovány pomocí simulací. Často však bývá obtížné interpretovat výstup z takových simulací, neboť je obvykle ve formátu XML, jenž je uzpůsoben strojovému čtení. Vytvořili jsme aplikaci pro grafickou vizualizaci těchto dat - v současné podobě dokáže graficky znázornit výstup z aplikací postavených na JDEECo komponentovém modelu, ale může být snadno upravena i pro jiné EBCS systémy. Aplikace dokáže znázornit komponenty a ensembly, jejichž grafickou realizaci je možné měnit pomocí připravené skriptovací konzole. Dále aplikace nabízí možnost rozšíření pomocí zásuvných modulů. Provedené měření výkonu ukazuje, že při použití typických vstupů aplikace běží rozumně rychle.Computational power of mobile devices has been continuously improving in the recent years. One of the benefits which it brings, is feasibility of new kinds of distributed systems, such as Ensemble-Based Component Systems (EBCS). For practical reasons, EBCS systems are usually tested using simulations before being released. However, it can be difficult to interpret the simulation output, as it is usually contained in XML format, which is more suited to be read by machines than by people. We provide a visualizing application, which creates a graphical representation of such a simulation output. Out of the box, it is able to visualize data from applications built on top of the JDEECo component model, but it can be easily modified to accept output from different EBCS applications. It is able to visualize both components and ensembles and provides a scripting interface to modify the graphical output. In addition, it has an extensibility mechanism for adding new functionalities. Our benchmarking shows that the application is expected to run reasonably fast in typical scenarios.Department of Distributed and Dependable SystemsKatedra distribuovaných a spolehlivých systémůMatematicko-fyzikální fakultaFaculty of Mathematics and Physic