A formalism for describing and simulating systems with interacting components.

This thesis addresses the problem of descriptive complexity presented by systems involving a high number of interacting components. It investigates the evaluation measure of performability and its application to such systems. A new description and simulation language, ICE and it's application to performability modelling is presented. ICE (Interacting ComponEnts) is based upon an earlier description language which was first proposed for defining reliability problems. ICE is declarative in style and has a limited number of keywords. The ethos in the development of the language has been to provide an intuitive formalism with a powerful descriptive space. The full syntax of the language is presented with discussion as to its philosophy. The implementation of a discrete event simulator using an ICE interface is described, with use being made of examples to illustrate the functionality of the code and the semantics of the language. Random numbers are used to provide the required stochastic behaviour within the simulator. The behaviour of an industry standard generator within the simulator and different methods of number allocation are shown. A new generator is proposed that is a development of a fast hardware shift register generator and is demonstrated to possess good statistical properties and operational speed. For the purpose of providing a rigorous description of the language and clarification of its semantics, a computational model is developed using the formalism of extended coloured Petri nets. This model also gives an indication of the language's descriptive power relative to that of a recognised and well developed technique. Some recognised temporal and structural problems of system event modelling are identified. and ICE solutions given. The growing research area of ATM communication networks is introduced and a sophisticated top down model of an ATM switch presented. This model is simulated and interesting results are given. A generic ICE framework for performability modelling is developed and demonstrated. This is considered as a positive contribution to the general field of performability research

Accounting for Recreational Drug Use: The Lived Practice of Qualitative Interviews

The thesis uses (semi-)open-ended interviews with drug users and non-drug users to document the lived practices of qualitative interviewing and talk about drugs. It draws on Sacks and more contemporary work on conversation analysis and membership categorization to outline in detail 'how qualitative interviews come off in and through talk-in-interaction. It also contextualises the interviewees' topic talk, their talk-about- drugs. An (intimately) related concern, is to document some ideals about qualitative interviewing and talk about drugs as ideals-in-and-as-lived-practice. Underlying all types of research interviews is the tension between an extra-local need to collect data on a topic and a here-and-now interactional event in which this data is collected in and through talk-in-interaction (Antaki and Rapley 1996, Mazeland and ten Have 1996/1998, Suchman and Jordan 1990). The thesis describes how interviewers and interviewees manage this tension, documenting (some of) the methods - the practical solutions -they routinely use "to get the job done". Special reference is given to how interviewers locally produce themselves as 'the sort of qualitative interviewers they are supposed to be'. The research outlines how qualitative interviews are locally produced, with reference to the structural, sequential and topical organization. It focuses on (some of) the methods interviewers use explicitly to inform the interviewee that their questions are to be heard and understood as neutralistic and/or facilitatory. These methods include producing questions without preferred responses, question prefaces, specific lexical choices and tag-components. It also outlines (some of) the methods interviewees use to produce themselves as 'morally adequate' types-of-people in relation to topics in the talk. This thesis seeks to unsettle some of the current research practices and theories in the academic 'drug' and 'qualitative interviewing' worlds. Chiefly, it shows how both speakers, the interviewee and interviewer, are essential to, in Silverman's (1973) term, 'bringing off the research instrument'

Simulation and statistical model-checking of logic-based multi-agent system models

This thesis presents SALMA (Simulation and Analysis of Logic-Based Multi- Agent Models), a new approach for simulation and statistical model checking of multi-agent system models. Statistical model checking is a relatively new branch of model-based approximative verification methods that help to overcome the well-known scalability problems of exact model checking. In contrast to existing solutions, SALMA specifies the mechanisms of the simulated system by means of logical axioms based upon the well-established situation calculus. Leveraging the resulting first-order logic structure of the system model, the simulation is coupled with a statistical model-checker that uses a first-order variant of time-bounded linear temporal logic (LTL) for describing properties. This is combined with a procedural and process-based language for describing agent behavior. Together, these parts create a very expressive framework for modeling and verification that allows direct fine-grained reasoning about the agents’ interaction with each other and with their (physical) environment. SALMA extends the classical situation calculus and linear temporal logic (LTL) with means to address the specific requirements of multi-agent simulation models. In particular, cyber-physical domains are considered where the agents interact with their physical environment. Among other things, the thesis describes a generic situation calculus axiomatization that encompasses sensing and information transfer in multi agent systems, for instance sensor measurements or inter-agent messages. The proposed model explicitly accounts for real-time constraints and stochastic effects that are inevitable in cyber-physical systems. In order to make SALMA’s statistical model checking facilities usable also for more complex problems, a mechanism for the efficient on-the-fly evaluation of first-order LTL properties was developed. In particular, the presented algorithm uses an interval-based representation of the formula evaluation state together with several other optimization techniques to avoid unnecessary computation. Altogether, the goal of this thesis was to create an approach for simulation and statistical model checking of multi-agent systems that builds upon well-proven logical and statistical foundations, but at the same time takes a pragmatic software engineering perspective that considers factors like usability, scalability, and extensibility. In fact, experience gained during several small to mid-sized experiments that are presented in this thesis suggest that the SALMA approach seems to be able to live up to these expectations.In dieser Dissertation wird SALMA (Simulation and Analysis of Logic-Based Multi-Agent Models) vorgestellt, ein im Rahmen dieser Arbeit entwickelter Ansatz für die Simulation und die statistische Modellprüfung (Model Checking) von Multiagentensystemen. Der Begriff „Statistisches Model Checking” beschreibt modellbasierte approximative Verifikationsmethoden, die insbesondere dazu eingesetzt werden können, um den unvermeidlichen Skalierbarkeitsproblemen von exakten Methoden zu entgehen. Im Gegensatz zu bisherigen Ansätzen werden in SALMA die Mechanismen des simulierten Systems mithilfe logischer Axiome beschrieben, die auf dem etablierten Situationskalkül aufbauen. Die dadurch entstehende prädikatenlogische Struktur des Systemmodells wird ausgenutzt um ein Model Checking Modul zu integrieren, das seinerseits eine prädikatenlogische Variante der linearen temporalen Logik (LTL) verwendet. In Kombination mit einer prozeduralen und prozessorientierten Sprache für die Beschreibung von Agentenverhalten entsteht eine ausdrucksstarke und flexible Plattform für die Modellierung und Verifikation von Multiagentensystemen. Sie ermöglicht eine direkte und feingranulare Beschreibung der Interaktionen sowohl zwischen Agenten als auch von Agenten mit ihrer (physischen) Umgebung. SALMA erweitert den klassischen Situationskalkül und die lineare temporale Logik (LTL) um Elemente und Konzepte, die auf die spezifischen Anforderungen bei der Simulation und Modellierung von Multiagentensystemen ausgelegt sind. Insbesondere werden cyber-physische Systeme (CPS) unterstützt, in denen Agenten mit ihrer physischen Umgebung interagieren. Unter anderem wird eine generische, auf dem Situationskalkül basierende, Axiomatisierung von Prozessen beschrieben, in denen Informationen innerhalb von Multiagentensystemen transferiert werden – beispielsweise in Form von Sensor- Messwerten oder Netzwerkpaketen. Dabei werden ausdrücklich die unvermeidbaren stochastischen Effekte und Echtzeitanforderungen in cyber-physischen Systemen berücksichtigt. Um statistisches Model Checking mit SALMA auch für komplexere Problemstellungen zu ermöglichen, wurde ein Mechanismus für die effiziente Auswertung von prädikatenlogischen LTL-Formeln entwickelt. Insbesondere beinhaltet der vorgestellte Algorithmus eine Intervall-basierte Repräsentation des Auswertungszustands, sowie einige andere Optimierungsansätze zur Vermeidung von unnötigen Berechnungsschritten. Insgesamt war es das Ziel dieser Dissertation, eine Lösung für Simulation und statistisches Model Checking zu schaffen, die einerseits auf fundierten logischen und statistischen Grundlagen aufbaut, auf der anderen Seite jedoch auch pragmatischen Gesichtspunkten wie Benutzbarkeit oder Erweiterbarkeit genügt. Tatsächlich legen erste Ergebnisse und Erfahrungen aus mehreren kleinen bis mittelgroßen Experimenten nahe, dass SALMA diesen Zielen gerecht wird

Theoretical and Practical Advances in Computer-based Educational Measurement

This open access book presents a large number of innovations in the world of operational testing. It brings together different but related areas and provides insight in their possibilities, their advantages and drawbacks. The book not only addresses improvements in the quality of educational measurement, innovations in (inter)national large scale assessments, but also several advances in psychometrics and improvements in computerized adaptive testing, and it also offers examples on the impact of new technology in assessment. Due to its nature, the book will appeal to a broad audience within the educational measurement community. It contributes to both theoretical knowledge and also pays attention to practical implementation of innovations in testing technology

Metalearning

This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence