Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

/pdf/handbook-of-software-reliability-engineering-32dwvaljmm.pdf

Handbook of software reliability engineering

https://dialnet.unirioja.es/descarga/articulo/4511388.pdf

Rules of encounter: designing conventions for automated negotiation among computers

ion is the selection and (possibly) coarsening (i.e. quantisation) of certain system characteristics (attributes, performance indicators, parameters) from the total set of system characteristics. The abstraction process comprises: • A simplification (Example: The colour Red is an abstraction, which neglects the different possible shades of Red, the wavelengths, the intensity etc.), • an aggregation (Example: ‘Temperature’ condenses the myriad of individual molecule movements in a gas volume into a single number.), • and consequently: loss of information. The opposite of abstraction is concretisation. It comprises: • gain of information, • detailing, • refinement, • disaggregation, • in engineering: the design process. 3.2.2 System Boundary Systems are defined by their system boundary, which makes a distinction between inside and outside possible (i.e. between self and non-self, see Sect. 4.1). When 3.2 What is a System? 87 Fig. 3.3 Trade-off between initial development cost and later adaptation cost systems are designed, we must choose where to place the boundary. This involves a consideration of cost (Fig. 3.3): A narrow boundary will reduce the cost for planning and development in the first place but bears the risk of a high effort in case a subsequent adaptation and extension of the system should be necessary. A wide system boundary reverses the cost curves. Apparently, the total cost minimum lies somewhere in the middle. 3.2.3 Some System Types and Properties Most systems are transient, i.e. they change over time. They are developed, assembled, modified, destroyed. Reactive systems react on inputs (events, signals, sensor data) by applying outputs (events, control signals, commands) to the environment. All open systems are reactive! In a more focused definition: Only systems reacting in real time (within a predefined time) are reactive systems. Such real-time systems are characterised by (1) time restrictions (deadlines), and (2) time determinism (i.e. guarantees). Planned vs. Unplanned systems: During the development process of technical systems, only predictable events are taken into account in the designed core. But there will always be an unpredictable rest. Therefore, the ‘spontaneous closure’ (Fig. 3.4) has to cover these events. In technical systems, the exception handler or the diagnosis system play the role of a simple spontaneous closure. Living systems are characterised by a powerful spontaneous closure. Recurring reactions of the spontaneous closure become part of the designed core. This amounts to learning.

Organic Computing – Technical Systems for Survival in the Real World

Energy Informatics: 4th D-A-CH Conference, EI 2015, Karlsruhe, Germany, November 12-13, 2015, Proceedings

Self-organisation aspects and the large number of entities in Organic Computing (OC) systems make them extremely hard to predict and analyse. However, the application of OC principles to, e.g., safety critical systems, is usually not conceivable without behavioural guarantees. In this article, a rigorous approach called the Restore Invariant Approach is presented, which provides a specification paradigm and a formal framework that allows to give guarantees for a system despite of self-organisation. The approach provides a method for specifying unwanted system states by constraining the system and defining a corridor of correct behaviour. Furthermore, a decentralised algorithm for monitoring and restoring the invariant based on coalition formation is presented.

Constraining Self-organisation Through Corridors of Correct Behaviour: The Restore Invariant Approach

Multi-agent systems often consist of heterogeneous agents with different capabilities and objectives. While some agents might try to maximize their system's utility, others might be self-interested and thus only act for their own good. However, because of their limited capabilities and resources, it is often necessary that agents cooperate to be able to satisfy given tasks. To work together on such a task, the agents have to solve a task allocation problem, e.g., by teaming up in groups like coalitions or distributing the task among themselves on electronic markets. In this paper, we introduce two algorithms that allow agents to cooperatively solve a dynamic task allocation problem in uncertain environments. Based on these algorithms, we investigate the influence of inter-agent variation on the system's behavior. One of these algorithms explicitly exploits inter-agent variation to solve the task without communication between the agents, while the other builds upon a fixed overlay network in which agents exchange information. Throughout the paper, the frequency stabilization problem from the domain of decentralized power management serves as a running example to illustrate our algorithms and results.

On the Influence of Inter-Agent Variation on Multi-Agent Algorithms Solving a Dynamic Task Allocation Problem under Uncertainty

This book treats the computational use of social concepts as the focal point for the realisation of a novel class of socio-technical systems, comprising smart grids, public display environments, and grid computing. These systems are composed of technical and human constituents that interact with each other in an open environment. Heterogeneity, large scale, and uncertainty in the behaviour of the constituents and the environment are the rule rather than the exception. Ensuring the trustworthiness of such systems allows their technical constituents to interact with each other in a reliable, secure, and predictable way while their human users are able to understand and control them. "Trustworthy Open Self-Organising Systems" contains a wealth of knowledge, from trustworthy self-organisation mechanisms, to trust models, methods to measure a user's trust in a system, a discussion of social concepts beyond trust, and insights into the impact open self-organising systems will have on society.

Trustworthy Open Self-Organising Systems

Structuring and controlling distributed power sources by autonomous virtual power plants

Resource allocation is a common problem in technical systems. For instance, the main task in power management systems is to maintain the balance between energy production and consumption at all times. If such a resource allocation problem has to be solved in a multi-agent system in a decentralized or regionalized manner, agents have to rely on cooperation due to their limited resources and knowledge. In open systems, various uncertainties - introduced by the environment as well as the agents' possibly self-interested or even malicious behavior - have to be taken into account to be able to allocate the resources according to the actual demand. Trust has been proposed as a concept to measure and deal with such uncertainties. In this paper, we present a trust- and cooperation-based algorithm that solves a dynamic resource allocation problem in open multi-agent systems. Throughout the paper, the problem of creating power plant schedules in decentralized autonomous power management systems serves as a running example to illustrate our algorithm and results.

A Trust- and Cooperation-Based Solution of a Dynamic Resource Allocation Problem

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