SELF-ORGANIZATION AND EMERGENCE PHENOMENA IN WIKIPEDIA AND FREE SOFTWARE DEVELOPMENT USING MASOES

This work models Wikipedia and Free Software Development through a mul- tiagent architecture for self-organizing and emergent systems called MASOES without mathematically representing the system. In that sense, each component, mechanism and process of MASOES is instanced at individual and collective levels by the observed phenomena at the modeled systems. Thus, this paper proposes a methodology to show how to model real systems using MASOES, in order to study their self-organizing and emergent properties and, later on, to facilitate the verication of these properties, mechanisms, components and social interactions for promoting collaborative work and sharing individual and collective knowledge in these systems. Keywords : Multiagent systems, Self-Organization, Emergent Systems, Wikipedia, Free Software DevelopmentRESUMEN AUTO-ORGANIZACI 3N Y EMERGENCIA EN WIKIPEDIA Y EL DESARROLLO DEL SOFTWARE LIBRE A TRAV\uc9S DE MASOESEste trabajo modela el comportamiento de Wikipedia y el desarrollo de Software Libre, a trav\ue9s de una arquitectura multiagente para sistemas emergentes y auto- organizados llamada MASOES, sin especicar matem\ue1ticamente el sistema. En ese sentido, cada componente, mecanismo y proceso de MASOES se instancia a nivel individual y colectivo en cada uno de los sistemas modelados. As\ued, en este trabajo se propone una metodolog\ueda para mostrar como modelar sistemas reales utilizando MASOES, con el fin de estudiar sus propiedades emergentes y auto-organizadas, y posteriormente, facilitar la verificaci\uf3n de estas propiedades, mecanismos, componentes e interacciones sociales para promover el trabajo colaborativo y el intercambio del conocimiento individual y colectivo en estos sistemas. Palabras claves del autor: Sistemas Multiagente, Auto-Organizaci\uf3n, Sistemas Emergentes, Wikipedia, Desarrollo de Software Libre<br

Auto-organización y emergencia en Wikipedia y el desarrollo del software libre a través de Masoes

This work models Wikipedia and Free Software Development through a multiagent architecture for self-organizing and emergent systems called MASOES without mathematically representing the system. In that sense, each component, mechanism and process of MASOES is instanced at individual and collective levels by the observed phenomena at the modeled systems. Thus, this paper proposes a methodology to show how to model real systems using MASOES, in order to study their self-organizing and emergent properties and, later on, to facilitate the verification of these properties, mechanisms, components and social interactions for promoting collaborative work and sharing individual and collective knowledge in these systems.Este trabajo modela el comportamiento de Wikipedia y el desarrollo de Software Libre, a través de una arquitectura multiagente para sistemas emergentes y auto-organizados llamada MASOES, sin especificar matemáticamente el sistema. En ese sentido, cada componente, mecanismo y proceso de MASOES se instancia a nivel individual y colectivo en cada uno de los sistemas modelados. Así, en este trabajo se propone una metodología para mostrar como modelar sistemas reales utilizando MASOES, con el fin de estudiar sus propiedades emergentes y auto-organizadas, y posteriormente, facilitar la verificación de estas propiedades, mecanismos, componentes e interacciones sociales para promover el trabajo colaborativo y el intercambio del conocimiento individual y colectivo en estos sistemas

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Digital ecosystems

We view Digital Ecosystems to be the digital counterparts of biological ecosystems, which are considered to be robust, self-organising and scalable architectures that can automatically solve complex, dynamic problems. So, this work is concerned with the creation, investigation, and optimisation of Digital Ecosystems, exploiting the self-organising properties of biological ecosystems. First, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. We then investigated its self-organising aspects, starting with an extension to the definition of Physical Complexity to include the evolving agent populations of our Digital Ecosystem. Next, we established stability of evolving agent populations over time, by extending the Chli-DeWilde definition of agent stability to include evolutionary dynamics. Further, we evaluated the diversity of the software agents within evolving agent populations, relative to the environment provided by the user base. To conclude, we considered alternative augmentations to optimise and accelerate our Digital Ecosystem, by studying the accelerating effect of a clustering catalyst on the evolutionary dynamics of our Digital Ecosystem, through the direct acceleration of the evolutionary processes. We also studied the optimising effect of targeted migration on the ecological dynamics of our Digital Ecosystem, through the indirect and emergent optimisation of the agent migration patterns. Overall, we have advanced the understanding of creating Digital Ecosystems, the self-organisation that occurs within them, and the optimisation of their Ecosystem-Oriented Architecture

Engineering Self-Adaptive Collective Processes for Cyber-Physical Ecosystems

The pervasiveness of computing and networking is creating significant opportunities for building valuable socio-technical systems. However, the scale, density, heterogeneity, interdependence, and QoS constraints of many target systems pose severe operational and engineering challenges. Beyond individual smart devices, cyber-physical collectives can provide services or solve complex problems by leveraging a “system effect” while coordinating and adapting to context or environment change. Understanding and building systems exhibiting collective intelligence and autonomic capabilities represent a prominent research goal, partly covered, e.g., by the field of collective adaptive systems. Therefore, drawing inspiration from and building on the long-time research activity on coordination, multi-agent systems, autonomic/self-* systems, spatial computing, and especially on the recent aggregate computing paradigm, this thesis investigates concepts, methods, and tools for the engineering of possibly large-scale, heterogeneous ensembles of situated components that should be able to operate, adapt and self-organise in a decentralised fashion. The primary contribution of this thesis consists of four main parts. First, we define and implement an aggregate programming language (ScaFi), internal to the mainstream Scala programming language, for describing collective adaptive behaviour, based on field calculi. Second, we conceive of a “dynamic collective computation” abstraction, also called aggregate process, formalised by an extension to the field calculus, and implemented in ScaFi. Third, we characterise and provide a proof-of-concept implementation of a middleware for aggregate computing that enables the development of aggregate systems according to multiple architectural styles. Fourth, we apply and evaluate aggregate computing techniques to edge computing scenarios, and characterise a design pattern, called Self-organising Coordination Regions (SCR), that supports adjustable, decentralised decision-making and activity in dynamic environments.Con lo sviluppo di informatica e intelligenza artificiale, la diffusione pervasiva di device computazionali e la crescente interconnessione tra elementi fisici e digitali, emergono innumerevoli opportunità per la costruzione di sistemi socio-tecnici di nuova generazione. Tuttavia, l'ingegneria di tali sistemi presenta notevoli sfide, data la loro complessità—si pensi ai livelli, scale, eterogeneità, e interdipendenze coinvolti. Oltre a dispositivi smart individuali, collettivi cyber-fisici possono fornire servizi o risolvere problemi complessi con un “effetto sistema” che emerge dalla coordinazione e l'adattamento di componenti fra loro, l'ambiente e il contesto. Comprendere e costruire sistemi in grado di esibire intelligenza collettiva e capacità autonomiche è un importante problema di ricerca studiato, ad esempio, nel campo dei sistemi collettivi adattativi. Perciò, traendo ispirazione e partendo dall'attività di ricerca su coordinazione, sistemi multiagente e self-*, modelli di computazione spazio-temporali e, specialmente, sul recente paradigma di programmazione aggregata, questa tesi tratta concetti, metodi, e strumenti per l'ingegneria di ensemble di elementi situati eterogenei che devono essere in grado di lavorare, adattarsi, e auto-organizzarsi in modo decentralizzato. Il contributo di questa tesi consiste in quattro parti principali. In primo luogo, viene definito e implementato un linguaggio di programmazione aggregata (ScaFi), interno al linguaggio Scala, per descrivere comportamenti collettivi e adattativi secondo l'approccio dei campi computazionali. In secondo luogo, si propone e caratterizza l'astrazione di processo aggregato per rappresentare computazioni collettive dinamiche concorrenti, formalizzata come estensione al field calculus e implementata in ScaFi. Inoltre, si analizza e implementa un prototipo di middleware per sistemi aggregati, in grado di supportare più stili architetturali. Infine, si applicano e valutano tecniche di programmazione aggregata in scenari di edge computing, e si propone un pattern, Self-Organising Coordination Regions, per supportare, in modo decentralizzato, attività decisionali e di regolazione in ambienti dinamici

Networks, complexity and internet regulation: scale-free law

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Internet... the final frontier: an ethnographic account: exploring the cultural space of the Net from the inside

The research project The Internet as a space for interaction, which completed its mission in Autumn 1998, studied the constitutive features of network culture and network organisation. Special emphasis was given to the dynamic interplay of technical and social conventions regarding both the Net’s organisation as well as its change. The ethnographic perspective chosen studied the Internet from the inside. Research concentrated upon three fields of study: the hegemonial operating technology of net nodes (UNIX) the network’s basic transmission technology (the Internet Protocol IP) and a popular communication service (Usenet). The project’s final report includes the results of the three branches explored. Drawing upon the development in the three fields it is shown that changes that come about on the Net are neither anarchic nor arbitrary. Instead, the decentrally organised Internet is based upon technically and organisationally distributed forms of coordination within which individual preferences collectively attain the power of developing into definitive standards. --