Evolving functional and structural dynamism in coupled boolean networks

© 2014 Massachusetts Institute of Technology. This article uses a recently presented abstract, tunable Boolean regulatory network model to further explore aspects of mobile DNA, such as transposons. The significant role of mobile DNA in the evolution of natural systems is becoming increasingly clear. This article shows how dynamically controlling network node connectivity and function via transposon-inspired mechanisms can be selected for to significant degrees under coupled regulatory network scenarios, including when such changes are heritable. Simple multicellular and coevolutionary versions of the model are considered

Evolving Gene Regulatory Networks with Mobile DNA Mechanisms

This paper uses a recently presented abstract, tuneable Boolean regulatory network model extended to consider aspects of mobile DNA, such as transposons. The significant role of mobile DNA in the evolution of natural systems is becoming increasingly clear. This paper shows how dynamically controlling network node connectivity and function via transposon-inspired mechanisms can be selected for in computational intelligence tasks to give improved performance. The designs of dynamical networks intended for implementation within the slime mould Physarum polycephalum and for the distributed control of a smart surface are considered.Comment: 7 pages, 8 figures. arXiv admin note: substantial text overlap with arXiv:1303.722

On the evolution of Boolean networks for computation: A guide RNA mechanism

© 2015 Taylor & Francis. There is a growing body of work within computational intelligence which explores the use of representations inspired by the genetic regulatory networks of biological cells. This paper uses a recently presented abstract, tunable model of such networks to investigate how their design through simulated evolution is affected through the ability to dynamically rewire connectivity. The contextual editing of transcribed RNA by other molecules such that the form of the final product differs from that specified in the corresponding DNA sequence is ubiquitous. It is here shown that a guide RNA-inspired editing mechanism can be selected for under various scenarios

Evolutionary acquisition of complex traits in artificial epigenetic networks

How complex traits arise within organisms over evolutionary time is an important question that has relevance both to the understanding of biological systems and to the design of bio-inspired computing systems. This paper investigates the process of acquiring complex traits within epiNet, a recurrent connectionist architecture capable of adapting its topology during execution. Inspired by the biological processes of gene regulation and epigenetics, epiNet captures biological organisms’ ability to alter their regulatory topologies according to environmental stimulus. By applying epiNet to a series of computational tasks, each requiring a range of complex behaviours to solve, and capturing the evolutionary process in detail, we can show not only how the physical structure of epiNet changed when acquiring complex traits, but also how these changes in physical structure affected its dynamic behaviour. This is facilitated by using a lightweight optimisation method which makes minor iterative changes to the network structure so that when complex traits emerge for the first time, a direct lineage can be observed detailing exactly how they evolved. From this we can build an understanding of how complex traits evolve and which regulatory environments best allow for the emergence of these complex traits, pointing us towards computational models that allow more swift and robust acquisition of complex traits when optimised in an evolutionary computing setting

Engineering behavioural differentiation in robots controlled by Boolean networks

The design of control software for robots that are required to face different and unpredictable environmental conditions is of paramount importance in current robotic research. A viable solution to attain such a control software consists in exploiting the rich dynamics of biological cell models; indeed, cells are capable of differentiating into specific types, each characterized by peculiar behavioural traits suited to the particular environmental condition in which the cell acts. Moreover, if properly triggered, cells can also undergo type changes. Inspired by this phenomenon, in this work we have devised a method to support the automatic design of robots controlled by Boolean networks (BNs), which are a notable model of genetic regulatory networks. The initial behaviour of the robot is not specific, i.e. its BN is in an undifferentiated state. When specific environmental conditions appear, the BN changes its dynamics that in turn induces a specific behaviour in the robot. If, subsequently, the environmental signals change, the robot is able to return to the initial, undifferentiated behaviour and then differentiate again into a different behaviour, according to the external signals. This method is shown in detail, along with a thorough experimental analysis, in a case study involving taxis behaviours

Fostering Distributed Business Logic in Open Collaborative Networks: an integrated approach based on semantic and swarm coordination

Given the great opportunities provided by Open Collaborative Networks (OCNs), their success depends on the effective integration of composite business logic at all stages. However, a dilemma between cooperation and competition is often found in environments where the access to business knowledge can provide absolute advantages over the competition. Indeed, although it is apparent that business logic should be automated for an effective integration, chain participants at all segments are often highly protective of their own knowledge. In this paper, we propose a solution to this problem by outlining a novel approach with a supporting architectural view. In our approach, business rules are modeled via semantic web and their execution is coordinated by a workflow model. Each company’s rule can be kept as private, and the business rules can be combined together to achieve goals with defined interdependencies and responsibilities in the workflow. The use of a workflow model allows assembling business facts together while protecting data source. We propose a privacy-preserving perturbation technique which is based on digital stigmergy. Stigmergy is a processing schema based on the principle of self-aggregation of marks produced by data. Stigmergy allows protecting data privacy, because only marks are involved in aggregation, in place of actual data values, without explicit data modeling. This paper discusses the proposed approach and examines its characteristics through actual scenarios

Developing a global observer programming model for large-scale networks of autonomic systems

Computing and software intensive systems are now an inextricable part of modern work, life and entertainment fabric. This consequently has increased our reliance on their dependable operation. While much is known regarding software engineering practices of dependable software systems; the extreme scale, complexity and dynamics of modern software has pushed conventional software engineering tools and techniques to their acceptable limits. Consequently, over the last decade, this has accelerated research into non-conventional methods, many of which are inspired by social and/or biological systems model. Exemplar of which are the DARPA-funded Se1f-Regenerative-Systems (SRS) programme, and Autonomic Computing, where a closed-loop feedback control model is essential to delivering the advocated cognitive immunity and self-management capabilities. While much research work has been conducted on vanous aspects of SRS and autonomy, they are typically based on the assumptions that the structural model (organisation) of managed elements is static and exhaustive monitoring and feedback is computationally scalable. In addition, existing federated approaches to distributed computation and control, such as Multi-Agent-Systems fail to satisfactorily address how global control may be enacted upon the whole system and how an individual component may take on specified monitoring duties - although methods of interaction between federated individuals is well understood. Equally, organic-inspired computing looks to deal with event scale and complexity largely from a mining perspective, with observation concerns deferred to a suitably selective abstraction known as the "observation model". However, computing and mathematical science research, along with other fields has developed problem-specific approaches to help manage complexity; abstraction-based approaches can simplify structural organisation allowing the underlying meaning to be better understood. Statistical and graph-based approaches can both provide identifying features along with selectively reducing the size of a modelled structure by selecting specific areas that conform to certain topological criteria. This research studies the engineering concerns relating to observation of large-scale networks of autonomic systems. It examines methods that can be used to manage scale and generalises and formalises them within a software engineering approach; guiding the development of an automated adaptive observation subsystem - the Global Observer Model. This approach uses a model-based representation of the observed system, represented by appropriately attached modelled elements; adapters between the underlying system and the observation subsystem. The concepts of Signature and Technique definitions describe large-scale or complex system characteristics and target selection techniques respectively. Collections of these objects are then utilised throughout the framework along with decision and deployment logic (collectively referred to as the Observer Behaviour Definition - an ECA-like observational control) to provide a runtime-adaptable observation overlay. The evaluation of this research is provided by demonstrations of the observation framework; firstly in experimental form for assessment of the Signature and Technique approach, and then by application to the Email Exploration Tool (EET), a forensic investigation utility

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