Catálogo de publicaciones - libros

A clear terminology is essential in every research discipline. In the context of ESOA, a lot of confusion exists about the meaning of the terms emergence and self-organisation. One of the sources of the confusion comes from the fact that a combination of both phenomena often occurs in dynamical systems. In this paper a historic overview of the use of each concept as well as a working definition, that is compatible with the historic and current meaning of the concepts, is given. Each definition is explained by supporting it with important characteristics found in the literature. We show that emergence and self-organisation each emphasise different properties of a system. Both phenomena can exist in isolation. The paper also outlines some examples of such systems and considers the combination of emergence and self-organisation as a promising approach in complex multi-agent systems.

We describe an analytic approach, multiscale analysis, that can demonstrate the fundamental limitations of decomposition based engineering for the development of highly complex systems. The planning based process is limited by the interdependence of components and communication between design teams. Thus, the construction of many highly complex systems should be pursued by strategies modeled after biological evolution, or market economies, where extensive planning is forsaken and multiple parallel design efforts compete for adoption through testing in actual use.

Positioned at the confluence between human/machine and hardware/software integration and backed by a solid proof of concept realized through several scenarios encompassing e-Securities, e-Health, and e-Logistics for global manufacturing and emergency response management, this work exploits latest advances in information and networking technologies to set a systematic framework for the design of the information infrastructures (coined as AIIs – Adaptive Information Infrastructures) destined to fuel tomorrow’s e-Society. Designed following the natural laws of evolution, which merge self-organization and natural selection [1], these socially embedded information infrastructures can adapt to fulfill various needs as their environment demands. Computational intelligence techniques endow the AIIs with learning and discovery capabilities, emulating social and biological behavior. AIIs are destined to become an integral part of our life by supporting, rather than disturbing, a framework that facilitates strategic partnerships while providing greater user-friendliness, more efficient services support, user-empowerment, and support for human interactions.

It is our belief that modelling the behaviour of stem cells in the adult human body as an agent-based system is the most appropriate way of understanding the process of self-organisation. We have undertaken several case studies where formal and/or computational models of stem cell systems, have been re-developed using an agent-based approach. This paper presents details of one of these case studies where we have used an agent-based approach as opposed to a cellular automata approach. A formalisation of the non-agent and agent-based approach is given, and from the results of this investigation, we aim to demonstrate the advantages of the agent-based approach for developing biologically plausible models with emergent self-organising dynamics. The aim of this paper first to discuss the importance of modelling and simulating stem cells, because of certain experimental limitations, but also to demonstrate that the multi-agent approach to modelling is the most appropriate.

One of the current trends in computer science leads to the design of computing organizations based on the activity of a multitude of tiny cheap decentralized computing entities. Whether these chips are integrated into paintings or disseminated in open environments like dust, the fundamental problem lies in their cooperative operation so that global functions are obtained collectively. In this paper, we address the issue of the creation of visual ambiences based on the coordinated activity of computing entities. These entities are distributed randomly on a 2D canvas and can only change their own color and perceive their immediate neighbors.

The ‘engineering’ and ‘adaptive’ approaches to system production are distinguished. It is argued that producing reliable self-organised software systems (SOSS) will necessarily involve considerable use of adaptive approaches. A class of apparently simple multi-agent systems is defined, which however has all the power of a Turing machine, and hence is beyond formal specification and design methods (in general). It is then shown that such systems can be evolved to perform simple tasks. This highlights how we may be faced with systems whose workings we have not wholly designed and hence that we will have to treat them more as natural science treat the systems it encounters, namely using the classic experimental method. An example is briefly discussed. A system for annotating such systems with hypotheses, and conditions of application is proposed that would be a natural extension of current methods of open source code development.

Contemporary software systems are exposed to demanding, dynamic, and unpredictable environments where the traditional adaptability mechanisms may not be sufficient. To imitate and fully benefit from life-like adaptability in software systems that might come closer to the complexity levels of biological organisms, we seek a formal mathematical model of certain fundamental concepts such as: life, organism, evolvability and adaptation. In this work we concentrate on the concept of software evolvability. Our work proposes an evolutionary computation model, based on the theory of hypercycles and autopoiesis. The intrinsic properties of hypercycles allow them to evolve into higher levels of complexity, analogous to multi-level, or hierarchical evolutionary processes. We aim to obtain structures of self-maintaining ensembles, that are hierarchically organised, and our primary focus is on such open-ended hierarchically organised evolution.

For Peer-2-Peer (P2P) networks to realize their full potential the nodes they are composed of need to coordinate and cooperate, to improve the performance of the network as a whole. This requires the suppression of selfish behavior (free-riding). Existing P2P systems often assume that nodes will behave altruistically, but this has been shown to be far from the case. We outline encouraging results from a P2P simulation that translates and applies the properties of “tag” models (initially developed within social simulations) [8, 9] to tackle these issues. We find that a simple node rewiring policy, based on the tag dynamics, quickly eliminates free-riding without centralized control. The process appears highly scalable and robust.

We present a programming approach to let a multitude of simple mobile computational “particles” (i.e. sorts of tiny mobile robots) to self-organize their respective locations to assume a coherent global formation (i.e. shape). The problem has a variety of applications in mobile robotics, modular robots, sensor networks, and computational self-assembly. Here we show how the TOTA (“Tuples On The Air”) middleware can be effectively exploited to enable self-organization of spatial shapes in mobile particles with minimal capabilities. The key idea in TOTA is to rely on spatially distributed tuples, spread across the network, to drive particles’ movements and activities. Several experiments are reported showing the effectiveness of the approach.

In this paper we present a general design methodology suitable for a class of complex multi-agent systems which are capable of self-assembly. Our methodology is based on a top-down, bottom-up approach, which has the potential to achieve a range of global design goals whilst retaining emergent behaviour somewhere in the system, and thereby allowing access to a richer solution space. Our experimental environment is a software system to model 2-dimensional self-assembly of groups of autonomous agents, where agents are defined as square smart blocks. The general design goal for such systems is to direct the self-assembly process to produce a specified structure. The potential of this design methodology has been realised by demonstrating its application to a “toy” problem – the self-assembly of rectangles of different sizes and shapes in a two-dimensional mesoblock environment. The design procedure shows different choices available for decomposing a system goal into subsidiary goals, as well as the steps needed to ensure a match to what is achievable from the bottom-up process. Encouraging results have been obtained, which allows mesoblock rectangles of specified size to be assembled in a directed fashion. Two different approaches to the same problem were presented, showing the flexibility of the method.