When designing decentralised autonomic computing systems, a fundamental engineering issue is to assess system-wide behaviour. Such decentralised systems are characterised by the lack of global control, typically consist of autonomous cooperating entities, and often rely on self-organised emergent behaviour to achieve the requirements. A well-founded and practically feasible approach to study overall system behaviour is a prerequisite for successful deployment. On one hand, formal proofs of correct behaviour and even predictions of the exact system-wide behaviour are practically infeasible due to the complex, dynamic, and often non-deterministic nature of self-organising emergent systems. On the other hand, simple simulations give no convincing arguments for guaranteeing system-wide properties. We describe an alternative approach that allows to analyse and assess trends in system-wide behaviour, based on so-called “equation-free” macroscopic analysis. This technique yields more reliable results about the system-wide behaviour, compared to mere observation of simulation results, at an affordable computational cost. Numerical algorithms act at the system-wide level and steer the simulations. This allows to limit the amount of simulations considerably. We illustrate the approach by studying a particular system-wide property of a decentralised control system for Automated Guided Vehicles and we outline a road map towards a general methodology for studying decentralised autonomic computing systems.

The goal of engineering self-organising emergent systems is to acquire a macroscopic system behaviour solely from autonomous local activity and interaction. Due to the non-deterministic nature of such systems, it is hard to guarantee that the required macroscopic behaviour is achieved and maintained. Before even considering a self-organising emergent system in an industrial context, e.g. for Automated Guided Vehicle (AGV) transportation systems, such guarantees are needed. An empirical analysis approach is proposed that combines realistic agent-based simulations with existing scientific numerical algorithms for analysing the macroscopic behaviour. The numerical algorithm itself obtains the analysis results on the fly by steering and accelerating the simulation process according to the algorithm’s goal. The approach is feasible, compared to formal proofs, and leads to more reliable and valuable results, compared to mere observation of simulation results. Also, the approach allows to systematically analyse the macroscopic behaviour to acquire macroscopic guarantees and feedback that can be used by an engineering process to iteratively shape a self-organising emergent solution.

Abstract — We present an ant model that solves a discrete foraging problem. We describe simulations and provide a complete convergence analysis: we show that the ant population computes the solution of some optimal control problem and converges in some well defined sense. We discuss the rate of convergence with respect to the number of ants: we give experimental and theoretical arguments that suggest that this convergence rate can be superlinear with respect to the number of agents. Furthermore, we explain how this model can be extended in order to solve optimal control problems in general and argue that such an approach can be applied to any problem that involves the computation of the fixed point of a contraction mapping. This allows to design a large class of formally well understood ant like algorithms for problem solving. I.

Self organization has long been a matter of immense interest and research in sociology, anthropology, physics and many other fields. Though the principles of self organization can be inducted from almost all walks of human, plant and animal lives (Charlotte K. Hemelrijk, 2002) it is most evident in ant and termites colonies. Self organization has been used to explain the formation of cities, software, brain cells and many natural phenomena in human societies. Though the principle is used to explain other phenomena, the concept itself is continuously evolving. Application of the theory into human affairs has been a less explored area. However, there has been some seminal works on understanding organisations through the idea of self-organisation. Seel (2001, 2003) is foremost amongst them. His work serves as the basic framework of this paper. Seel has identified many factors that are necessary conditions for building self-organised entities. However, Seel’s framework is incomplete, as the effect of physical environment on the formation and functioning of self organised teams has not been considered. This paper attempts to incorporate environmental effects on self-organizing teams. The paper also raises questions on the limitation regarding lack of

Definitions of autonomous agents---simple living systems--- have often included the thermodynamic idea that agents need to do work to construct constraints and to maintain these constraints. We show that an ant-pheromone system is sufficiently rich to allow an explanation of its behavior in terms of thermodynamic properties, particularly the work done by the system on itself to build structure. This work is done by an inward pressure whose origin is the antpheromone system itself. The degree of structure formed by the system can be traced back to the behavioral rules of each of the ants---the more relevant information each ant is given in its behavioral repertoire, the more work is done by the system in compressing itself, and the tighter and more constrained is the final structure. The language introduced in this paper can serve as a framework for quantifying the propensity of a system to do work, self-organize and coalesce into life.

When engineering different decentralised autonomic computing solutions for a certain application there is a lack of a common and usable evaluation and comparison approach. Useful metrics and measurements are mostly domain-specific and are hard to generalise. This paper proposes to use radar charts which allow to apply these domain-specific metrics in an overall evaluation and comparison approach. As such, multiple solutions can be compared with each other and evaluated along multiple dimensions that are important for evaluating decentralised autonomic systems. The approach is illustrated in mobile ad-hoc network management.

This paper explores current metaphors for visualizing agent-based models. Metaphors include grid, network, n-dimensional cubes and landscape visualization techniques. A final section offers some theory underlying visualization of complex systems models with emphasis on mappings to non-equilibrium systems; conserved quantities and their flows; identifying order parameters and control parameters; and the presentation of phase transitions. 1

A major challenge within open markets is the ability to satisfy service demand with an adequate supply of service providers, especially when such demand may be volatile due to changing requirements, or fluctuations in the availability of services. Ideally, this supply and demand should be balanced; however, when consumer demand changes over time, and providers independently choose which services they provide, a coordination problem known as ‘herding’ can arise bringing instability to the market. This behavior can emerge when consumers share similar preferences for the same providers, and thus compete for the same resources. Likewise, providers which share estimates of fluctuating demand may respond in unison, withdrawing some services to introduce others, and thus oscillate the available supply around some ideal equilibrium. One approach to avoid this unstable behavior is to limit the flow of information