Covers advancements in spacecraft and tactical and strategic missile systems, including subsystem design and application, mission design and analysis, materials and structures, developments in space sciences, space processing and manufacturing, space operations, and applications of space technologies to other fields.

The move from platform centric planning to network enabled capability poses not only organisational challenges but also challenges to the technology that will be needed to enable networked operations. Within this context, this paper outlines the central role that autonomous software agents, that coordinate their activities in flexible ways, have to play in providing robust system solutions. The major research challenges are addressed in order to take this promising technology forward and to make it suitable for NEC application. The interactions of the various autonomous agents within the system, that are necessary in order to achieve their individual and collective aims, can be analysed and designed using techniques from Game Theory and Mechanism Design. Game theory is exploited because it has developed powerful tools for analysing decision making in decentralised open systems with multiple autonomous agents. Recently, these tools have been tailored to computational settings to provide a principled foundation for building multiple agent systems. This tailoring gives rise to the field of computational mechanism design. A whole system approach to the requirements and the assessment of the technologies has been adopted. Of prime important for driving this research programme are: • Information Assurance: Currently in networked systems, security and trust are key issues with no robust solution. Therefore assurance of information over dynamic networks requires tackling dynamic and cross coalition, multi-level security systems. • Self-management of Sensor Networks: The complexity of future systems means that a degree of sub-system autonomy will be required for self-management of the networks and its assets.

We begin by describing the importance of emergence in industry and the need, in certain situations, to move away from a reduction mind-set to a more holist approach. We define the term emergence in context of self-organizing systems, autopoiesis and chaotic systems. We then examine a field that is commonly used to explore emergence and self-organization, namely agent and multi-agent systems. After an overview of this field, we highlight the most appropriate aspects of agent research used in aiding the understanding of emergence. We conclude with an example of our recent research where we measure agent emergent performance and flexibility and relate it to the make-up of the agent organization

The aerospace industry is at the forefront of technological innovation, both at product level and manufacturing and support levels. We draw upon our experience in this sector to illustrate the increasing challenges that large-scale complex organizations, exemplified by this sector, are facing. We examine why traditional methodologies are no longer globally appropriate and discuss how work on multi agent systems and emergence is promising the means to overcome the limitations of traditional approaches. Furthermore, we draw upon our research on relating organizational structure to performance to illustrate how such potential solutions can be applied to organizational complexity. Finally, we conclude by looking at the future of this industry and the technological solutions that may play a part in its evolution

The structure and performance of organisations – natural or man-made – are intricately linked, and these multifaceted interactions are increasingly being investigated using Multi Agent System concepts. This paper shows how a selection of generic structural metrics for organisations can be explored using a combination of Pareto Frontier exemplars; extensive simulations of simple goal-orientated Multi Agent Systems, and exposé of organisational types through Self-Organising Map clusters can provide insights into desirable structures for such objectives as robustness and efficiency.

Within a Multi Agent System (MAS) environment, principled metrics are developed that encapsulate the structure and performance of organizations. From extensive simulation work, we can explore performance/cost/structure trade-offs; and, by incorporating data visualization techniques, we can observe the emergence of organization classes and begin to identify optimum organizational structures to meet specified constraints and tasks. We illustrate our approach through specific examples and suggest future directions.

The classification of image regions of interest in an image is an important area of research. Generally most investigations concentrate on the optimisation of the constituent parts of the system without regard to the overall performance. This work takes a system centred approach. Using a novel multi-class receiver operating characteristic, which also allows for the inherent uncertaintypresent, it is shownthat the influenceof differentregionbased segmentation algorithms on the performance of classification algorithms can be determined. The results generated, using this approach, for an airborne infrared application highlight the non-linear relationship between the constituent algorithms and show quantitatively that the system performance can be strongly class and segmenter/classifier dependent.

As many systems and organisations are migrating from centralised control to a distributed one, there is a need for a better understanding of organisational behaviour. The implications of such a trend are discussed with reference to current agent technology. A custom simulation, together with some preliminary results, for the investigation of organisational scenarios is presented. Finally, the development of suitable metrics to quantify the efficiency and effectiveness of differing control mechanisms is considered.

An alternative approach to learning decision strategies in multi-state multiple agent systems is presented here. The method, which uses a game theoretic construction which is model free and does not rely on direct communication between the agents in the system. Limited experiments show that the method can find Nash equilibrium point for 3 player multi-stage game and converges more quickly than a comparable co-evolution method.

This paper presents a Bayesian network framework for situation assessment. The framework is generated from a set of technical requirements that would be a prerequisite for any situation assessment system. It is shown that Bayesian networks readily satisfy these requirements and produce a system that readily fits into the Endsley (1995) description of situation assessment. The framework can also be seen as part of the observe and orientate components of the OODA loop paradigm.

This paper will focus on relating organisational structure with organisational performance. We first outline the motivation behind this research, from both industrial and academic perspectives. After defining the problem and the research aim, an outline of organisational performance metrics is provided, followed by a detailed look at the centralisation metric. Finally, using our testbed simulation, the metric is applied and compared against the simulation’s performance output, namely speed and robustness. We show that while the centralisation metric is a sufficient measure of performance, the implementation of further metrics should produce further promising results.

We present here a novel use of Gaussian processes for the fusion of results of computational simulations with varying degrees of accuracy and computational loads. We demonstrate the efficacy of the approach on one toy problem and two real word examples.

In adaptive systems that involve large numbers of entities, emergent, global behaviours that arise from localised interactions are a critical concept. Understanding and shaping emergence may be essential to such systems’ success. To aid in this understanding, this paper introduces a measure gleaned from non-linear systems theory. The paper discusses how this measure can be used in reinforcing self organising behaviours in adaptive systems. Further, it is shown that the measure can be successfully employed as feedback to a system employing evolutionary computation (EC) and using this to design in desired self organising behaviours in an approximation to a biological plausible collective system.

An alternative approach to learning decision strategies in multi-state multiple agent systems is presented here. The method, which uses a game theoretic construction of “best response with error” does not rely on direct communication between the agents in the system. Limited experiments show that the method can find Nash equilibrium points at least for a 2 player multi-stage coordination game and converges more quickly than a comparable co-evolution method.

In adaptive systems that involve large numbers ofagents, emergent, global behaviours that arise fromlocal agent interactions are a critical concept. In nature,such behaviours are central complex group behavioursthat must arise from individuals that evolve selfishly. Inartificial systems that mimic these adaptive, multi-agentmodels, understanding and shaping emergence may beessential to such systems" success. To aid in thisunderstanding, this paper introduces a measure gleanedfrom...

The development of image processing/computer vision algorithms is often limited by the data available to the researcher, in terms of both quality and quantity. This paper describes an extensive image data base, the Sowerby Image Database (SID), which has been specifically designed to facilitate rigorous algorithm development and testing in the areas of image segmentation and image labelling. Details are given of the database content, the process by which it was constructed and some preliminary results from its use

We have shown how four sensors can be used to investigate the location of acoustic emission in aerospace structures and how the effective propagation speed can be calculated on an event by event basis. We have also investigated the possible multiplicity of solutions and used a sensitivity analysis to obtain a maximum entropy deconvolution of an acoustic emission map. We have finally shown how embedding methods can be used to divide the signal into separate components which can be treated as “virtual” sensors. (5 pages)