There is little or no guidance to systematically design a selforganising emergent solution that achieves the desired macroscopic behaviour. This paper describes decentralised coordination mechanisms such as digital pheromones as design patterns, similar to patterns used in mainstream software engineering. As a consequence, a structured consolidation of best practice in using each coordination mechanism becomes available to guide engineers in applying them, and to directly decide which mechanisms are promising to solve a certain problem. As such, self-organising emergent solutions can be engineered more systematically, which is illustrated in a packet delivery service application. This document includes extensive pattern descriptions for digital pheromones, gradient fields, market-based coordination, tag-based coordination, and token-based coordination.

In this paper we propose an approach for flexible and decentralized task allocation based on a negotiation protocol and applicable in case of delayed commencement of tasks. Delayed task commencement arises when an agent has to make some e#ort in order to start the task, e.g. a robot first has to move towards the starting position of its task of moving a packet before the task can be performed.

This report describes the current state of research in the area known as Swarm Intelligence. Swarm Intelligence relies upon stigmergic principles in order to solve complex problems using only simple agents. Swarm Intelligence has been receiving increasing attention over the last 10 years as a result of the acknowledgement of the success of social insect systems in solving complex problems without the need for central control or global information. In swarmbased problem solving, a solution emerges as a result of the collective action of the members of the swarm, often using principles of communication known as stigmergy. The individual behaviours of swarm members do not indicate the nature of the emergent collective behaviour and the solution process is generally very robust to the loss of individual swarm members. This report describes the general principles for swarm-based problem solving, the way in which stigmergy is employed, and presents a number of high level algorithms that have proven utility in solving hard optimization and control problems. Useful tools for the modelling and investigation of swarm-based systems are then briefly described. Applications in the areas of combinatorial optimization, distributed manufacturing, collective robotics, and routing in networks (including mobile ad hoc networks) are then reviewed. Military and security applications are then described, specifically highlighting the groups that have been or continue to be active in swarm research. The final section of the document identifies areas of future research of potential military interest. A substantial bibliography is provided in

Abstract: Indirect interactions by olfactory stimuli between living organisms are a powerful mechanism for self-organizing coordination in biology. Various adoptions of this paradigm for computer systems however are mainly based on the usage of digital pheromones, although these chemical substances are only one type that mediate indirect interactions. Biology provides an ingenious diversity of such substances, all grouped by the term semiochemicals. In this paper we adopt the principles behind semiochemical coordination in biology and present a model that defines a coarse-grained architecture of selforganizing computer systems based on indirect interactions. This model allows for any combination of semiochemical coordination mechanisms within one single system architecture, which will pave the way for an easier engineering of selforganizing solutions better adapted to complex problems. We further demonstrate how to efficiently combine different types of semiochemical coordination into one mechanism, based on pollination in biology, and evaluate its application to instances of pickup and delivery problems.

This research examines the feasibility of using a field-based approach, within the multi-agent system (MAS) paradigm, to achieve transport assignment in an automatic guided vehicle (AGV) system. In a field-based approach, transports emit fields into the environment. The AGV’s behavior consists of continuously combining received fields and following the gradient of these combined fields. This will guide towards pick locations of transports, much like a ball rolls towards a valley in a (continuously changing) mountainous landscape. To avoid multiple AGVs driving towards the same transport, AGVs emit repulsive fields. The AGVs continuously reconsider the situation of the environment and transport assignment is delayed until a pickup, which benefits the flexibility of the system. The field-based approach is implemented in the AGV simulator developed by the AgentWise task force and its performance is examined.