Distributed Control of Emergence: Local and Global Anti-Component Strategies in Particle Swarms and Ant Colonies

Brintrup, Alexandra; Gong, Tao; Ligtvoet, Andreas; Davis, Chris; Willigen, Willem van; Robinson, Edward

doi:10.1109/saso.2009.6

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…However, central control is not always feasible. For example, when the problem is dynamic, and messaging has to be kept to a minimum, distributed control represents a well-suited alternative [3]. At the distributed end of the spectrum, each component acts with limited local knowledge, and operational efficiency cannot be guaranteed to emerge from system behavior.…”

Section: Related Workmentioning

confidence: 99%

Dependable Risk-Aware Efficiency Improvement for Self-Organizing Emergent Systems

Hudson

Denzinger

Kasinger

et al. 2011

2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems

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An efficiency improvement advisor agent acts as a consultation service for a self-organizing multi-agent system that improves operational efficiency. It identifies recurrent tasks in past problems that allow the creation of so-called exception rules for individual agents to limit future inefficient behavior. There exists the danger that introduced rules could possibly infringe on the flexibility and therefore reliability of the system. In this paper, we present a dependable riskaware efficiency improvement advisor that uses Monte Carlo simulation techniques in strategic analysis assessing the longterm potential and risks of prospective rules. Our experimental evaluation, for the domain of dynamic pickup and delivery problems, shows that the result is a minimal, yet effective, set of risk-averse exception rules. These rules can be provided to individual agents to reliably achieve an overall long-term improvement in efficiency while maintaining flexibility.

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Section: Related Workmentioning

confidence: 99%

Dependable Risk-Aware Efficiency Improvement for Self-Organizing Emergent Systems

Hudson

Denzinger

Kasinger

et al. 2011

2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems

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“…In our opinion, the work on regulated norm-based multiagent systems and electronic institutions (Jones et al, 2013;Pitt et al, 2012) can be an effective starting point towards achieving predictable and controllable behaviors in urban superorganisms. In addition, those works proposing special classes of control agents to be injected in order to act as leader and affect the global behaviors of the multi-agent system (Brintrup et al, 2009;Caprari et al, 2005;Scheidler et al, 2013), can be promising as well.…”

Section: Predicting and Controlling Emergent Behaviorsmentioning

confidence: 99%

“…To control emergent behaviors, one can think at deploying in the infrastructure special classes of agents that, by spreading "fake" LSAs that have the only goal of triggering some coordination-laws, eventually affecting the way coordination laws apply to other LSAs of other agents (Brintrup et al, 2009;Halloy et al, 2013;Scheidler et al, 2013). The result could be in an overall adaptation of the behavior of the superorganism, yet obtained in a fully decentralized way.…”

Section: Addressing the Challengesmentioning

confidence: 99%

Engineering self-organizing urban superorganisms

Zambonelli

2015

Engineering Applications of Artificial Intelligence

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Progresses in ubiquitous, embedded, and social networking and computing make possible for people in urban areas to dynamically interact with each other and with ICT devices around. This can result in a system with a very large number of agents working together in an orchestrated and self-organizing way to achieve specific urban-level goals, i.e., as if they were a “superorganism”. In this paper, we sketch the future vision of urban superorganisms and overview some emerging application areas heading towards the vision. Following, we identify the key challenges in engineering self-organizing multi-agent systems that can work as a superorganism, i.e., seamlessly involving ICT agents and human agents so to achieve some required urban level goals. Finally, we introduce the reference architecture for an infrastructure to support our future vision of self-organizing urban superorganisms

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“…It should be noted that the swarm controlled emergence approach as proposed in our preliminary paper (Merkle et al , 2007) has inspired other authors to apply this approach. In Brintrup et al (2008) it was used to control the self‐organized usage of a distributed resource by a swarm of agents. The anti‐agents are used to prevent a situation where the normal agents all use the resource in the same area.…”

Section: Introductionmentioning

confidence: 99%

Swarm controlled emergence for ant clustering

Scheidler¹,

Merkle²,

Middendorf³

2013

International Journal of Intelligent Computing and Cybernetics

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Purpose: Swarm controlled emergence is proposed as an approach to control emergent effects in (artificial) swarms. The method involves the introduction of specific control agents into the swarm systems. Control agents behave similar to the normal agents and do not directly influence the behavior of the normal agents. The specific design of the control agents depends on the particular swarm system considered. The aim of this paper is to apply the method to ant clustering. Ant clustering, as an emergent effect, can be observed in nature and has inspired the design of several technical systems, e.g. moving robots, and clustering algorithms. Design/methodology/approach: Different types of control agents for that ant clustering model are designed by introducing slight changes to the behavioural rules of the normal agents. The clustering behaviour of the resulting swarms is investigated by extensive simulation studies. Findings: It is shown that complex behavior can emerge in systems with two types of agents (normal agents and control agents). For a particular behavior of the control agents, an interesting swarm size dependent effect was found. The behaviour prevents clustering when the number of control agents is large, but leads to stronger clustering when the number of control agents is relatively small. Research limitations/implications: Although swarm controlled emergence is a general approach, in the experiments of this paper the authors concentrate mainly on ant clustering. It remains for future research to investigate the application of the method in other swarm systems. Swarm controlled emergence might be applied to control emergent effects in computing systems that consist of many autonomous components which make decentralized decisions based on local information. Practical implications: The particular finding, that certain behaviours of control agents can lead to stronger clustering, can help to design improved clustering algorithms by using heterogeneous swarms of agents. Originality/value: In general, the control of (unwanted) emergent effects in artificial systems is an important problem. However, to date not much research has been done on this topic. This paper proposes a new approach and opens a different research direction towards future control principles for self-organized systems that consist of a large number of autonomous components

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Distributed Control of Emergence: Local and Global Anti-Component Strategies in Particle Swarms and Ant Colonies

Cited by 9 publications

References 5 publications

Dependable Risk-Aware Efficiency Improvement for Self-Organizing Emergent Systems

Dependable Risk-Aware Efficiency Improvement for Self-Organizing Emergent Systems

Engineering self-organizing urban superorganisms

Swarm controlled emergence for ant clustering

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