Stability of multi-agent systems

Chli, Maria; Wilde, Philippe De; Goossenaerts, J.B.M.; Абрамов, В. А.; Szirbik, Nick; Correia, Luís; Mariano, Pedro; Ribeiro, Richardson

doi:10.1109/icsmc.2003.1243872

Cited by 18 publications

(37 citation statements)

References 13 publications

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“…By using ideas from evolutionary computing, a multiagent system can be seen as a discrete Markov chain and its evolution as a Markov process, possibly with unknown transition probabilities [13]. In a model using this approach, in which the number of agents varies according to the fitness of the individuals, a definition for the degree of instability is proposed based on the entropy of the limit probabilities [34].…”

Section: Related Workmentioning

confidence: 99%

“…In this case, the system can be considered to be stable if the distribution of states converges to an equilibrium distribution; that is, P ( X n = j ) → π j , when n → ∞ . In other words, the system is stable if the probability distribution of system states becomes independent of the time step n , for large values of n [13]. …”

Section: Interpretations Of Stabilitymentioning

confidence: 99%

“…Therefore, stability, intuitively understood as the property of a system to exhibit bounded behaviour [13], is one of the most desired features in multiagent systems, because of the importance of predicting their response to various conditions in the environment.…”

Section: Introductionmentioning

confidence: 99%

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Stabilization Methods for a Multiagent System with Complex Behaviours

Leon

2015

Computational Intelligence and Neuroscience

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The main focus of the paper is the stability analysis of a class of multiagent systems based on an interaction protocol which can generate different types of overall behaviours, from asymptotically stable to chaotic. We present several interpretations of stability and suggest two methods to assess the stability of the system, based on the internal models of the agents and on the external, observed behaviour. Since it is very difficult to predict a priori whether a system will be stable or unstable, we propose three heuristic methods that can be used to stabilize such a system during its execution, with minimal changes to its state.

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Section: Related Workmentioning

confidence: 99%

Section: Interpretations Of Stabilitymentioning

confidence: 99%

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Stabilization Methods for a Multiagent System with Complex Behaviours

Leon

2015

Computational Intelligence and Neuroscience

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“…Whereas Chli-DeWilde stability of MASs [9] may be suitable, because it models MASs as Markov chains, which are an established modelling approach in evolutionary computing [28]. A MAS is viewed as a discrete time Markov chain with potentially unknown transition probabilities, in which the agents are modelled as Markov processes, and is considered to be stable when its state, a stochastic process, has converged to an equilibrium distribution [9].…”

Section: Agent Stabilitymentioning

confidence: 99%

Digital ecosystems

Wilde

Briscoe

2009

Proceedings of the International Conference on Management of Emergent Digital EcoSystems

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Stability is perhaps one of the most desirable features of any engineered system, given the importance of being able to predict its response to various environmental conditions prior to actual deployment. Engineered systems are becoming ever more complex, approaching the same levels of biological ecosystems, and so their stability becomes ever more important, but taking on more and more differential dynamics can make stability an ever more elusive property. The Chli-DeWilde definition of stability views a Multi-Agent System as a discrete time Markov chain with potentially unknown transition probabilities. With a Multi-Agent System being considered stable when its state, a stochastic process, has converged to an equilibrium distribution, because stability of a system can be understood intuitively as exhibiting bounded behaviour. We investigate an extension to include Multi-Agent Systems (MASs) with evolutionary dynamics, focusing on the evolving agent populations of our Digital Ecosystem. We then built upon this to construct an entropy-based definition for the degree of instability (entropy of the limit probabilities), which was later used to perform a stability analysis. The Digital Ecosystem is considered to investigate the stability of an evolving agent population through simulations, for which the results were consistent with the original Chli-DeWilde definition.

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“…Our broader interest lies in understanding the dynamics of ecosystems [1]- [3]. Furthermore, we are interested in analyzing the global system properties which emerge from the interactions that occur in a market ecosystem.…”

Section: Introductionmentioning

confidence: 99%

The Emergence of Knowledge Exchange: An Agent-based Model of a Software Market

Chli¹,

Wilde²

2009

Convergence and Knowledge Processing in Multi-Agent Systems

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Abstract-We investigate knowledge exchange among commercial organizations, the rationale behind it, and its effects on the market. Knowledge exchange is known to be beneficial for industry, but in order to explain it, authors have used high-level concepts like network effects, reputation, and trust. We attempt to formalize a plausible and elegant explanation of how and why companies adopt information exchange and why it benefits the market as a whole when this happens. This explanation is based on a multiagent model that simulates a market of software providers. Even though the model does not include any high-level concepts, information exchange naturally emerges during simulations as a successful profitable behavior. The conclusions reached by this agent-based analysis are twofold: 1) a straightforward set of assumptions is enough to give rise to exchange in a software market, and 2) knowledge exchange is shown to increase the efficiency of the market.Index Terms-Adaptive behavior, agent-based modeling, business economics, cooperative systems, intelligent agents, multiagent systems.

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Stability of multi-agent systems

Cited by 18 publications

References 13 publications

Stabilization Methods for a Multiagent System with Complex Behaviours

Stabilization Methods for a Multiagent System with Complex Behaviours

Digital ecosystems

The Emergence of Knowledge Exchange: An Agent-based Model of a Software Market

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