Modeling dynamic environments in multi-agent simulation

Helleboogh, Alexander; Vizzari, Giuseppe; Uhrmacher, Adelinde M.; Michel, Fabien

doi:10.1007/s10458-006-0014-y

Cited by 73 publications

(38 citation statements)

References 22 publications

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“…However, those approaches find it difficult to incorporate the ability of market participants to repeatedly probe markets and adapt their strategies. As an alternative to the equilibrium approaches, the multi-agent based simulation (MABS) comes forth as being particulary well fitted to analyze dynamic and adaptive systems with complex interactions among constituents [19,9].…”

Section: Decision-making For Electricity Marketsmentioning

confidence: 99%

Collective-Intelligence and Decision-Making

Trigo¹,

Coelho²

2010

Computational Intelligence for Engineering Systems

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Abstract. Decision-making, while performed by humans, is also expected to be found in most (artificial) intelligent systems. Usually, the cognitive research assumption is that the individual is the correct unit for the analysis of (human) intelligence. Yet, the multi-agent assumption is that of a society of interacting individuals (agency) that collectively supersedes individual capabilities. Therefore, the entire society of agents is, itself, an additional locus for the analysis of this collective-intelligence. In this paper we propose models that explore the agent-agency mutual influence from the decision-making perspective. We outline three case study scenarios used during the model's experimental evaluation: i) large-scale disasters, ii) electricity markets, and iii) Web-empowered knowledge and social connectivity. The scenario-driven evaluations are being used to provide the alignment weights for the effort along our research lines.

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Section: Decision-making For Electricity Marketsmentioning

confidence: 99%

Collective-Intelligence and Decision-Making

Trigo¹,

Coelho²

2010

Computational Intelligence for Engineering Systems

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“…The LSA-space is a realized as a local tuple space, which hosts LSAs in the form of tuples. The shape of the actual network of connection is determined by a reconfigurable component, which can be based on, e.g., a strategy that connects nodes based on spatial proximity or rather one relying on social proximity [11]. The shape of such network determines the paths along which LSAs on a node can propagate and diffuse to other nodes.…”

Section: Example Applicationmentioning

confidence: 99%

Agent Environments for Multi-agent Systems – A Research Roadmap

Weyns

Michel

2015

Lecture Notes in Computer Science

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Abstract. Ten years ago, researchers in multi-agent systems became more and more aware that agent systems consist of more than only agents. The series of workshops on Environments for Multi-Agent Systems (E4MAS 2004(E4MAS -2006 emerged from this awareness. One of the primary outcomes of this endeavor was a principled understanding that the agent environment should be considered as a primary design abstraction, equally important as the agents. A special issue in JAAMAS 2007 contributed a set of influential papers that define the role of agent environments, describe their engineering, and outline challenges in the field that have been the drivers for numerous follow up research efforts. The goal of this paper is to wrap up what has been achieved in the past 10 years and identify challenges for future research on agent environments. Instead of taking a broad perspective, we focus on three particularly relevant topics of modern software intensive systems: large scale, openness, and humans in the loop. For each topic, we reflect on the challenges outlined 10 years ago, present an example application that highlights the current trends, and from that outline challenges for the future. We conclude with a roadmap on how the different challenges could be tackled.

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“…[20][21][22][23]. For example, Helleboogh et al consider the requirements for modelling multi-agent systems, with a particular emphasis on the modelling of physical applications via simulation [20].…”

Section: Related Workmentioning

confidence: 99%

“…As with CARMA, the authors make a distinction between the simulated environment and the simulating environment, which is transparent to the modeller. However, in the work presented in [20], the distinction between the simulated environments and the agents within it is not strong (cf. the framework of Weyns et al [2]).…”

Section: Related Workmentioning

confidence: 99%

Specification and Analysis of Open-Ended Systems with CARMA

Hillston

Loreti

2015

Lecture Notes in Computer Science

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Abstract. CARMA is a new language recently defined to support quantified specification and analysis of collective adaptive systems. It is a stochastic process algebra equipped with linguistic constructs specifically developed for modelling and programming systems that can operate in open-ended and unpredictable environments. This class of systems is typically composed of a huge number of interacting agents that dynamically adjust and combine their behaviour to achieve specific goals. A CARMA model, termed a "collective", consists of a set of components, each of which exhibits a set of attributes. To model dynamic aggregations, which are sometimes referred to as "ensembles", CARMA provides communication primitives based on predicates over the exhibited attributes. These predicates are used to select the participants in a communication. Two communication mechanisms are provided in the CARMA language: multicast-based and unicast-based. A key feature of CARMA is the explicit representation of the environment in which processes interact, allowing rapid testing of a system under different open world scenarios. The environment in CARMA models can evolve at runtime, due to the feedback from the system, and it further modulates the interaction between components, by shaping rates and interaction probabilities.

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Modeling dynamic environments in multi-agent simulation

Cited by 73 publications

References 22 publications

Collective-Intelligence and Decision-Making

Collective-Intelligence and Decision-Making

Agent Environments for Multi-agent Systems – A Research Roadmap

Specification and Analysis of Open-Ended Systems with CARMA

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