GAMA: An Environment for Implementing and Running Spatially Explicit Multi-agent Simulations

Amouroux, Edouard; Chu, Thanh-Quang; Boucher, Alain; Drogoul, Alexis

doi:10.1007/978-3-642-01639-4_32

Cited by 59 publications

(45 citation statements)

References 7 publications

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“…In the context of this work, the GAMA platform was used [7,8,9]. GAMA is a freely available modeling and simulation development environment for building spatially explicit agent-based simulations.…”

Section: Gama Platformmentioning

confidence: 99%

On the Joint Modeling of the Behavior of Social Insects and Their Interaction with Environment by Taking into Account Physical Phenomena Like Anisotropic Diffusion

Cazin

Histace²,

Picard

et al. 2015

Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability - The PAAMS Collection

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This work takes place in the framework of GEODIFF project (funded by CNRS) and deals with the general issue of the social behavior modeling of pest insects with a particular focus on Bark Beetles. Bark Beetles are responsible for pine trees devastation in North America since 2005. In order to stem the problem and to apply an adapted strategy, one should be able to predict the evolution of the population of Bark Beetles. More precisely, a model taking into account a given population of insects (a colony) interacting with its environment, the forest ecosystem, would be very helpful. In a previous work, we aimed to model diffusive phenomenons across the environment using a simple reactive Multiagent System. Bark beetle use pheromones as a support for recruitment of other bark beetles in the neighborhood in order to achieve a mass attack over a tree. They are first attracted by the ethanol or other phytopheromones emitted by a sick, stressed or dead tree and reinforce the presence of other individuals amongst the targeted tree. Both ethanol and semiochemicals are transported through the forest thanks to the wind, thermic effects and this advection phenomenon is modulated by the topology of the environment, tree and other obstacles distribution. In other words, the environment is involved in the process of a bark beetle attack. The first modeling we used to tackle our objective was not spatially explicit as long as free space propagation only was taken into account (isotropic phenomenon) with no constraint imposed by the environment such as wind. This article is intended to take into account such physical phenomenons and push the modeling one step further by providing predictions driven by measures provided by a Geographical Information System.

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Section: Gama Platformmentioning

confidence: 99%

On the Joint Modeling of the Behavior of Social Insects and Their Interaction with Environment by Taking into Account Physical Phenomena Like Anisotropic Diffusion

Cazin

Histace²,

Picard

et al. 2015

Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability - The PAAMS Collection

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“…We can cite as examples GAMA [2], Mason [3], Repast [4], NetLogo [5]. In this work, we chose to develop our simulation with the GAMA platform.…”

Section: Simulation Implementationmentioning

confidence: 99%

Inferring Equation-Based Models from Agent-Based Models: A Case Study in Competition Dynamics

Nguyen

Taillandier

Drogoul

et al. 2012

Principles and Practice of Multi-Agent Systems

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Two types of model, equation-based models (EBMs) and agentbased models (ABMs) are now widely used in modeling ecological complex systems and seem not to be reconciled. While ABMs can help in exploring and explaining the local causes of global phenomena, EBMs are useful for predicting their long-term evolution without having to explore them through simulated experiments. In this paper, we show that it is possible to use an ABM to infer an EBM. Base on the case study, a dynamics of two competing species, we illustrate our methodology through the presentation of two models: an ABM and an EBM. We also show that the two models give the same results on coexistence of the two competing species.

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“…−Simulation platform drivers package contains the kernel of agent-based plateforms such as Repast [8] or Gama [3].…”

Section: Logical Architecturementioning

confidence: 99%

“…Many ABM platforms (Repast [8], NetLogo [9], Swarm [7] or GAMA [3]) have been developed and used by researchers [3,10]. In the context of collaborative research, one major question is: How can geographically diverse researchers effectively work together to conduct ABM simulations without regard to the ABM platforms used?…”

Section: Introductionmentioning

confidence: 99%

PAMS – A New Collaborative Framework for Agent-Based Simulation of Complex Systems

Nguyen-Trong¹,

Marilleau²,

Ho³

2008

Intelligent Agents and Multi-Agent Systems

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Abstract.Major researches in the domain of complex systems are interdisciplinary, collaborative and geographically distributed. The purpose of our work is to explore a new collaborative approach that facilitates scientist's interactions during the modelling and simulating process. The originality of the presented approach is to consider models and simulators as a board of the collaboration: a shared object manipulated by a group of scientists. Agent-based simulations are powerful tools for studying complex systems. In this context, we develop a collaborative platform dedicated to agent-based simulation (PAMS). This new environment integrates common collaborative tools (e.g. videoconferencing, instant messaging, whiteboard) and specific tools to share and manipulate models, simulators, experiments and results... The current version of PAMS is based technologies coming from distributed systems. Today PAMS has been designed to support major agent based simulation frameworks. This paper aims to give an overview of the PAMS environment by defining the collaborating approach, the framework architecture and an example of its utilization.

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GAMA: An Environment for Implementing and Running Spatially Explicit Multi-agent Simulations

Cited by 59 publications

References 7 publications

On the Joint Modeling of the Behavior of Social Insects and Their Interaction with Environment by Taking into Account Physical Phenomena Like Anisotropic Diffusion

On the Joint Modeling of the Behavior of Social Insects and Their Interaction with Environment by Taking into Account Physical Phenomena Like Anisotropic Diffusion

Inferring Equation-Based Models from Agent-Based Models: A Case Study in Competition Dynamics

PAMS – A New Collaborative Framework for Agent-Based Simulation of Complex Systems

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