Christophe Lang

A survey on parallel and distributed multi-agent systems for high performance computing simulations

Abstract Simulation has become an indispensable tool for researchers to explore systems without having recourse to real experiments. Depending on the characteristics of the modeled system, methods used to represent the system may vary. Multi-agent systems are often used to model and simulate complex systems. In any cases, increasing the size and the precision of the model increases the amount of computation, requiring the use of parallel systems when it becomes too large. In this paper, we focus on parallel platforms that support multi-agent simulations and their execution on high performance resources as parallel clusters. Our contribution is a survey on existing platforms and their evaluation in the context of high performance computing. We present a qualitative analysis of several multi-agent platforms, their tests in high performance computing execution environments, and the performance results for the only two platforms that fulfill the high performance computing constraints.

Modelling of Complex Systems with AML as Realized in MIRO Project

In this paper, we propose a modeling approach for a spatial complex system. The targeted system is the city with its mobility patterns. The goal of MIRO project is to study service accessibility in the city. In fact, we simulate the city with multi agent systems using them to represent each part of the system(individuals, buildings, streets,). The MIRO team is composed by scientists of several domains (computer sciences, geography or economy), so we want to construct a model of the city to share knowledges of each domain. The next step, we will use verification approach in order to validate the model and then the simulator because we want to generate simulator from model. Thus, we propose a method for modeling such complex system. This method is based on AML (Agent Modeling Language) that is a language well adapted for modeling multi-agent systems. We, then, present a spatial AML meta-model coupled with a method. The use case is the MIRO project.

EPIS: A Grid Platform to Ease and Optimize Multi-agent Simulators Running

This paper presents the work done during the first year of the EPIS project. This project deals with the process of conductingmultiple and parallelmulti agents-based simulations (MABS) on a cluster or a grid in order to generate sufficient data for scientific use (e.g. in the case of a sensibility analysis of a simulation). We provide a new, general and user-friendly approach to marry MABS and High- Performance Computing (HPC). We, thus, propose a workflow and an associated HPC infrastructure. These two permit to easily deploy a lot of simulations on a cluster without any prior parallelizing work. The method wants to be as generic as possible: no particular MABS targeted, no overhead and HPC compliance work has to be done only once. Moreover the user is guided by a web interface that handles the workflow.

Exploring Intra-Urban Accessibility and Impacts of Pollution Policies with an Agent-Based Simulation Platform: GaMiroD

In this work we address the issue of sustainable cities by focusing on one of their very central components: daily mobility. Indeed, if cities can be interpreted as spatial organizations allowing social interactions, the number of daily movements needed to reach this goal is continuously increasing. Therefore, improving urban accessibility merely results in increasing traffic and its negative externalities (congestion, accidents, pollution, noise, etc.), while eventually reducing the quality of life of people in the city. This is why several urban-transport policies are implemented in order to reduce individual mobility impacts while maintaining equitable access to the city. This challenge is however non-trivial and therefore we propose to investigate this issue from the complex systems point of view. The real spatial-temporal urban accessibility of citizens cannot be approximated just by focusing on space and implies taking into account the space-time activity patterns of individuals, in a more dynamic way. Thus, given the importance of local interactions in such a perspective, an agent based approach seems to be a relevant solution. This kind of individual based and “interactionist” approach allows us to explore the possible impact of individual behaviors on the overall dynamics of the city but also the possible impact of global measures on individual behaviors. In this paper, we give an overview of the Miro Project and then focus on the GaMiroD model design from real data analysis to model exploration tuned by transportation-oriented scenarios. Among them, we start with the the impact of a LEZ (Low Emission Zone) in the city center.

Using GPU for Multi-Agent Soil Simulation

Multi-Agent Systems (MAS) can be used to model systems where the global behavior cannot be uniformly represented by standard techniques such as partial differential equations or linear systems because the system elements have their own independent behavior. This is, for instance, the case in complex systems such as daily mobility in a city for example. Depending on the system size the computing power needs for the MAS may be as big as for more traditional linear numerical systems and may need to be parallelized to fully represent real systems. Graphical Processing Units (GPU) have already proven to be an efficient support to execute large linear programs. In this paper we present the use of GPU for the execution of Sworm, a multi-scale MAS system. We show that GPU computing can be efficient in that less regular case and when the agent behavior is simple. We advocate for a wider use of the GPU in Agent Based Models in particular for multi-scale systems with work distribution between the CPU and GPU.

A meta-model of group for urban mobility modeling

Mobility is a concept which is studied in many research areas as astronomy, sociology, physics, and so on. Urban mobility study aims at looking and designing human displacements in an urban environment. In the literature, two main kinds of mobility are emergent: macroscopic displacements and microscopic movements. Our study takes place at the boundary of them. We want to describe human displacements in a city by describing their behaviours. We use multi-agent systems (MAS). People behaviours depend on many parameters like social class, geographical location... So we need to look at these elements. In this article, we suggest a meta-model which intends to organise agents into groups. This tool is associated with another one which intends to define mobile behavior. These meta-models are encapsulated in a method which helps simulator creations.

Nested graphs: A model to efficiently distribute multi-agent systems on HPC clusters: Nested graphs: A model to efficiently distribute multi-agent systems on HPC clusters

Computational simulation is becoming increasingly important in numerous research fields. Depending on the modeled system, several methods such as differential equations or Monte‐Carlo simulations may be used to represent the system behavior. The amount of computation and memory needed to run a simulation depends on its size and precision, and large simulations usually lead to long runs, thus requiring to adapt the model to a parallel system. Complex systems are often simulated using multi‐agent systems (MASs). While linear system based models benefit from a large set of tools to take advantage of parallel resources, multi‐agent systems suffer from a lack of platforms that ease the use of such resources. In this paper, we propose the use of Nested Graphs for a new modeling approach that allows the design of large, complex, and multi‐scale multi‐agent models, which can efficiently be distributed on parallel resources. Nested Graphs are formally defined and are illustrated on the well‐known predator‐prey model. We also introduce PDMAS (parallel and distributed multi‐agent system): a platform that implements the Nested Graph modeling approach to ease the distribution of multi‐agent models on High Performance Computing clusters. Performance results are presented to validate the efficiency of the resulting models.

Multiscale Modeling: Application to Traffic Flow

Abstract: Traffic modeling is a particularly active field, the origins of which can be traced back to the pioneering work of Greenshield in the 1930s. Greenshield was the first to formulate a structural relation between the speed of vehicles on a road and the distance between them. This relation between the flow rate/density, at the heart of the so-called fundamental diagram, has been used by all families of traffic flow models developed ever since. These families of models can be grouped into three distinct but strongly interconnected subcategories: macroscopic models, which consider flows of vehicles, microscopic models, which consider individual vehicles and their interactions, and mesoscopic models, which lie in between the other two categories.

Introduction to NetLogo

Abstract NetLogo is a programming environment which allows for the construction and exploration of agent-based models. Developed at the Center for Connected Learning, the software currently draws from StarLogoT, which is available for Mac OSX, and StarLogo, which was developed at MIT’s Media Laboratory. It is the latter that has had the greatest influence on the programming language used by NetLogo, known as Logo, which was itself inspired by the Lisp programming language family. The history of Logo allows for a partial understanding of NetLogo’s philosophy.

Cognitive Perception in RAFALE-SP Methodology

Several methodologies based on multi-agent systems (MAS) already exist. They help designers to describe software or to create MAS which aim at solving complex problems by simulations. Due to used formalisms, a methodology may be more or less generic. In this context, we have created a mobility oriented methodology called RAFALE-SP based on multi-agent systems. It helps us to describe mobiles which move on a space. This environment can be a virtual representation of a real space like a town where unpredictable events arise. We apply our methodology to solve problems which come from different research areas. We use it to find answers to geographical problems. The presented methodology begins by a conceptual representation of each mobile type and finishes by a mobility simulator. It uses several formalisms: UML and Ploom-unity. They allow us to define mobiles, their interactions and their environment. According to their knowledge, their behaviour rules, mobiles moves on a space by following few motion types. They get an individual perception of the world. In this paper we focus on mobile perception description