Source: http://www.sarl.io/publications/index.html
Timestamp: 2019-04-21 00:33:01+00:00

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This page lists publications that have used or cited SARL (or Janus 2.x) software and/or models.
This list is by no means complete or exhaustive. If you are using and/or citing SARL in your work, or you know of work that is not listed, please send the relevant citations to stephane.galland@utbm.fr.
Rodriguez, S., Gaud, N., & Galland, S. (2014). SARL: a general-purpose agent-oriented programming language. In the 2014 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. Warsaw, Poland: IEEE Computer Society Press.
SARL agent-oriented language website, http://www.sarl.io.
A list of references (papers and PhD theses on or using SARL) is available below.
SARL is a general-purpose agent-oriented programming language. This language aims at providing the fundamental abstractions for dealing with concurrency, distribution, interaction, decentralization, reactivity, autonomy and dynamic reconfiguration that are usually considered as essential for implementing agent-based applications. Every programming language specifies an execution model. For SARL, this run-time model is supported by a SARL run- time environment. The goals of this paper are to highlight the key principles for creating a SARL run-time environment, and its concrete implementation into the Janus agent platform.
The performance evaluation of an agent platform is central in the agent-based modeling field. This performance is influenced by the agent platform modules, and by the hardware and operating system modules, including any virtual machine. The impact of these hardware and operating system modules should be understood and evaluated due to there high impact on the global performances. In this paper, we propose the scoring approach, named SARL Experience Index in order to evaluate these two components. The score is based on CPU, memory and disk sub-scores. We advocate this score may be helpful for the agent platform users for determining quickly the positive or negative impacts of a new deployment platform for an agent framework.
Cich, G., Galland, S., Knapen, L., Yasar, A.-ul-haque, Bellemans, T., & Janssens, D. (2017). Addressing the Challenges of Conservative Event Synchronization for the SARL Agent-Programming Language. In the 15th International Conference on Practical Applications of Agents and Multi-Agent Systems. Springer.
Synchronization mechanism is a key component of an agent-based simulation model and platform. Conservative and optimistic models were proposed in the domain of distributed and parallel simulation. However, the SARL agent-programming language is not equipped with specific simulation features, including synchronization mechanisms. The goal of this paper is to propose a conservative synchronization model for the SARL language and its run-time platform Janus.
Tchappi Haman, I., Kamla, V. corneille, Galland, S., & Kamgang, J.-C. (2017). Towards an Multilevel Agent-based Model for Traffic Simulation. In the 6th International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications (ABMTRANS’17). Springer.
Large scale road traffic is a complex system that could be modelled with a multilevel approach. Most of the multilevel models from the literature have fixed a priori level of details (micro-meso, micro- macro, meso-macro). This paper has two goals: it presents the state of the art related to large scale traffic models, and it gives the main research direction to create a novel multilevel model that support dynamic selection of the level during the simulation. Our proposal is based on an organizational modelling approach and the use of the concept of holon (agent composed of agents).
Feraud, M., & Galland, S. (2017). First Comparison of SARL to Other Agent-Programming Languages and Frameworks. In International Workshop on Agent-based Modeling and Applications with SARL (SARL 2017). Elsevier.
This paper proposes a first comparison of different agent-oriented programming languages, including the SARL agent-programming language. The study of those tools is based upon various criteria that are defining the essential aspects of multi-agent systems. For the sake of clarity, the comparative study is presented in a tabular form, including the languages and all the criteria. For each criterion, a definition is made to understand its meaning. At the end of the article, all the languages will be described in order to define their advantages and disadvantages.
Cich, G., Knapen, L., Maciejewski, A.-U.-H., Michał Yasar, Bellemans, T., & Janssens, D. (2017). Modeling Demand Responsive Transport using SARL and MATSim. In International Workshop on Agent-based Modeling and Applications with SARL (SARL 2017). Elsevier.
Cich, G., Knapen, L., Galland, S., Vuurstaek, J., Neven, A., & Bellemans, T. (2016). Towards an Agent-based Model for Demand-Responsive Transport Serving Thin Flows. In the 5th International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications (ABMTRANS 2016). Procedia Computer Science, Elsevier.
Low volume traveler flows cause problems for public transportation (PT) providers. The Smart- PT project aims to find out how such flows can be combined to increase the service provider viability. The capability to conceive multi-modal trips is fundamental in that context and is modeled by the Trip Sequence Composer (TSC) concept. A TSC is an essential component of the traveler’s brain, of the customer support operated by collective transport providers, of trip advisers in websites etc. We present a simulation model design to evaluate the effect of cooperating TSCs on the viability of demand responsive collective transport providers. While obeying specific regulations, specialized services targeting mobility impaired people can also serve regular requests in order to save fleet and personnel costs.
This work has been performed within the ICT COST Action IC1404, Multi-Paradigm Modelling for Cyber-Physical Systems (MPM4CPS).
The environment is now considered as a first class abstraction in multiagent systems. However, the boundary between real and simulated environment and the application logic is not so well defined. Depending on applications, the environment as a space shared between agents may integrate physical, communication or social dimensions where agents interact. In this paper, authors introduce a new view for structuring the notion of environment in a multiagent systems using the fundamental concepts provided by the SARL programming language.
Galland, S., Balbo, F., Gaud, N., Rodriguez, S., Picard, G., & Boissier, O. (2015). A multidimensional environment implementation for enhancing agent interaction. In R. Bordini & E. Elkind (Eds.), Autonomous Agents and Multiagent Systems (AAMAS15). Istanbul, Turkey.
The environment, as a space shared between agents, is a key component of multiagent systems (MAS). Depending on systems, this space may integrate physical, communication or social dimensions. Each of them has its own process and rules to support agents’ interaction. For instance, in the physical dimension, the rules may be based on the agents’ location. The agents’ interaction is then contextualized and the environment allows a multiagent control since it performs the interactions according to the state and rules of the MAS. The dimensions of the environment are generally connected either in an application dependent adhoc way outside of the agents or within each agent. In order to ensure a multiagent control, the relations between dimensions must be explicit outside of the agents. Using these relations between the environment dimensions, the interaction becomes also multi-dimensional. In this paper we propose to formalize rules and mechanisms to make this connection outside of the agents and in a generic way.
Galland, S., Balbo, F., Gaud, N., Rodriguez, S., Picard, G., & Boissier, O. (2015). Contextualize Agent Interactions by Combining Social and Physical Dimensions in the Environment. In Y. Demazeau & K. Decker (Eds.), 13th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS).
The environment, as a space shared between agents, is a key component of multiagent systems (MAS). Depending on systems, this space may integrate physical, communication or social dimensions. Each of them has its own process and rules to support agents’ interaction. The dimensions of the environment are generally connected either outside of the agents or within each agent, according to the target application. In order to ensure a multiagent control, the relations between dimensions must be explicit outside of the agents. Using these relations between the environment dimensions, the interaction becomes also multi-dimensional. In this paper, rules and mechanisms to make this connection outside of the agents are formalized. The model connects the physical and social dimensions to realize contextualized interactions. It is implemented using the SARL multiagent programming language, and illustrated with an urban traffic simulation.
Galland, S., Gaud, N., Rodriguez, S., Balbo, F., Picard, G., & Boissier, O. (2014). Contextualiser l’interaction entre agents en combinant dimensions sociale et physique au sein de l’environnement. In R. Courdier & J.-P. Jamont (Eds.), Les 22èmes Journées Francophones sur les Systèmes Multi-Agents (pp. 65–74). Loriol-sur-Drôme, France: Cépaduès Éditions.
L’environnement, en tant qu’espace partagé entre agents, est un élément essentiel des systèmes multiagents. Selon les systèmes, cet espace intègre des dimensions différentes comme une dimension physique support à l’ancrage spatial et à l’activité des agents sur cette dimension, ou une dimension sociale support aux communications entre agents. Ces dimensions sont souvent traitées de manière indépendante et ne sont reliées qu’au sein de l’agent qui constitue alors le lieu de jonction et de combinaison des informations véhiculées dans ces différentes dimensions. Il s’avère cependant que la combinaison entre ces dimensions est à considérer également en dehors des agents, pour pouvoir par exemple, situer des communications. Dans cet article, nous proposons un modèle unifié supportant la combinaison des dimensions physiques et sociales pour la mise en \oeuvre d’interactions contextualisées entre agents. Ce modèle est développé avec le langage multiagent SARL. Nous illustrons cette proposition par une application de simulation de trafic routier dans la ville de Belfort.
Complex software systems development require appropriate high-level features to better and easily tackle the new requirements in terms of interactions, concurrency and distribution. This requires a paradigm change in software engineering and corresponding programming languages. We are convinced that agent-oriented programming may be the support for this change by focusing on a small corpus of commonly accepted concepts and the corresponding programming language in line with the current developers’ programming practices. This papers introduces SARL, a new general-purpose agent-oriented programming language undertaking this challenge. SARL comes with its full support in the Eclipse IDE for compilation and debugging, and a new version 2.0 of the Janus platform for execution purposes. The main perspective that guided the creation of SARL is the establishment of an open and easily extensible language. Our expectation is to provide the community with a common forum in terms of a first working testbed to study and compare various programming alternatives and associated metamodels.
Matson, E. (2017). Building a HARMS Implementation Model Using SARL. Madeira, Portugal: International Workshop on Agent-based Modeling and Applications with SARL (SARL 2017).
Galland, S., Gaud, N., & Rodriguez, S. (2014). Agent-Oriented Programming Language for Mobility and Transport Sciences. Institute for Mobility, University of Hasselt, Belgium.
Rodriguez, S., Gaud, N., & Galland, S. (2014). SARL: Agent Oriented Programming Language. Facultad Regional Tucumán, Universidad Tecnologica Nacional – Tucumán, Argentina.
Rodriguez, S., Gaud, N., & Galland, S. (2014). SARL: Agent Oriented Programming Language. Universide Federal de Santa Catarina – Florianopolis, Brazil.
Rodriguez, S., Gaud, N., & Galland, S. (2014). SARL: Agent Oriented Programming Language. ISCOD / LSTI, Institut Henri Fayol – Saint étienne, France.
Rodriguez, S. (2005). From Analysis to Design of Holonic Multi-Agent Systems: a Framework, Methodological Guidelines and Applications (PhD thesis). Technology University of Belfort-Montbéliard, Belfort, France.
Rodriguez, S., Gaud, N., Hilaire, V., Galland, S., & Koukam, A. (2007). An analysis and design concept for self-organization in Holonic Multi-Agent Systems. In International Conference on Engineering Self-Organising Systems, Lecture Notes in Artificial Intelligence 4435 (pp. 15–27). Springer-Verlag.
Holonic Multi-Agent Systems (HMAS) are a convenient way to engineer complex and open systems. HMAS are based upon selfsimilar entities, called holons, which define an organizational structure called holarchy. An open issue of HMAS is to give holons means of selforganization to satisfy their goals. Our works focus on modeling and engineering of complex systems using a holonic organizational approach. This paper introduces the concept of capacity as the description of agents know-how. This concept allows the representation and reasoning about agents know-hows. Even more, it encourages a reusable modeling and provides agents with means to self-organize.
Gaud, N. (2007). Holonic Multi-Agent Systems: From the analysis to the implementation. Metamodel, Methodology and Multilevel simulation. (PhD thesis). Technology University of Belfort-Montbéliard, Belfort, France.
Gaud, N., Gechter, F., Galland, S., & Koukam, A. (2007). Holonic multiagent multilevel simulation : Application to real-time pedestrians simulation in urban environment. In the 30th International Joint Conference on Artificial Intelligence (IJCAI’07) (pp. 1275–1280). Hyderabad India.
Cossentino, M., Gaud, N., Galland, S., Hilaire, V., & Koukam, A. (2007). A Holonic Metamodel for Agent-Oriented Analysis and Design. In V. Marik, V. Vyatkin, & A. W. Colombo (Eds.), LNAI 4659 "Holonic and Multi-Agent Systems for Manufacturing" (HoloMAS’07 International Conference) (pp. 237–246).
Molesini, A., Denti, E., Nardini, E., & Omicini, A. (2009). Situated Process Engineering for Integrating Processes from Methodologies to Infrastructures. In Proceedings of the 2009 ACM Symposium on Applied Computing (SAC’09) (pp. 699–706). New York, NY: ACM.
Galland, S., Gaud, N., Demange, J., & Koukam, A. (2009). Environment Model for Multiagent-Based Simulation of 3D Urban Systems. In the 7th European Workshop on Multiagent Systems (EUMAS09). Ayia Napa, Cyprus.
Demange, J., Galland, S., & Koukam, A. (2010). Analysis and Design of Multi-Level Virtual Indoor Environment. Int. Journal Systemics and Informatics World Network, 10, 145–152.
Cossentino, M., Hilaire, V., & Koukam, A. (2010). Holons for Analysis, Modelling and Simulation of Complex Systems. European Research Consortium for Informatics and Mathematics News, (81), 40–41.
Galland, S., Gaud, N., Rodriguez, S., & Hilaire, V. (2010). Janus: Another Yet General-Purpose Multiagent Platform. In the 7th Agent-Oriented Software Engineering Technical Forum (TFGAOSE-10). Paris, France: Agent Technical Fora.
Behe, F., Nicolle, C., Galland, S., & Koukam, A. (2011). Qualifying Building Information Models with Multi-Agent System. In J. jules Meyer, A. el fallah Seghrouchni, & A. magda Florea (Eds.), the 3rd International Workshop on Multi-Agent Systems Technology and Semantics (MASTS 2011) (pp. 309–314). Delft, The Netherlands: Springer.
Behe, F., Galland, S., Nicolle, C., & Koukam, A. (2011). Semantic Management of Intelligent Multi-Agents Systems in a 3D Environment. In International Conference on Knowledge Engineering and Ontology Development (KEOD11). Paris, France.
Galland, S., Demange, J., & Koukam, A. (2011). Towards the Agentification of a Virtual Situated Environment for Urban Crowd Simulation. In the Insitution of Engineering and Technology Conference on Smart and Sustainable City 2011 (ICSSC11). Shanghaı̈, China: Shanghaı̈ University Press.
Basso, G., Hilaire, V., Lauri, F., Roche, R., & Cossentino, M. (2011). A MAS-based simulator for the prototyping of Smart Grids. In 9th European Workshop on Multiagent Systems (EUMAS11).
Demange, J. (2012). Un modèle d’environnement pour la simulation pour la simulation multiniveau : application à la simulation de foules (PhD thesis). Université de Technologie de Belfort-Montbéliard, Belfort, France.
Zato, C., Villarrubia, G., Sánchez, A., Barri, I., Rubión, E., Fernández, E., … Corchado, J. M. (2012). PANGEA - Platform for Automatic coNstruction of orGanizations of intElligent Agents. Distributed Computing and Artificial Intelligence, Advances in Intelligent and Soft Computing, 151, 229–239.
Cossentino, M., Hilaire, V., Gaud, N., Galland, S., & Koukam, A. (2014). The ASPECS Process. In M. Cossentino, V. Hilaire, A. Molesini, & V. Seidita (Eds.), Handbook on Agent-Oriented Design Processes (pp. 65–114). Springer.

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