Nabil Sahli

EKEMAS, an agent-based geo-simulation framework to support continual planning in the real-word

In this paper, we propose an agent-based geo-simulation framework EKEMAS to assist human planners when planning under strong spatial constraints in a real large-scale space. The approach consists in drawing a parallel between the real environment (for example, a forest in fire) and the simulated environment based on GIS data. This virtual environment uses software agents which are aware of the space and equipped with advanced spatial reasoning capabilities. In addition, we suggest some enhancements for the Continual Planning approach. Our aim is to demonstrate how EKEMAS, when coupled with a continual planning approach and agent’s spatial reasoning capabilities, can assist human planners overcoming obstacles related to real world constraints: dynamic, uncertain, and spatially constrained environment. We illustrate this idea on the forest firefighting problem and we use MAGS as a simulation platform and Prometheus as a fire simulator. Finally, and since plans in the studied case (wildfire fighting) are mainly paths, we also propose a new approach based on agent geo-simulation in order to solve particular Pathfinding problems.

Agent-Based geo-simulation to support human planning and spatial cognition

AbstracL In this paper we emphasize the strengths and weaknesses of human planning and especially within a geographic space. We propose a multi-agent simulation approach in order to overcome some of these limitations while reasoning about a large-scale geographic space. A cognitive complementarity between software agents and human beings emerges from this approach. An illustration on wildfire fighting is presented.

Using Multi-agent Geo-simulation Techniques for the Detection of Risky Areas for Trains

A transportation system is spatially and functionally distributed; its subsystems have a high degree of autonomy and are in constant interaction with each other and with the surrounding geographic environment. Modeling and simulating such systems in large-scale geographic spaces is a complex process. In this paper we address the domain of railway systems, and more particularly the problem of detecting risky areas along railroads. This requires that we consider a variety of static and dynamic variables, including train characteristics, hazardous events (e.g. rock-falls), and the properties of the large-scale geographic environment, as well as weather conditions. This simulation enables us to recommend speed limits in risky areas while taking into account all of the aforementioned factors. Since statistical and analytical models are not appropriate to represent such a complex process in which spatial constraints are of high importance, we adopted a multi-agent geo-simulation (MAGS) approach that facilitates the simulation of complex systems in large-scale geo-referenced environments. In this paper, we present Train-MAGS, an agent-based geo-simulation tool that simulates train behaviors in risky areas in large-scale virtual geographic environments. We also demonstrate how risky areas can be detected in real time using an agent-based approach. This work also illustrates how the application of artificial intelligence techniques, such as the MAGS approach, provides interesting perspectives of realistic and plausible simulations aimed at improving the functioning, the efficiency, and the safety of the transportation systems.

On the reputation of communities of web services

Web services communities are virtual clusters that agglomerate Web services with the same functionality. However, selecting the best community to deal with is challenging to both users and providers. Reputation has been widely used for evaluating and ranking candidates. In this paper, we introduce a reputation-based Web services community architecture and define some of the performance metrics that are needed to assess the reputation of a Web service community as perceived by the users and providers.

Sensor Network and GeoSimulation: Keystones for Spatial Decision Support Systems

Natural hazards and man-made disasters are victimizing large numbers of people and causing significant social and economical losses. By developing efficient Spatial Decision Support Systems (SDSS), managers will be efficiently assisted in identifying and managing impending hazards. This goal could not be reached without addressing significant challenges, including data collection, management, discovery, translation, integration, visualization, and communication. As an emergent technology, sensor networks have proven efficiency in providing geoinformation for any decision support system particularly those aiming to manage hazardous events. Thanks to their spatially distributed nature, these networks could be largely deployed to collect, analyze, and communicate valuable in-situ spatial information in a timely fashion. Since some decisions are expected to be taken onthe-fly, the right data must be collected by the right set of sensors at the right time. In addition to saving the limited resources of sensor networks, this will speed up the usability of data especially if this data is provided in the right format. In order to boost the decision support process, a thorough understanding and use of the semantics of available and collected heterogeneous data will obviously help to determine what data to use and how confident one can be in the results ultimately. An appropriate representation of the geoinformation should enhance this process. Data collected by sensors is often associated with spatial information, which makes it voluminous and difficult to assimilate by human being. In critical situations, the hazard manager has to work under pressure. Coping with such collected data is a demanding task and may increase the risk of human error. In this context, Geosimulation emerges as an efficient tool. Indeed, mapping the collected data into a simulated environment which takes into account the spatial dimension may dramatically help the hazard manager to easily visualize the correlation between data collected by sensors and the geospatial constraints. In this chapter, we first present fundamental concepts of SDSS and the most important challenges related to their development. Second, we outline the sensor network technology as an emergent tool for leveraging SDSS. Third, we present the Geosimulation approach as another keystone to enhance SDSS. In this part, we summarize the current opportunities, research challenges, and potential benefits of this technique in SDSS. Finally, for better efficiency, we propose an encoding that emphasizes the semantics of available data and

La géo-simulation multi-agent : Un support pour la planification dans un environnement réel

In this paper, we propose an agent-based geo-simulation approach to assist planners when planning under strong spatial constraints in a real space. The approach consists in drawing a parallel between the real environment (for example, a forest on fire) and the simulated environment based on georeferenced data. This virtual environment uses software agents which are aware of the space and equipped with advanced cognitive capabilities, in order to support planning and coordination of operations within the real terrain. Since plans in the studied case (forest fires) are mainly paths, we also propose a new approach based on agent geosimulation in order to solve particular Pathfinding problems. Finally, when applying our approach to firefighting, we use MAGS as a simulation platform and Prometheus as afire simulator.