Alexis Drogoul

Multi-agent patrolling: an empirical analysis of alternative architectures

A group of agents can be used to perform patrolling tasks in a variety of domains ranging from computer network administration to computer wargame simulations. Despite its wide range of potential applications, multi-agent architectures for patrolling have not been studied in depth yet. First state of the art approaches used to deal with related problems cannot be easily adapted to the patrolling task specificity. Second, the existing patrolling-specific approaches are still in preliminary stages. In this paper, we present an original in-depth discussion of multi-agent patrolling task issues, as well as an empirical evaluation of possible solutions. In order to accomplish this study we have proposed different architectures of multi-agent systems, various evaluation criteria, two experimental scenarios, and we have implemented a patrolling simulator. The results show which kind of architecture can patrol an area more adequately according to the circumstances.

GAMA: a simulation platform that integrates geographical information data, agent-based modeling and multi-scale control

The agent-based modeling is now widely used to study complex systems. Its ability to represent several levels of interaction along a detailed (complex) environment representation favored such a development. However, in many models, these capabilities are not fully used. Indeed, only simple, usually discrete, environment representation and one level of interaction (rarely two or three) are considered in most of the agent-based models. The major reason behind this fact is the lack of simulation platforms assisting the work of modelers in these domains. To tackle this problem, we developed a new simulation platform, GAMA. This platform allows modelers to define spatially explicit and multi-levels models. In particular, it integrates powerful tools coming from Geographic Information Systems (GIS) and Data Mining easing the modeling and analysis efforts. In this paper, we present how this platform addresses these issues and how such tools are available right out of the box to modelers.

Multi-Agent Simulation as a Tool for Modeling Societies: Application to Social Differentiation in Ant Colonies

This paper presents the notion of multi-agent simulation that is based on the definition of computational agents that represent individual organisms (or groups of organisms) in a one to one correspondence. We discuss the properties of multi-agent simulation. We then present a multiagent simulation system based on the definition of reactive agents whose behavior is governed by the selection of simple competing tasks due to stimuluss perception. An example of a simulation of an ant colony follows as an illustration of the multiple domains in which multi-agent simulation may be used.

GAMA 1.6: Advancing the Art of Complex Agent-Based Modeling and Simulation

Agent-based models tend to be more and more complex. In order to cope with this increase of complexity, powerful modeling and simulation tools are required. These last years have seen the development of several platforms dedicated to the development of agent-based models. While some of them are still limited to the development of simple models, others allow to develop rich and complex models. Among them, the GAMA modeling and simulation platform is aimed at supporting the design of spatialized, multiple-paradigms and multiple-scales models. Several papers have already introduced GAMA, notably in earlier PRIMA conferences, and we would like, in this paper, to introduce the new features provided by GAMA 1.6, the latest revision to date of the platform. In particular, we present its capabilities concerning the tight combination of 3D visualization, GIS data management, and multi-level modeling. In addition, we present some examples of real projects that rely on GAMA to develop complex models.

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

In this paper, we introduce the GAMA (Gis & Agent-based Modelling Architecture) simulation platform, which aims at providing field experts, modellers, and computer scientists with a complete modelling and simulation development environment for building spatially explicit multi-agent simulations. The most important requirements of spatially explicit multi-agent simulations that our platform fulfils are: (1) the ability to transparently use complex Geographical Information System (GIS) data as an environment for the agents; (2) the ability to handle a vast number of (heterogeneous) agents (3); the ability to offer a platform for automated controlled experiments (by automatically varying parameters, recording statistics, etc.); (4) the possibility to let non-computer scientists design models and interact with the agents during simulations. While still in its implementation phase, the platform is currently used for two main applications. One is about the modelling of the spread of avian influenza in a province of North Vietnam in collaboration with CIRAD (French Agricultural Research Centre working for International Development). Its goal is to simulate the poultry value chain of a whole province using geolocalised data, and to use this to optimise a monitoring network. A second application conducted with the Institute for Marine Geology and Geophysics (VAST, Hanoi) is about using an interactive simulation for supporting decision-making during urban post-disaster situations. This application relies on geolocalised data as well, and requires facilities of interaction between users and the simulation.

The robocup physical agent challenge: Phase i

Traditional AI research has not given due attention to the important role that physical bodies play for agents as their interactions produce complex emergent behaviors to achieve goals in the dynamic real world. The RoboCup Physical Agent Challenge provides a good test bed for studying how physical bodies play a significant role in realizing intelligent behaviors using the RoboCup framework (Kitano et al., 1995). In order for the robots to play a soccer game reasonably well, a wide range of technologies needs to be integrated, and a number of technical breakthroughs must be made. In this article, we present three challenging tasks as the RoboCup Physical Agent Challenge Phase I: (1) moving the ball to the specified area (shooting, passing, and dribbling) with no, with stationary, or with moving obstacles; (2) catching the ball from an opponent or a teammate (receiving, goal keeping, and intercepting); and (3) passing the ball between two players. The first two tasks are concerned with single-agent skills,...

Applying an Agent-Oriented Methodology to the Design of Artificial Organizations: A Case Study in Robotic Soccer

The multi-agent paradigm is widely used to provide solutions to a number of organizational problems related to the collective achievement of one or more tasks. All these problems share a common difficulty of design: how to proceed from the global specification of a collective task to the specification of the local behaviors to be provided to the agents? We have defined the Cassiopeia method whose specificity is to articulate the design of a multi-agent system around the notion of organization. This paper reports the use of this method for designing and implementing the organization of a soccer-playing robotic team. We show why we chose this application and how we designed it, and we discuss its interest and inherent difficulties in order to clearly express the needs for a design methodology dedicated to DAI.

When Agents Emerge from Agents: Introducing Multi-scale Viewpoints in Multi-agent Simulations

Current multi-agent simulations, which have many individual entities evolve and interact, often lead to the emergence of local groups of entities, but provide no means of manipulating them. To our mind, giving full a sense to multi-agent simulations would consist though in making use of such dynamically created potential groups, by granting them an existence of their own, and specific behaviours. Brought into operation, they would provide effective and new tools for modelling purposes : for instance, encapsulating physical laws which depend on scaling, thus giving means of apprehending micro-macro links in multi-agent simulations, or introducing the experimentater’s viewpoints on the specific behaviours of such groups. We thus have to imagine how to give any set of agents means of becoming aware of their mutual interaction, and giving birth to new types of agents out of their collective activity. In other words we look for a computer equivalent to our own emergence recognition ability. We present here a conceptual reflexion on such matters in the light of our own experience in the development of the RIVAGE project at Orstom, which aims at simulating runoff and infiltration processes. Conversely, we believe that the development of our methods in such a novel and original field of research as the multi-agent simulation of pure physical processes will provide new ideas and tools useful for many multi-agent architectures and modelling purposes.

When ants play chess (Or can strategies emerge from tactical behaviours

Because we think that plans or strategies are useful for coordinating multiple agents, and because we hypothesise that most of the plans we use are build partly by us and partly by our immediate environment (which includes other agents), this paper is devoted to the conditions in which strategies can be viewed as the result of interactions between simple agents, each of them having only local information about the state of the world. Our approach is based on the study of some examples of reactive agents applications. Their features are briefly described and we underline, in each of them, what we call the emergent strategies obtained from the local interactions between the agents. Three examples are studied this way: the eco-problem-solving implementations of Pengi and the N-Puzzle, and the sociogenesis process occurring in the artificial ant colonies that compose the MANTA project. We then consider a typical strategical game (chess), and see how to decompose it through a distributed reactive approach called MARCH. Some characteristics of the game are analysed and we conclude on the necessity to handle both a global strategy and local tactics in order to obtain a decently strong chess program.

How to combine reactivity and anticipation: the case of conflicts resolution in a simulated road traffic

In this article we present the method used to solve the conflicts that can happen between agents that represent simulated drivers in a simulated road traffic. This work is part of the ARCHISIM project, which aims at both simulating a realistic traffic evolution and making the behaviour of the simulated drivers credible for a human driver placed in a driving simulator. After having categorized the types of conflicts that can happen, and the constraints that determine the choice of a solving method, we propose a method that combines reactivity and anticipation. This method is based on the works of driving psychologists who work in the INRETS institute. We offer an exprimental validation of this method with respect to real data and discuss its advantages in the perspective of largest applications.