Section: New Results
Selforganization & Swarm Intelligence
There are many situations which require us to deal with strongly interacting, massively parallel and decentralized systems. This is what brought us to work in the field of selforganized systems. These systems are described by various formal models such as reactive multiagent systems or cellular automata. The work of the team mixes both theoretical and experimental approaches and seeks to provide applications in the field of image processing, localization and tracking, and bioinspired problem solving.
Multiagent modeling of the impact of user behavior in dynamical networks
Participants : Vincent Chevrier, Julien Siebert.
L. Ciarletta (Madynes team) is external collaborator for this action.
In distributed, dynamic networks and applications, such as PeertoPeer (P2P) networks or Mobile Ad hoc NETworks (MANET), the users behaviour has a strong influence on the quality of service (QoS). Furthermore, the QoS also influences the users behaviour. In worst cases these mutual influences could lead the system to crash. We propose a novel approach to model relationships between users and QoS.
We propose to use models and simulators from both fields (computer networks and human behaviour) and to make them interact. This raises some coordination issues (synchronization, compatibility and coherence).
We first implemented this proposal by adapting an existing simulator (PeerfactSim). This is called a strong coupling approach. We undertook experiments to study the influence of the rate of cooperation of user and the rate of pollution of data on the functioning of the network [15] . These first experiments show us the limits of such a strong and centralized approach.
So we propose to use the Agent and Artefact paradigm in order to deal with the coordination issues and to make the interaction of the existing simulators and models decentralized and as simple as possible. We have developed a decentralized coordination framework called AA4MM[27]
The aim of this framework is to make heterogeneous simulators interact in such a way that coordination and integration issues are transparent for the people involved in the simulation process. When someone wants to include an existing simulator within the AA4MM framework, only few changes are needed.
Moreover, the framework is based upon a decentralised coordination model we proposed. This has been formalised (in EventB) in collaboration with Joris Rehm(Email: joris.rehm@loria.fr Mosel Team)[45] . This specification has been used to prove that coordination occurs with a finite number of simulators and that no deadlock is possible.
Source code and JMS implementation have been developed in collaboration with Virginie Galtier Ciarletta(Email: galtier@supelec.fr Supelec Metz). Examples, demonstrations and the first realase of the framework are available on www.loria.fr/ siebertj/aa4mm/aa4mm.html
This framework is currently used in order to study the impact of the user mobility on the performances of MANET.
Using global patterns to control a reactive multiagent system
Participants : Christine Bourjot, Vincent Chevrier, François Klein.
In a reactive multiagent system (MAS), the link between the collective behaviour and the behaviours of the individuals who make up this system is difficult to set up. We support the concept of driving the behaviour of a MAS by a control approach. In order to obtain this control, we act on the MAS by using information about its global behaviour.
In [40] , we proposed to model the global dynamics of the MAS as a graph of states, and to use reinforcement learning tools help to compute a policy. This policy indicates which action to choose based on the current state and a target behaviour.
The originality of our proposal lies in the global level description of the MAS's dynamics. Thus the different behaviours of the MAS are expressed, in our proposal, at the same description level as the one of the target behaviour. We developed a clustering measure to identify a current behaviour in a MAS representing pedestrians.
We studied this approach, and its control performance, that is its capacity to reach a target behaviour even if the MAS is initialised in a stable, undesired behaviour. It remains efficient when the MAS undergoes a perturbation. The use of luring agents as control actions is studied too. The proposal provides good control performance on a study MAS and achieves a target behaviour more frequently than other tested approaches. The details of the experiments are provided in the Phd Thesis [2] .
Study of a deterministic nonlinear way for ant algorithms modeling
Participants : Rodolphe Charrier, François Charpillet, Christine Bourjot.
This work [1] , [20] , [19] is an attempt to formalize swarm intelligence under the angle of the science of complex systems. Its purpose is to design a generic model of situated reactive multiagent systems capable of explaining collective behaviors resulting from autoorganization mechanisms such as those observed in natural systems like birds flocking or ants foraging.
The model we propose integrates decisional mechanisms inspired from coupled map lattice (CML) imagined in 1986 by the physicist Kuhiniko Kaneko for the study chaotic spacetime phenomena. Roughly speeking CML can be seen as cellular automata in continuous space in which the transitions are controlled by chaotic nonlinear functions like the logistic function (the logistic map is a polynomial mapping, often cited as an archetypal example of how complex chaotic behaviour can arise from very simple nonlinear dynamical equations).
This source of inspiration has several advantages: the mathematical framework is well suited to model dynamical systems such those we want to study and interesting mathematical results are available.
Cellular Automata as a model of Complex Systems
Participant : Nazim Fatès.
Cellular automata can be seen as the environment part of a multiagent system. Formally, they are discrete dynamical systems and they are widely used to model natural systems. Classically they are run with perfect synchrony; i.e. , the local rule is applied to each cell at each time step. A possible modification of the updating scheme consists in applying the rule with a fixed probability, called the synchrony rate.
Phase transitions
It has been shown previously that the updating method of a cellular automaton could produce a discontinuity in the behaviour of the cellular automaton. We investigated the nature of this change of behaviour using MonteCarlo numerical simulations. For a stochastic version of the GreenbergHastings CA, we showed that the phenomenon is a phase transition whose critical exponents are in good agreement with the predicted values of directed percolation [22] . . We wrote a short survey to gather the references relevant to cellular automata and critical phenomena [6] . We also contributed to a collective book dedicated to the Game of Life edited by A. Adamatzky. Our chapter, entitled “Does Life resist desynchronisation?” examines the behaviour of the wellknown cellular automaton under asynchronous updating [39] .
A bioinspired model of aggregation
These phase transitions were also observed in the context of bioinspired computing. We examined how to model cellular societies such as the Dictyostelium Discoideum amoebae . We proposed a simple model of their behaviour, which allows to group a great number of agents at the same localisation without any need for a centralised control [7] .
The influencereaction method for modelling multiagent systems
Participants : Vincent Chevrier, Nazim Fatès, Olivier Simonin.
It is a wellknown problem that there exists no agreement in the scientific community on how multiagent systems should be defined formally. The practise so far has been either to use an ad hoc formalism to describe a model, or, to present a model informally and to analyse the simulations obtained with a particular simulation platform. As a result, two major drawbacks appear: (a) reproducing the experiments on another platform is difficult, if not impossible, since one needs to have all the implicit parameters of a simulation in hand (e.g., the order of updating of the agents), (b) it is not clear whether the behaviour observed is due to the rules defining the agents and the environment or due to the “simulation scheme” that governs the interaction between the components of a multiagent system.
To tackle this problem, we focused our efforts on two directions:

We examine what is the relationship between cellular automata and multiagent systems. A first method to automatically translate a reactive multiagent model into a cellular automaton was proposed, see ICAART publication [28] .

We propose a framework for describing multiagent systems as discrete dynamical systems. As a starting point, we have restricted this study to very simple agents where the cells of environement are binary and where the agent's actions are restricted to turning left/right, moving forward and changing the state of the cells on which they are located (submitted).
Digital Pheromone based Algorithms
Swarm intelligence emerges from the interactions performed by a large number of simple agents. In this context, we are interested by algorithms relying on the marking of the environment, which is used as a common memory (a wellknown example is pheromone dropped by ants to perform indirect communication). Such an approach is now called digital pheromones, as marks are values that can be read and written by agents in cells of a discrete environment. In this framework we address the following challenge: understanding and designing selforganized systems, deploying physically these models and enable their interaction with real robots.
Theoretical study of Antbased algorihms for the Patrolling problem
Participants : Arnaud Glad, Olivier Simonin, François Charpillet, Olivier Buffet.
We proposed in 2007 a reactiveagent algorithm, called EVAP, to deal with multiagent patrolling, which is based on the marking and evaporation of a digital pheromone (cf. publications ICTAI'07, JFSMA'07). During the simulations carried out to measure the performances of EVAP, we identified that the system can selforganize towards an optimal behavior. In particular we observed that agents tends to follow stable cycles corresponding to a hamiltonian covering of the environment. We then established the mathematical proof that the system can stabilize only in cycles, one per agent, having the same lenght, cf. publication in ECAI'2008. Moreover, we recently introduced new heuristics in the agent behavior that improve dramaticaly the time for convergence, and we proved that under some hypotheses the system always converge to stable cycles (these results have been published to SASO [23] and JFPDA [32] conferences). This work is in line with the PhD thesis of Arnaud Glad (since dec. 2007) which concerns the understanding and the optimization of selforganization resulting from such algorithms.
Collective construction of artificial potential fields (APF)
Participants : Olivier Simonin, François Charpillet.
In the context of pathplanning, we rewrited the classical Artificial Potential Field (APF) computation proposed by Barraquand & Latombe in an asynchronous and collective construction by reactive agents. We proved this model builds an optimal APF while dealing with the collective forraging problem (research and transport of resources by a set of autonomous agents/robots). In 2008, we extended simulations and measures by introducing dynamic environments (moving obstacles). Then we have shown that our approach is more efficient in static environments than the classical ant algorithm, and need to be extended with a behavioral heuristics to compete with it in dynamic environments. An article is under submission to TAAS international journal. We done this work in collaboration with Eric Thierry from LIP, ENS Lyon.
Swarm Robotics and Active Environments
Participants : Olivier Simonin, François Charpillet, Alexis Scheuer, Antoine Bautin, Nicolas Beaufort, Romain Mauffray.
The Maia team acquired in the end of year 2008 six Kheperas III mobile robots, in order to study and validate several multirobot models. In particular we aim at studying reactive coordination and swarm models (as presented below). In 2009, we obtained ten more Kheperas III robots throught the CPER MIS Action. Thus we dispose now of a first swarm robotic system, that will allow us to focus our work on multiagent models. In the same time, we explored different ways to tackle the challenge of implementing environmentbased models with real robots. It requires to allow robots to read and write information in the environment, for instance digital pheromones.
MultiRobot cooperation

Reactive coordination with Potential Fields . We investigate the multirobot navigation problem funded on artificial potential fields (APF) and real signals emission. We first considered the definition and the solving of the “robotpushing” task. It extends the boxpushing task by replacing the box for an immobilized robot, which can send signals to be pushed in a desired direction by a swarm of reactive robots [11] (Mechatronics Journal). During the Master internship of Kaouther Bouzouita, we analysed and proposed generic mathematical models of the potential fields and signals used in the collective control. It allowed us to optimize infrared settings and to validate the control with Kheperas III robots (publication in preparation).

Multirobot Deployment and Mapping . As introduced in Sec. 8.1.5.7 , we started through the ANR Cartomatic project the study of multirobot deployment and mapping. We aim at exploring multiagent models for fast exploration of multiroom spaces, and to improve accuracy of the localization of robots and objects. This work is in line with the PhD thesis of Antoine Bautin, starting in november 2009.

Human inspired heuristics for robots . This work concerns a new collaboration with E. Zibetti from Paris 8 university and CHART Laboratory (Cognition Humaine et Artificielle), and N. Sabouret and A. Beynier from Paris 6. It concerns the identification of human heuristics face to spatial and cooperation problems, and their translation to mobile robots. The approach and first experimental results are introduced in [34] . We also plan to submit in january 2010 a young researcher ANR project, headed by E. Zibetti.
Interactive environments for bioinspired models
We aim at defining and studying bioinspired robots able to interact through their environment by reading, writing or modifying it (as laying pheromone trails, following a signal, etc.). The challenge is to translate theoritical and/or simulated models to real systems in order to define new robotics abilities. We started two original approaches, one relying on the design of an interactive table (funded by INRIA ADT ROMEA 8.1.5.8 ), another consisting in paving the ground with intelligent tiles.
Interactive Table : For this purpose we developp a first original environment based on an interactive table. It will allow robots to read and write information on their environment without requiring/communicating their location (see ROMEA project for the design of the table, section 8.1.5.8 ). Robots evolve on the table (2m x 2m) and mark their presence through infrared emissions. Such a support should allow the study of continous and discrete active environment models, and also to consider the man in the loop.
Intelligent Tiles : In this second approach we consider large real environments (indoor), and study how to deploy discrete multiagent models. To this end we propose to pave the floor with “communicating” and autonomous tiles . Each tile is defined to ensure communication with its neighbours, and to allow a possible supported agent to read and write information. As a consequence tiles can be exploited to extend agents' perceptions and communications, and to physically implement bioinspired algorithms. A first Tile model has been defined and evaluated using a simulator. In particular we rewrited the SatisfactionAltruism model (Simonin & Ferber 2001) by spliting its behavior between a tile behavior and a simplified agent behavior. These first results were presented in a paper published to ICAART'09 international conference [25] . In 2009, during the Master internship of Romain Mauffray, we developed a tiles' emulator and performed some experiments with real mobiles robots (Kheperas III), which validated the interest and the efficiency of the approach.
Safe decentralised control of intelligent vehicles (the platooning task)
Participants : Alexis Scheuer, Olivier Simonin, François Charpillet, Arnaud Glad.
We consider decentralised control methods to operate autonomous vehicles at close spacings to form a platoon. We study models inspired by the flocking approach, where each vehicle computes its control from its local perceptions. Such decentralised approaches provide robust and scalable solutions.
However, collision avoidance needs to be studied to be guaranteed. Stability is also an open problem for platoon of more than three vehicles: do oscillations appear when the motion of some vehicles are disrupted?
Formal specification and verification of situated multiagent systems
This action is related to the ANR project TACOS (see Sec. 8.1.4 ), which started in January 2007. It is a collaboration with the DEDALE project of LORIA Lab. We are interested in formally specifying and studying situated multiagent systems. This is an open problem, which is particularly interesting when designing critical decentralised systems. Our approach relies on the formal specification – in B language – of the influencereaction model, which is a generic formulation of multiagent systems proposed by Ferber & Muller in 1996.
This specification can be instantiated to prove properties for specific multiagent systems. We considered as example the platooning task: we proved that the Daviet and Parent constant coefficient controller [46] ensures collision avoidance with a simple longitudinal platooning model (perceptions and actions are synchronous and without error). This work is presented as an INRIA research report [54] and an article under submission to an international journal.
Safe decentralised control reducing intervehicle distances
We studied a more realistic model of the platooning task, introducing a delay between perceptions and actions and noise/errors in perceptions and actions. Within this modelling, Daviet and Parent controllers [46] show their limitations. We thus proposed a highlevel controller, which transforms any controller into a safe controller, i.e. avoiding collision, and we proved this property. This work has been published in the 2009 IEEE International Conference on Robotics and Automation (ICRA'09) [26] . An extended version of this article, with detailed proof, is available as INRIA research report [53] and will be submitted to an international journal.
Coupling lateral and longitudinal controls
The work presented in the two previous paragraphs focuses on longitudinal control: all the vehicles are moving along a fixed path. When vehicles move in a two dimension space, a lateral controller is needed to steer the vehicles. While lateral and longitudinal controls can be considered separately, the longitudinal control should be done after the lateral control: while turning, a higher intervehicle distance is needed to avoid collision. A lateral control only based on individual sensors will be studied and the current longitudinal control adapted to curvilinear distances. First results were obtained through the cooperation with the team of Pr. D. Matko from Ljubljana University (see PHC Egide 0608) where an approach based on referencepath control was explored, see publication ICINCO'08. In 2009, we started the study of the nonstop crossing of two orthogonal decentralized platoons, through the internship of Sebastien Alouze (Mines de Nancy, initiation a la recherche internship).