Apprentissage Multi Agent à Mémoire Bornée

National audienceNous abordons ici l'apprentissage supervisé en ligne collaboratif dans une société d'agents. La démarche adoptée est celle du maintien collectif d'une notion de consistance, ici correspondant au maintien, par révision de l'hypothèse courante, d'une hypothèse d'erreur empirique nulle. L'hypothèse prend la forme d'une formule de taille réduite et la révision repose sur les exemples mémorisés. Lors de précédents travaux, dans le cadre du projet SMILE, tous les exemples rencontrés par un agent, plus ceux transmis par d'autres agents, étaient mémorisés. Dans le travail présenté ici, chaque agent a une mémoire bornée, limitant ainsi le nombre d'exemples maintenus dans la mémoire de chaque agent. Nous proposons une adaptation du mécanisme de révision collective de SMILE prenant en compte cette restriction. Plusieurs variantes de ce mécanisme, se différenciant en particulier selon la méthode utilisée par les agents pour gérer leur mémoire, sont explorées expérimentalement. Nous observons alors dans quelle mesure ces restrictions en mémoire peuvent être dépassées résultant parfois de manière surprenante en une erreur en test plus faible que sans ces restrictions

Dynamic Interest Points: A Formalism to Identify Areas to Patrol within a Continuous Environment

The multi-agent patrolling problem consists of positioning agents to minimize the idleness, which represents the time difference between two visits of a same location by at least one agent.In the literature, these locations are defined manually by setting static nodes within a graph representation. However, in the context of patrolling a continuous environment, using static nodes cannot guarantee the coverage of the whole environment. In this article, we propose to discretize the continuous environment in order to generate dynamic waypoints called interest points (IP). We prove that these dynamic IP guarantee the coverage of the whole environment while dealing with its topography and the agent's observation range. We evaluated and compared our approach by benchmarking patrolling environment dealing with different observation ranges. Experiments show that dynamic IP locations are adaptive and more efficient to locate high idleness areas compared to static IP approach

08361 Abstracts Collection -- Programming Multi-Agent Systems

From 31th August to 5th September, the Dagstuhl Seminar 08361 ``Programming Multi-Agent Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

LSTM Path-Maker : une nouvelle stratégie pour la patrouille multiagent basée sur l'architecture LSTM

National audienceAbstract For over a decade, the multi-agent patrol task has received a growing attention from the multi-agent community due to its wide range of potential applications. However, the existing patrolling-specific algorithms based on deep learning algorithms are still in preliminary stages. In this paper, we propose to integrate a recurrent neural network as part of * Paper presented at the 52nd Hawaii International Conference on System Sciences (HICSS52 2019), titre, résumé et mots-clés en français ajou-tés. a multi-agent patrolling strategy. Hence we proposed a formal model of an LSTM-based agent strategy named LSTM Path Maker. The LSTM network is trained over simulation traces of a coordinated strategy, then embedded on each agent of the new strategy to patrol efficiently without communicating. Finally this new LSTM-based strategy is evaluated in simulation and compared with two representative strategies : a coordinated one and a reactive one. Preliminary results indicate that the proposed strategy is better than the reactive.Depuis plus d'une décennie, la tâche de la patrouille mul-tiagent a attiré l'attention de la communauté multiagent de manière croissante, en raison de son grand nombre d'applications potentielles. Cependant, les algorithmes ba-sés sur des méthodes d'apprentissage profond pour traiter cette tâche sont à ce jour peu développés. Dans cet article, nous proposons d'intégrer un réseau de neurone récurrent à une stratégie de patrouille multiagent. Ce faisant, nous avons proposé un modèle formel de stratégie d'agent basée sur l'architecture LSTM, que nous avons nommé LSTM-Path-Maker. Le réseau LSTM est entraîné sur des traces de simulation d'une stratégie coordonnée et centralisée, puis embarqué dans chaque agent en vue de patrouiller effica-cement sans communication. Enfin, cette nouvelle stratégie basée sur l'architecture LSTM est évaluée en simulation et comparée d'une part à une stratégie coordonnée et d'autre part à une stratégie réactive. Les résultats préliminaires in-diquent que la stratégie proposée est meilleure que la stra-tégie réactive

Decentralized multi-agent patrolling strategies using global idleness estimation

International audienceThis paper presents preliminary results in the challenge of developing decentralised strategies approaching the performances of centralised ones. Indeed, the latter are better than the former due to centralisation of information. The approach studied here involves the estimation of node idlenesses derived from the paths of all agents, also known as real idlenesses, on the basis of those derived from the path of each agent considered alone, also known as individual idlenesses. This relation between real and individual idlenesses is learnt using traces of execution of a centralised strategy by optimising an error criterion. The strategy thereupon, uses online the learnt relation and is assessed according to certain evaluation criteria. The results indicate that such a relation between perceived and real idlenesses is not a function, leading to large values of the fitting criterion. Finally, the assessment of the strategy shows that performances are good in terms of mean interval but unsatisfactory in terms of quadratic mean interval

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

An optimized fuzzy logic model for proactive maintenance

Fuzzy logic has been proposed in previous studies for machine diagnosis, to overcome different drawbacks of the traditional diagnostic approaches used. Among these approaches Failure Mode and Effect Critical Analysis method(FMECA) attempts to identify potential modes and treat failures before they occur based on subjective expert judgments. Although several versions of fuzzy logic are used to improve FMECA or to replace it, since it is an extremely cost-intensive approach in terms of failure modes because it evaluates each one of them separately, these propositions have not explicitly focused on the combinatorial complexity nor justified the choice of membership functions in Fuzzy logic modeling. Within this context, we develop an optimization-based approach referred to Integrated Truth Table and Fuzzy Logic Model (ITTFLM) that smartly generates fuzzy logic rules using Truth Tables. The ITTFLM was tested on fan data collected in real-time from a plant machine. In the experiment, three types of membership functions (Triangular, Trapezoidal, and Gaussian) were used. The ITTFLM can generate outputs in 5ms, the results demonstrate that this model based on the Trapezoidal membership functions identifies the failure states with high accuracy, and its capability of dealing with large numbers of rules and thus meets the real-time constraints that usually impact user experience.Comment: 16 pages in single column format, 11 figures, 12th International Conference on Artificial Intelligence, Soft Computing and Applications (AIAA 2022) December 22 ~ 24, 2022, Sydney, Australi