Bilateral negotiation of a meeting point in a maze: Demonstration

International audienceNegotiation between agents aims at reaching an agreement in which the conflicting interests of agents are accommodated. In this demonstration, we present a concrete negotiation scenario where two agents are situated in a maze and the negotiation outcome is a cell where they will meet. Their individual preferences match with a minimal distance computed from their partial knowledge of the environment. We illustrate a bargaining protocol which allows agents to submit several proposals at the same round and a negotiation strategy which consists in starting from the best deal for the agent and then concedes. The path between the agents emerges from the repeated negotiations

Bilateral negotiation of a meeting point in a maze

International audienceNegotiation between agents aims at reaching an agreement in which the conflicting interests of agents are accommodated. In this paper, we present a concrete negotiation scenario where two agents are situated in a maze and the negotiation outcome is a cell where they will meet. Based on their individual preferences (a minimal distance from their location computed from their partial knowledge of the environment), we propose a negotiation protocol which allows agents to submit more than two proposals at the same time and a conciliatory strategy. Formally, we prove that the agreement reached by such a negotiation process is Pareto- optimal and a compromise, i.e. a solution which minimizes the maximum effort for one agent. Moreover, the path between the two agents emerges from the repeated negotiations in our experiments

Réduire l'arbitraire par la négociation quitte à concéder

National audienceConflicts are first-class citizen in Multi-Agents Systems and negotiation allows to handle these conflicts. We consider here the decision of agents having partial preferences since some alternatives are equivalent or incomparable. In order to evaluate the alternatives, we refine here the Pareto-optimality criteria by defining two kinds of compromise. We propose in this paper a negotiation game, i.e. a protocol and two strategies~: a conciliatory one and a temporizing one. Finally, we prove that the first one is social optimal while the second one is self-interested. Our experiments show that the temporizing strategy is dominant in very restrictive conditions and the number of agreements is smaller than the outcomes of a single agent decision and so less arbitrary.Les oppositions sont intrinsèques aux systèmes multi-agents (SMA) et la négociation est un processus permettant de résoudre ces conflits. Nous nous intéressons ici à la négociation bilatérale mono-attribut. Elle consiste en un échange d'offres pour résoudre un problème de décision collective où les préférences sont partielles, certaines alternatives étant incomparables ou équivalentes. Afin d'évaluer les alternatives du point de vue de la société d'agents, nous raffinons ici le critère de Pareto-optimalité en définissant deux types de compromis. Nous proposons dans cet article un jeu de négociation bilatérale, c'est-à-dire un protocole et deux stratégies qui s'appuient sur des concessions : l'une conciliante et l'autre temporisatrice. Finalement, nous montrons que la première est meilleure socialement et la seconde meilleure individuellement. Nos expériences montrent que temporiser est une stratégie dominante dans un nombre très restreint de situations. De plus, nous observons que le résultat de la négociation est plus restreint que celui d'une décision mono-agent et donc réduit l'arbitraire de la décision

An adaptive multi-agent system for task reallocation in a MapReduce job

International audienceWe study the problem of task reallocation for load-balancing of MapReduce jobs in applications that process large datasets. In this context, we propose a novel strategy based on cooperative agents used to optimise the task scheduling in a single MapReduce job. The novelty of our strategy lies in the ability of agents to identify opportunities within a current unbalanced allocation, which in turn trigger concurrent and one-to-many negotiations amongst agents to locally reallocate some of the tasks within a job. Our contribution is that tasks are reallocated according to the proximity of the resources and they are performed in accordance to the capabilities of the nodes in which agents are situated. To evaluate the adaptivity and responsiveness of our approach, we implement a prototype test-bed and conduct a vast panel of experiments in a heterogeneous environment and by exploring varying hardware configurations. This extensive experimentation reveals that our strategy significantly improves the overall runtime over the classical Hadoop data processing

Allocation équitable de tâches pour l'analyse de données massives

L'URL de l'ouvrage est la suivante:http://www.cepadues.com/livres/jfsma-2016-systemes-multi-agents-simulations-9782364935594.htmlInternational audienceMany companies are using MapReduce applications to process very large amounts of data. Static optimization of such applications is complex because they are based on user-defined operations, called map and reduce, which prevents some algebraic optimization. In order to optimize the task allocation, several systems collect data from previous runs and predict the performance doing job profiling. However they are not effective during the learning phase, or when a new type of job or data set appears. In this paper, we present an adaptive multiagent system for large data sets analysis with MapReduce. We do not preprocess data and we adopt a dynamic approach, where the reducer agents interact during the job. In order to decrease the workload of the most loaded reducer - and so the execution time - we propose a task re-allocation based on negotiation.De nombreuses entreprises utilisent l'application MapReduce pour le traitement de données massives. L'optimisation statique de telles applications est complexe car elles reposent sur des opérations définies par l'utilisateur, appelées map et reduce, ce qui empêche une optimisation algébrique. Afin d'optimiser l'allocation des tâches, plusieurs systèmes collectent des données à partir des exécutions précédentes et prédisent les performances en faisant une analyse de la tâche. Cependant, ces systèmes ne sont pas efficaces durant la phase d'apprentissage ou lorsqu'un nouveau type de tâches ou de données apparait. Dans ce papier, nous présentons un système multi-agents adaptatif pour l'analyse de données massives avec MapReduce. Nous ne pré-traitons pas les données et adoptons une approche dynamique où les agents reducers interagissent durant l'exécution. Nous proposons une ré-allocation des tâches basée sur la négociation pour parvenir à faire décroitre la charge de travail du plus chargé des agents reducers et ainsi réduire le temps d'exécution

Stratégie situationnelle pour l'équilibrage de charge

National audienceWe study a novel location-aware strategy for distributed systems where cooperating agents perform the load-balancing. The strategy allows agents to identify opportunities within a current unbalanced allocation, which in turn triggers concurrent and one-to-many negotiations amongst agents to locally reallocate some tasks. The tasks are reallocated according to the proximity of the resources and they are performed in accordance with the capabilities of the nodes in which agents are situated. This dynamic and ongoing negotiation process takes place concurrently with the task execution and so the task allocation process is adaptive to disruptions (task consumption, slowing down nodes). We evaluate the strategy in a multi-agent deployment of the MapReduce design pattern for processing large datasets. Empirical results demonstrate that our strategy significantly improves the overall runtime of the data processing.Nous étudions une stratégie qui tient compte de la localité des ressources pour équilibrer les charges dans un système distribué. Cette stratégie permet aux agents coopératifs d'identifier une allocation non équilibrée, voire de déclencher des enchères concurrentes pour réallouer localement certaines des tâches. Les tâches sont réallouées en tenant compte de l'accessibilité des ressources pour les agents ; elles sont exécutées conformément aux capacités des noeuds de calcul sur lesquels se trouvent les agents. Ce processus de négociation dynamique et continu est concurrent à l'exécution des tâches, ce qui permet d'adapter l'allocation des tâches aux perturbations (exécution de tâche, chute de performance d'un nœud). Nous évaluons cette stratégie dans le cadre du déploiement multi-agents de MapReduce. Ce patron de conception permet le traitement distribué de données massives. Les résultats empiriques démontrent que notre stratégie améliore significativement le temps d'exécution du traitement d'un jeu de données

A Location-Aware Strategy for Agents Negotiating Load-balancing

International audienceWe study a novel location-aware strategy for distributed systems where cooperating agents perform the load-balancing. The strategy allows agents to identify opportunities within a current unbalanced allocation , which in turn triggers concurrent and one-to-many negotiations amongst agents to locally reallocate some tasks. The tasks are reallocated according to the proximity of the resources and they are performed in accordance with the capabilities of the nodes in which agents are situated. This dynamic and ongoing negotiation process takes place concurrently with the task execution and so the task allocation process is adaptive to disruptions (task consumption, slowing down nodes). We evaluate the strategy in a multi-agent deployment of the MapReduce design pattern for processing large datasets. Empirical results demonstrate that our strategy significantly improves the overall runtime of the data processing

PET-BIDS, an extension to the brain imaging data structure for positron emission tomography

The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets, serving not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PET) data, also known as PET-BIDS, and share several open-access datasets curated following PET-BIDS along with tools for conversion, validation and analysis of PET-BIDS datasets