A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances

An Energy-Aware Algorithm for Large Scale Foraging Systems

International audienceThe foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of coordinated robots have to find and transport one or more objects to one or more specific storage points. Swarm robotics has been widely considered in such situations, due to its strengths such as robustness, simplicity and scalability. Typical multi-robot foraging systems currently consider tens to hundreds of agents. This paper presents a new algorithm called Energy-aware Cooperative Switching Algorithm for Foraging (EC-SAF) that manages thousands of robots. We investigate therefore the scalability of EC-SAF algorithm and the parameters that can affect energy efficiency overtime. Results indicate that EC-SAF is scalable and effective in reducing swarm energy consumption compared to an energy-aware version of the reference well-known c-marking algorithm (Ec-marking)

Multi‑Agent Foraging: state‑of‑the‑art and research challenges

International audienceThe foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth. First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems. Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic

Exploring unknown environments with multi-modal locomotion swarm

International audienceSwarm robotics is focused on creating intelligent systems from large number of simple robots. The majority of nowadays robots are bound to operations within mono-modal locomotion (i.e. land, air or water). However, some animals have the capacity to alter their locomotion modalities to suit various terrains, operating at high levels of competence in a range of substrates. One of the most significant challenges in bio-inspired robotics is to determine how to use multi-modal locomotion to help robots perform a variety of tasks. In this paper, we investigate the use of multi-modal locomotion on a swarm of robots through a multi-target search algorithm inspired from the behavior of flying ants. Features of swarm intelligence such as distributivity, robustness and scalability are ensured by the proposed algorithm. Although the simplicity of movement policies of each agent, complex and efficient exploration is achieved at the team level

A Java-Based Agent Platform for Programming Wireless Sensor Networks

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A Distributed Foraging Algorithm Based on Artificial Potential Field

International audienceSimple collections of agents that perform collectively and use distributed control algorithms constitute the interests of swarm robotics. A key issue to improve system performances is to effectively coordinate the team of agents. We present in this paper a multi-agent foraging algorithm called Cooperative-Color Marking Foraging Agents (C-CMFA). It uses the coordination rules of the S-MASA (Stigmergic Multi-Ant Search Area) algorithm to (i) speed up the search process and (ii) allow agents to build an optimal Artificial Potential Field (APF) simultaneously while exploring. To benefit from multiple robots, we add one cooperation rule in the algorithm to attract large number of agents to the found food. This algorithm constitutes a distributed and synchronous version of the c-marking algorithm. Simulation results in comparison with the c-marking one show the superiority of C-CMFA in different environment configurations

A Decentralized Ant Colony Foraging Model Using Only Stigmergic Communication

International audienceThis paper addresses the problem of foraging by a coordinated team of robots. This coordination is achieved by markers deposited by robots. In this paper, we present a novel decentralized behavioral model for multi robot foraging named cooperative c-marking agent model. In such model, each robot makes a decision according to the affluence of resource locations, either to spread information on a large scale in order to attract more agents or the opposite. Simulation results show that the proposed model outperforms the well-known c-marking agent model

Stigmergic MASA: A Stigmergy Based Algorithm for Multi-Target Search

International audienceWe explore the on-line problem of coverage where multiple agents have to find a target whose position is unknown, and without a prior global information about the environment. In this paper a novel algorithm for multi-target search is described, it is inspired from water vortex dynamics and based on the principle of pheromone-based communication. According to this algorithm, called Stigmergic MASA (for "Multi Ant Search Area"), the agents search nearby their base incrementally using turns around their center and around each other, until the target is found, with only a group of simple distributed cooperative Ant like agents, which communicate indirectly via depositing/detecting markers. This work improves the search performance in comparison with pure random walks, we show the obtained results using computer simulations

BodyCloud: a SaaS approach for community body sensor networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behaviour surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals