Object Distribution Networks for World-wide Document Circulation

This paper presents an Object Distribution System (ODS), a distributed system inspired by the ultra-large scale distribution models used in everyday life (e.g. food or newspapers distribution chains). Beyond traditional mechanisms of approaching information to readers (e.g. caching and mirroring), this system enables the publication, classification and subscription to volumes of objects (e.g. documents, events). Authors submit their contents to publication agents. Classification authorities provide classification schemes to classify objects. Readers subscribe to topics or authors, and retrieve contents from their local delivery agent (like a kiosk or library, with local copies of objects). Object distribution is an independent process where objects circulate asynchronously among distribution agents. ODS is designed to perform specially well in an increasingly populated, widespread and complex Internet jungle, using weak consistency replication by object distribution, asynchronous replication, and local access to objects by clients. ODS is based on two independent virtual networks, one dedicated to the distribution (replication) of objects and the other to calculate optimised distribution chains to be applied by the first network

Could Blockchain Decentralize Supply Chain? A Dynamic Analysis of Token Delivery Motivations of Mid-tier Suppliers in Blockchain-driven Supply Chain Finance

Blockchain, or distributed ledger technology (DLT), is expected to be a disruptive technology by enabling a highly decentralized and trust-free business environment. Yet the business pursuit for profit maximization calls for a more centralized structure and thereby conflicts with the decentralized ideology of blockchain. In the context of blockchain-driven supply chain finance (SCF), while blockchain technology enables the decentralization of information, the decentralization of cash flow still relies on mid-tier suppliers’ token delivery in a centralized transaction structure. In other words, mid-tier suppliers can become a “bottleneck” in blockchain-driven SCF. In this paper, we consider the supply chain network as a complex system where firms are self-organized and adaptive to their competitive environment. Via this theoretical lens, we investigate how the application of blockchain technology (information flow), mid-tier suppliers’ token delivery (cash flow) and supply chain transaction structures (goods flow) interplay over time. We propose that in short term, blockchain technology increases mid-tier suppliers’ transaction efficiency and thus motivates mid-tier suppliers’ token delivery and promotes the decentralization of supply chain transaction structure; in long term, the decentralized supply chain transaction structure will in turn negatively affect mid-tier suppliers’ token delivery motivations and drive the centralization of a supply chain. We will test our theoretical propositions by a series of simulation experiments in an agent-based model

COACHES Cooperative Autonomous Robots in Complex and Human Populated Environments

Public spaces in large cities are increasingly becoming complex and unwelcoming environments. Public spaces progressively become more hostile and unpleasant to use because of the overcrowding and complex information in signboards. It is in the interest of cities to make their public spaces easier to use, friendlier to visitors and safer to increasing elderly population and to citizens with disabilities. Meanwhile, we observe, in the last decade a tremendous progress in the development of robots in dynamic, complex and uncertain environments. The new challenge for the near future is to deploy a network of robots in public spaces to accomplish services that can help humans. Inspired by the aforementioned challenges, COACHES project addresses fundamental issues related to the design of a robust system of self-directed autonomous robots with high-level skills of environment modelling and scene understanding, distributed autonomous decision-making, short-term interacting with humans and robust and safe navigation in overcrowding spaces. To this end, COACHES will provide an integrated solution to new challenges on: (1) a knowledge-based representation of the environment, (2) human activities and needs estimation using Markov and Bayesian techniques, (3) distributed decision-making under uncertainty to collectively plan activities of assistance, guidance and delivery tasks using Decentralized Partially Observable Markov Decision Processes with efficient algorithms to improve their scalability and (4) a multi-modal and short-term human-robot interaction to exchange information and requests. COACHES project will provide a modular architecture to be integrated in real robots. We deploy COACHES at Caen city in a mall called “Rive de l’orne”. COACHES is a cooperative system consisting of ?xed cameras and the mobile robots. The ?xed cameras can do object detection, tracking and abnormal events detection (objects or behaviour). The robots combine these information with the ones perceived via their own sensor, to provide information through its multi-modal interface, guide people to their destinations, show tramway stations and transport goods for elderly people, etc.... The COACHES robots will use different modalities (speech and displayed information) to interact with the mall visitors, shopkeepers and mall managers. The project has enlisted an important an end-user (Caen la mer) providing the scenarios where the COACHES robots and systems will be deployed, and gather together universities with complementary competences from cognitive systems (SU), robust image/video processing (VUB, UNICAEN), and semantic scene analysis and understanding (VUB), Collective decision-making using decentralized partially observable Markov Decision Processes and multi-agent planning (UNICAEN, Sapienza), multi-modal and short-term human-robot interaction (Sapienza, UNICAEN

Multi-Agent Reinforcement Learning for Joint Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems

We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-infrastructure (V2I) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together. Due to the nature of fast channel variant in vehicular environment, traditional centralized optimization approach relying on global channel information might not be viable in C-V2X systems with large number of users. Utilizing a reinforcement learning (RL) approach, we propose a distributed resource allocation (RA) algorithm to overcome this challenge. Specifically, we model the RA problem as a multi-agent system. Based solely on the local channel information, each platoon leader, who acts as an agent, collectively interacts with each other and accordingly selects the optimal combination of sub-band and power level to transmit its signals. Toward this end, we utilize the double deep Q-learning algorithm to jointly train the agents under the objectives of simultaneously maximizing the V2I sum-rate and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm achieves a close performance compared to that of the well-known exhaustive search algorithm.Comment: 6 pages, 4 figure

Design of a middleware for QoS-aware distribution transparent content delivery

Developers of distributed multimedia applications face a diversity of multimedia formats, streaming platforms and streaming protocols. Furthermore, support for end-to-end quality-of-service (QoS) is a crucial factor for the development of future distributed multimedia systems. This paper discusses the architecture, design and implementation of a QoS-aware middleware platform for content delivery. The platform supports the development of distributed multimedia applications and can deliver content with QoS guarantees. QoS support is offered by means of an agent infrastructure for QoS negotiation and enforcement. Properties of content are represented using a generic content representation model described using the OMG Meta Object Facility (MOF) model. A content delivery framework manages stream paths for content delivery despite differences in streaming protocols and content encoding. The integration of the QoS support, content representation and content delivery framework results in a QoS-aware middleware that enables representation transparent and location transparent delivery of content

An agent-based architecture for managing the provision of community care - the INCA (Intelligent Community Alarm) experience

Community Care is an area that requires extensive cooperation between independent agencies, each of which needs to meet its own objectives and targets. None are engaged solely in the delivery of community care, and need to integrate the service with their other responsibilities in a coherent and efficient manner. Agent technology provides the means by which effective cooperation can take place without compromising the essential security of both the client and the agencies involved as the appropriate set of responses can be generated through negotiation between the parties without the need for access to the main information repositories that would be necessary with conventional collaboration models. The autonomous nature of agents also means that a variety of agents can cooperate together with various local capabilities, so long as they conform to the relevant messaging requirements. This allows a variety of agents, with capabilities tailored to the carers to which they are attached to be developed so that cost-effective solutions can be provided. </p

Multi Site Coordination using a Multi-Agent System

A new approach of coordination of decisions in a multi site system is proposed. It is based this approach on a multi-agent concept and on the principle of distributed network of enterprises. For this purpose, each enterprise is defined as autonomous and performs simultaneously at the local and global levels. The basic component of our approach is a so-called Virtual Enterprise Node (VEN), where the enterprise network is represented as a set of tiers (like in a product breakdown structure). Within the network, each partner constitutes a VEN, which is in contact with several customers and suppliers. Exchanges between the VENs ensure the autonomy of decision, and guarantiee the consistency of information and material flows. Only two complementary VEN agents are necessary: one for external interactions, the Negotiator Agent (NA) and one for the planning of internal decisions, the Planner Agent (PA). If supply problems occur in the network, two other agents are defined: the Tier Negotiator Agent (TNA) working at the tier level only and the Supply Chain Mediator Agent (SCMA) working at the level of the enterprise network. These two agents are only active when the perturbation occurs. Otherwise, the VENs process the flow of information alone. With this new approach, managing enterprise network becomes much more transparent and looks like managing a simple enterprise in the network. The use of a Multi-Agent System (MAS) allows physical distribution of the decisional system, and procures a heterarchical organization structure with a decentralized control that guaranties the autonomy of each entity and the flexibility of the network