This chapter presents the currently not established and identifies design requirements for new systems to address this challenge and provide directions for possible improvement. As a result, this chapter introduces the concept of SamenMarkt®, a participatory system in which multi-agent system technology enables distributed price negotiation, distribution and communication between producers, retailers and consumers.
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Presented at the International Conference on Flexible Automation and Intelligent Manufacturing (FAIM) 23 - 26 June 2015 at the University of Wolverhampton, UK. Authorsupplied abstract: ABSTRACT Customized, on-demand manufacturing is growing through the use of new paradigms and technologies. Agile Manufacturing, cyber physical systems, and reconfigurable systems are examples of these changes. To provide high-mix, low-volume production there is a need for dynamic behaviour and manufacturing machines that can handle a large variety of services. Manufacturing systems can be made more dynamic by using agent-based technology. However, the reconfigurable aspect of these machines has yet to be explored. This paper investigates the possibility to adapt, i.e., reconfigure the hardware of manufacturing machines based on the current manufacturing demand. Using a simulation for a working agent-based platform with reconfiguration capabilities, this paper validates the effects of reconfigurable hardware to change capacity when producing a variety of products in a dynamic production environment. The paper continues to investigate required strategies to effectively use reconfiguration and counter the effects of disturbances that are likely to happen in such systems.
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Key to reinforcement learning in multi-agent systems is the ability to exploit the fact that agents only directly influence only a small subset of the other agents. Such loose couplings are often modelled using a graphical model: a coordination graph. Finding an (approximately) optimal joint action for a given coordination graph is therefore a central subroutine in cooperative multi-agent reinforcement learning (MARL). Much research in MARL focuses on how to gradually update the parameters of the coordination graph, whilst leaving the solving of the coordination graph up to a known typically exact and generic subroutine. However, exact methods { e.g., Variable Elimination { do not scale well, and generic methods do not exploit the MARL setting of gradually updating a coordination graph and recomputing the joint action to select. In this paper, we examine what happens if we use a heuristic method, i.e., local search, to select joint actions in MARL, and whether we can use outcome of this local search from a previous time-step to speed up and improve local search. We show empirically that by using local search, we can scale up to many agents and complex coordination graphs, and that by reusing joint actions from the previous time-step to initialise local search, we can both improve the quality of the joint actions found and the speed with which these joint actions are found.
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Author supplied: The production system described in this paper in an implementation of an agile agent-based production system. This system is designed to meet the requirements of modern production, where short time to market, requirementdriven production and low cost small quantity production are important issues. The production is done on special devices called equiplets. A grid of these equiplets connected by a fast network is capable of producing a variety of different products in parallel. The multi-agent-based software infrastructure is responsible for the agile manufacturing. A product agent is responsible for the production of a single product and equiplet agents will perform the production steps to assemble the product. This paper describes this multiagent-based production system with the focus on the product agent. Presented at EUMAS 2013 ( 11th European Workshop on Multi-Agent Systems) , At Toulouse.
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Author supplied: The production system described in this paper in an im- plementation of an agile agent-based production system. This system is designed to meet the requirements of modern production, where short time to market, requirement-driven production and low cost small quan- tity production are important issues. The production is done on special devices called equiplets. A grid of these equiplets connected by a fast network is capable of producing a variety of diverent products in parallel. The multi-agent-based software infrastructure is responsible for the agile manufacturing. A product agent is responsible for the production of a single product and equiplet agents will perform the production steps to assemble the product. This paper describes this multiagent-based production system with the focus on the product agent.
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From the article: "To extend the lifetime of products, an agent is connected to the product. This agent has several roles. It depends on the phase of the lifecycle what these roles will be. One of the roles in the usage or recycling phase is to negotiate for buying spare parts in case a part of the product is broken. The same agent can also decide to offer spare parts to other agents to reuse working parts of a broken product. To accomplish this idea, a marketplace for agents has to be set up, where the auctions can take place. To support this concept, blockchain technology has been used. Blockchains are a new type of technology, known from bitcoins, but there are other cases where blockchains can be used. Blockchain is known for its decentralisation, transparency and for making trustful transactions. In this paper the working of different types of blockchains will be briefly explained and determined if they can be useful for online auctions by agents. A prototype of the marketplace using blockchains has been built."
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Accurate modeling of end-users’ decision-making behavior is crucial for validating demand response (DR) policies. However, existing models usually represent the decision-making behavior as an optimization problem, neglecting the impact of human psychology on decisions. In this paper, we propose a Belief-Desire-Intention (BDI) agent model to model end-users’ decision-making under DR. This model has the ability to perceive environmental information, generate different power scheduling plans, and make decisions that align with its own interests. The key modeling capabilities of the proposed model have been validated in a household end-user with flexible loads
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Author supplied: A manufacturing process can be described by a sequence or combination of production steps. Based on this approach a manufacturing system has been developed that is capable to produce several different products in parallel. A batch size of one unit is possible and the production is pull-driven. The manufacturing system is based on agent technology and a special so-called product agent collects information about the assembly process. This agent will be connected to the actual product and can guide the disassembly process at the end of the products life. The agent will show the inverse steps to be taken to take a product apart. This approach can be used in the agent based manufacturing process described in this paper but the concept can also be used for other manufacturing systems. The paper discusses the possibilities as well as the restrictions of the method proposed here.
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Dit proefschrift heeft als onderwerp de toepassing van agenttechnologie in productie en productondersteuning. Onder een agent verstaan we in deze context een autonoom opererende software entiteit die gemaakt is om een zeker doel te realiseren en daartoe met de omgeving comuniceert en zelfstandig acties kan uitvoeren. In moderne productiesystemen streeft men ernaar om de tijd van ontwerp tot productie zo kort mogelijk te houden en de productie af te stemmen op de wensen van de individuele eindgebruiker. Vooral dit laatste streven past niet in het concept van massaproductie. Een methode moet gezocht worden om kleine hoeveelheden of zelfs unieke producten tegen een lage kostprijs te fabriceren. Om dit te verwezenlijken zijn voor dit onderzoek speciale goedkope productieplatforms ontwikkeld. Deze hercongureerbare productiemachines noemen we equiplets. Een verzameling van deze equiplets in een gridopstelling geplaatst en gekoppeld met een snelle netwerkverbinding is in staat om een aantal verschillende producten tegelijk te produceren. Dit noemen we exibele parallelle productie. Voor de softwareinfrastructuur is agenttechnologie toegepast. Twee typen agenten spelen hierin een hoofdrol. Een productagent is verantwoordelijk voor de totstandkoming van een enkel product. De productiemachines worden voorgesteld door zogenoemde equipletagenten. De productagent weet wat er moet gebeuren voor het maken van een product terwijl de equipletagent weet hoe een of meer productiestappen moeten worden uitgevoerd. Het hier voorgesteld concept verschilt in veel opzichten van standaard massaproductie. Elk product in wording volgt zijn eigen, mogelijk unieke pad langs de equiplets, de productie wordt per product gescheduled en niet per batch en er is geen sprake van een productielijn. Dit proefschrift stelt de softwarearchitectuur voor en beschrijft oplossingen voor de routeplanning waarbij het aantal wisselingen tussen equiplets geminimaliseerd is, een scheduling die gebaseerd is op schedulingschema's zoals toegepast in real-time operating systems en een op autonome voertuigen gebaseerd transportsysteem. Bij al deze oplossingen speelt de productagent een belangrijke rol. (uit de samenvatting van het proefschrift) SIKS Dissertation Series No. 2014-31 The research reported in this thesis has been carried out under the auspices of SIKS, the Dutch Research School for Information and Knowledge Systems.
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From the article: "The term Internet of Things (IoT) is used for situations where one or more devices are connected to a network or possibly the Internet. Most studies focus on the possibilities that arise when a device is capable to share its data with other devices or humans. In this study, the focus is on the device itself and what kind of possibilities an Internet connection gives to the device and its owner or user. Also the data the device needs to participate in a smart way in the IoT are part of this study. Agent technology is the enabling technology for the ideas introduced here. A proof of concept is given, where some concepts proposed in the paper are put into practice."
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