Titlebook: Agent and Multi-Agent Systems: Technologies and Applications; 4th KES Internationa Piotr J?drzejowicz,Ngoc Thanh Nguyen,Lakhmi C. Jai Confe

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弄臟

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MAS Approach to Business Models Simulations: Supply Chain Management Case Study for business simulations. The sections of the paper gradually prove these theses. Firstly the agent orientation has been presented as a?source of natural and very intuitive modeling constructs and complexity management tools. Then the classical model of supply chain simulation was altered to its ag

STALL

Analysing Bidder Performance in Randomised and Fixed-Deadline Automated Auctionsaper we examine two such rules: auctions with randomised closing times and fixed deadlines. To this end, stochastic models of auctions with discrete state-space representing the prices attained are developed and analysed. The models allow us to determine the stationary probabilistic outcomes of the

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cljRobust - Clojure Programming API for Lego Mindstorms NXTn, concurrent dialect of the . language. It shortens the distance between deliberative, symbolic processing and hardware. In this way, . facilitates the writing of control algorithms which operate close to the hardware layer, and thus are able to quickly respond to changes in the environment. Since

協(xié)迫

Agent-Enabled Collaborative Downloading: Towards Energy-Efficient Provisioning of Group-Oriented Serading. The main idea is that mobile users, represented with their agents and corresponding profiles, interested in the same content download some parts directly from a service server and others afterwards locally exchange among themselves to reduce overall energy consumption. Collaborative downloadi

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Lifespan-Aware Routing for Wireless Sensor Networksties remotely and independently of an energy source. Services based on this technology may change the way we monitor and control remote areas and objects..In this paper we propose a different approach to routing data across Wireless Sensor Networks. We argue that a distance-vector protocol using a l

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捏造

Neural Networks for Solving the Superposition Problem Using Approximation Method and Adaptive Learnioth batch data containing input-output of the hidden neurons and the final neural output of the network. The training set is associated to the adjustable parameters of the network by weight equations. Then we have obtained the exact input weight of the nonlinear equations and the approximated output