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Titlebook: Biologically-Inspired Collaborative Computing; IFIP 20th World Comp Mike Hinchey,Anastasia Pagnoni,Hartmut Schmeck Conference proceedings 2

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发表于 2025-3-21 19:39:23 | 显示全部楼层 |阅读模式
期刊全称Biologically-Inspired Collaborative Computing
期刊简称IFIP 20th World Comp
影响因子2023Mike Hinchey,Anastasia Pagnoni,Hartmut Schmeck
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发行地址The papers in this volume were peer-reviewed and carefully selected.Much information in this series is published in advance of journal publication.The contributors in this volume are world-renowned ex
学科分类IFIP Advances in Information and Communication Technology
图书封面Titlebook: Biologically-Inspired Collaborative Computing; IFIP 20th World Comp Mike Hinchey,Anastasia Pagnoni,Hartmut Schmeck Conference proceedings 2
影响因子“Look deep into nature and you will understand everything better.” advised Albert Einstein. In recent years, the research communities in Computer Science, Engineering, and other disciplines have taken this message to heart, and a relatively new field of “biologically-inspired computing” has been born. Inspiration is being drawn from nature, from the behaviors of colonies of ants, of swarms of bees and even the human body. This new paradigm in computing takes many simple autonomous objects or agents and lets them jointly perform a complex task, without having the need for centralized control. In this paradigm, these simple objects interact locally with their environment using simple rules. Applications include optimization algorithms, communications networks, scheduling and decision making, supply-chain management, and robotics, to name just a few. There are many disciplines involved in making such systems work: from artificial intelligence to energy aware systems. Often these disciplines have their own field of focus, have their own conferences, or only deal with specialized s- problems (e.g. swarm intelligence, biologically inspired computation, sensor networks). The Second IFIP C
Pindex Conference proceedings 2008
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书目名称Biologically-Inspired Collaborative Computing影响因子(影响力)




书目名称Biologically-Inspired Collaborative Computing影响因子(影响力)学科排名




书目名称Biologically-Inspired Collaborative Computing网络公开度




书目名称Biologically-Inspired Collaborative Computing网络公开度学科排名




书目名称Biologically-Inspired Collaborative Computing被引频次




书目名称Biologically-Inspired Collaborative Computing被引频次学科排名




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书目名称Biologically-Inspired Collaborative Computing年度引用学科排名




书目名称Biologically-Inspired Collaborative Computing读者反馈




书目名称Biologically-Inspired Collaborative Computing读者反馈学科排名




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Heuristics for Uninformed Search Algorithms in Unstructured P2P Networks Inspired by Self-Organizingp network protocols such as Gnutella use a flooding-based mechanism for resource searching that generates considerable traffic in the network for each search query. When the searching activity by users in a p2p network is high, the traffic generated from the search requests could ensue congestion an
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Congestion Control in Ant Like Moving Agent Systemsgents have to visit a service station to refill their energy storage. After visiting the service station the ants can move randomly and fast. The less energy an agent has the slower it becomes and the more it moves in direction of the service station. Different methods for self-organized congestion
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Self-stabilizing Automatate. This property is very important to computer-based systems, too. However, building self-stabilizing systems is still very difficult. Proving that any given implementation is in fact self-stabilizing is even harder. Nature has a big advantage: Any living being must eventually die and limited energ
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Experiments with Biologically-Inspired Methods for Service Assignment in Wireless Sensor Networksce the amount of information that has to be routed to the base station and thereby to reduce communication and energy consumption. However, to minimize the amount of communication between services and their requesters, the locations of services in the network have to be selected carefully. Therefore
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Evolving Collision Avoidance on Autonomous Robotsieving complex goals. We use an onboard online evolutionary model, based on finite Moore automata, to develop collective behavior in an artificial swarm of micro-robots. Experiments have been made in simulation to achieve Collision Avoidance. The model is shown to be capable to generate the desired
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