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

Flexibility

書目名稱Biologically-Inspired Collaborative Computing影響因子(影響力)




書目名稱Biologically-Inspired Collaborative Computing影響因子(影響力)學(xué)科排名




書目名稱Biologically-Inspired Collaborative Computing網(wǎng)絡(luò)公開度




書目名稱Biologically-Inspired Collaborative Computing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Biologically-Inspired Collaborative Computing被引頻次




書目名稱Biologically-Inspired Collaborative Computing被引頻次學(xué)科排名




書目名稱Biologically-Inspired Collaborative Computing年度引用




書目名稱Biologically-Inspired Collaborative Computing年度引用學(xué)科排名




書目名稱Biologically-Inspired Collaborative Computing讀者反饋




書目名稱Biologically-Inspired Collaborative Computing讀者反饋學(xué)科排名

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大酒杯

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

Biguanides

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

Odyssey

hidebound

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

行乞

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

Arb853

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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