By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning-Intel ligent Systems are presented in a new report. According to news reporting origin ating from Jilin, People's Republic of China, by NewsRx correspondents, research stated, "Norms are a coordination mechanism. They control agents' behavior in a multiagent system (MAS) and need to evolve to cope with changing environments." Our news editors obtained a quote from the research from Jilin University, "Muta tion oriented norm evolution is a strategies for allowing norms to evolve. Howev er, this strategy simply adds some possible trigger condition constraints on the norms, which means that some agents are unable to perform actions. To address t his problem, this paper presents a new strategy for norm evolution based on an i mproved crossover operator. First, this paper presents a power-set approach to i mprove the integrity of norm evolution. This approach can help ensure that all p ossible combinations of norms are considered during the analysis, providing a de eper understanding of how norms interact and evolve within a norm set. Then, to improve the efficiency of norm evolution, a trade-off between efficiency and com pleteness is proposed. This approach reduces the search space and improves effic iency, as not every power set combination needs to be searched; it also ensures completeness. Finally, the crossover operator in this strategy is improved based on the trade-off approach. Specifically, the triggers and expectations of one m utated norm enrich the triggers and expectations of other norms. All of these fa ctors enrich the normative conditions through the trade-off approach. A MAS can take immediate action to adapt to new requirements or problems encountered, and quickly make normative changes and learn to respond appropriately to a new situa tion. The MAS is able to more clearly understand and learn about causality in th e environment during norm evolution, and understand the connection between behav ior and outcomes. The proposed strategy is applied to a case study of an unmanne d vehicle system. The experimental results show that the trade-off approach has greater completeness and effectiveness in norm evolution. This strategy achieves a more complete and effective autonomous norm evolution."
Key words
Jilin/People's Republic of China/Asia/Intelligent Systems/Machine Learning/Genetics/Jilin University
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