{"title":"代理设计器工具包","authors":"Aengus Martin, O. Bown","doi":"10.1145/2466627.2481211","DOIUrl":null,"url":null,"abstract":"The Agent Designer Toolkit is the result of a study of methods for designing the behaviour of musical agents (i.e. autonomous systems) intended to perform high-level musical decision-making. It uses machine learning methods informed by a musician's knowledge and insights, to discover the salient musical patterns demonstrated in a set of example performances. Based on these patterns, it can produce agents with a variety of behaviours, corresponding to differing degrees of similarity to the demonstrated performance style. The agents can perform in real-time and respond to other musicians or external factors.","PeriodicalId":333903,"journal":{"name":"Proceedings of the 9th ACM Conference on Creativity & Cognition","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The agent designer toolkit\",\"authors\":\"Aengus Martin, O. Bown\",\"doi\":\"10.1145/2466627.2481211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Agent Designer Toolkit is the result of a study of methods for designing the behaviour of musical agents (i.e. autonomous systems) intended to perform high-level musical decision-making. It uses machine learning methods informed by a musician's knowledge and insights, to discover the salient musical patterns demonstrated in a set of example performances. Based on these patterns, it can produce agents with a variety of behaviours, corresponding to differing degrees of similarity to the demonstrated performance style. The agents can perform in real-time and respond to other musicians or external factors.\",\"PeriodicalId\":333903,\"journal\":{\"name\":\"Proceedings of the 9th ACM Conference on Creativity & Cognition\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 9th ACM Conference on Creativity & Cognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2466627.2481211\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 9th ACM Conference on Creativity & Cognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2466627.2481211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Agent Designer Toolkit is the result of a study of methods for designing the behaviour of musical agents (i.e. autonomous systems) intended to perform high-level musical decision-making. It uses machine learning methods informed by a musician's knowledge and insights, to discover the salient musical patterns demonstrated in a set of example performances. Based on these patterns, it can produce agents with a variety of behaviours, corresponding to differing degrees of similarity to the demonstrated performance style. The agents can perform in real-time and respond to other musicians or external factors.
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