可解释BDI试剂的设计与评价

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2010-08-31 DOI:10.1109/WI-IAT.2010.115

M. Harbers, K. Bosch, J. Meyer

{"title":"可解释BDI试剂的设计与评价","authors":"M. Harbers, K. Bosch, J. Meyer","doi":"10.1109/WI-IAT.2010.115","DOIUrl":null,"url":null,"abstract":"It is widely acknowledged that providing explanations is an important capability of intelligent systems. Explanation capabilities are useful, for example, in scenario-based training systems with intelligent virtual agents. Trainees learn more from scenario-based training when they understand why the virtual agents act the way they do. In this paper, we present a model for explainable BDI agents which enables the explanation of BDI agent behavior in terms of underlying beliefs and goals. Different explanation algorithms can be specified in the model, generating different types of explanations. In a user study (n=20), we compare four explanation algorithms by asking trainees which explanations they consider most useful. Based on the results, we discuss which explanation types should be given under what conditions.","PeriodicalId":340211,"journal":{"name":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"57","resultStr":"{\"title\":\"Design and Evaluation of Explainable BDI Agents\",\"authors\":\"M. Harbers, K. Bosch, J. Meyer\",\"doi\":\"10.1109/WI-IAT.2010.115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is widely acknowledged that providing explanations is an important capability of intelligent systems. Explanation capabilities are useful, for example, in scenario-based training systems with intelligent virtual agents. Trainees learn more from scenario-based training when they understand why the virtual agents act the way they do. In this paper, we present a model for explainable BDI agents which enables the explanation of BDI agent behavior in terms of underlying beliefs and goals. Different explanation algorithms can be specified in the model, generating different types of explanations. In a user study (n=20), we compare four explanation algorithms by asking trainees which explanations they consider most useful. Based on the results, we discuss which explanation types should be given under what conditions.\",\"PeriodicalId\":340211,\"journal\":{\"name\":\"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"57\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI-IAT.2010.115\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI-IAT.2010.115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 57

摘要

人们普遍认为提供解释是智能系统的一项重要能力。解释能力很有用，例如，在具有智能虚拟代理的基于场景的训练系统中。当受训者理解虚拟代理的行为方式时，他们会从基于场景的培训中学到更多东西。在本文中，我们提出了一个可解释的BDI代理模型，该模型可以根据潜在的信念和目标来解释BDI代理的行为。可以在模型中指定不同的解释算法，生成不同类型的解释。在一项用户研究(n=20)中，我们通过询问受训者他们认为最有用的解释来比较四种解释算法。在此基础上，我们讨论了在什么条件下应该给出哪些解释类型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Design and Evaluation of Explainable BDI Agents

It is widely acknowledged that providing explanations is an important capability of intelligent systems. Explanation capabilities are useful, for example, in scenario-based training systems with intelligent virtual agents. Trainees learn more from scenario-based training when they understand why the virtual agents act the way they do. In this paper, we present a model for explainable BDI agents which enables the explanation of BDI agent behavior in terms of underlying beliefs and goals. Different explanation algorithms can be specified in the model, generating different types of explanations. In a user study (n=20), we compare four explanation algorithms by asking trainees which explanations they consider most useful. Based on the results, we discuss which explanation types should be given under what conditions.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

自引率

0.00%

发文量