{"title":"Experiments with Single-class Support Vector Data Descriptions as a Tool for Vocabulary Grounding","authors":"Aneesh Chauhan, L. Lopes","doi":"10.5220/0003028000700078","DOIUrl":null,"url":null,"abstract":"This paper explores support vectors as a tool for vocabulary acquisition in robots. The intention is to investigate the language grounding process at the single-word stage. A social language grounding scenario is designed, where a robotic agent is taught the names of the objects by a human instructor. The agent grounds the names of these objects by associating them with their respective sensor-based category descriptions. A system for grounding vocabulary should be incremental, adaptive and support gradual evolution. A novel learning model based on single-class support vector data descriptions (SVDD), which conforms to these requirements, is presented. For robustness and flexibility, a kernel based implementation of support vectors was realized. For this purpose, a sigmoid kernel using histogram pyramid matching has been developed. The support vectors are trained based on an original approach using genetic algorithms. The model is tested over a series of semi-automated experiments and the results are reported.","PeriodicalId":378427,"journal":{"name":"International Workshop on Natural Language Processing and Cognitive Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on Natural Language Processing and Cognitive Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0003028000700078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper explores support vectors as a tool for vocabulary acquisition in robots. The intention is to investigate the language grounding process at the single-word stage. A social language grounding scenario is designed, where a robotic agent is taught the names of the objects by a human instructor. The agent grounds the names of these objects by associating them with their respective sensor-based category descriptions. A system for grounding vocabulary should be incremental, adaptive and support gradual evolution. A novel learning model based on single-class support vector data descriptions (SVDD), which conforms to these requirements, is presented. For robustness and flexibility, a kernel based implementation of support vectors was realized. For this purpose, a sigmoid kernel using histogram pyramid matching has been developed. The support vectors are trained based on an original approach using genetic algorithms. The model is tested over a series of semi-automated experiments and the results are reported.