{"title":"共时因果条件在视觉知识学习中的作用","authors":"Seng-Beng Ho","doi":"10.1109/CVPRW.2017.8","DOIUrl":null,"url":null,"abstract":"We propose a principled approach for the learning of causal conditions from actions and activities taking place in the physical environment through visual input. Causal conditions are the preconditions that must exist before a certain effect can ensue. We propose to consider diachronic and synchronic causal conditions separately for the learning of causal knowledge. Diachronic condition captures the \"change\" aspect of the causal relationship – what change must be present at a certain time to effect a subsequent change – while the synchronic condition is the \"contextual\" aspect – what \"static\" condition must be present to enable the causal relationship involved. This paper focuses on discussing the learning of synchronic causal conditions as well as proposing a principled framework for the learning of causal knowledge including the learning of extended sequences of cause-effect and the encoding of this knowledge in the form of scripts for prediction and problem solving.","PeriodicalId":6668,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","volume":"221 1","pages":"9-16"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"The Role of Synchronic Causal Conditions in Visual Knowledge Learning\",\"authors\":\"Seng-Beng Ho\",\"doi\":\"10.1109/CVPRW.2017.8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a principled approach for the learning of causal conditions from actions and activities taking place in the physical environment through visual input. Causal conditions are the preconditions that must exist before a certain effect can ensue. We propose to consider diachronic and synchronic causal conditions separately for the learning of causal knowledge. Diachronic condition captures the \\\"change\\\" aspect of the causal relationship – what change must be present at a certain time to effect a subsequent change – while the synchronic condition is the \\\"contextual\\\" aspect – what \\\"static\\\" condition must be present to enable the causal relationship involved. This paper focuses on discussing the learning of synchronic causal conditions as well as proposing a principled framework for the learning of causal knowledge including the learning of extended sequences of cause-effect and the encoding of this knowledge in the form of scripts for prediction and problem solving.\",\"PeriodicalId\":6668,\"journal\":{\"name\":\"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)\",\"volume\":\"221 1\",\"pages\":\"9-16\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVPRW.2017.8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPRW.2017.8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Role of Synchronic Causal Conditions in Visual Knowledge Learning
We propose a principled approach for the learning of causal conditions from actions and activities taking place in the physical environment through visual input. Causal conditions are the preconditions that must exist before a certain effect can ensue. We propose to consider diachronic and synchronic causal conditions separately for the learning of causal knowledge. Diachronic condition captures the "change" aspect of the causal relationship – what change must be present at a certain time to effect a subsequent change – while the synchronic condition is the "contextual" aspect – what "static" condition must be present to enable the causal relationship involved. This paper focuses on discussing the learning of synchronic causal conditions as well as proposing a principled framework for the learning of causal knowledge including the learning of extended sequences of cause-effect and the encoding of this knowledge in the form of scripts for prediction and problem solving.