{"title":"上下文模型和上下文感知","authors":"M. Hartmann, Gerhard Austaller","doi":"10.4018/978-1-59904-832-1.CH011","DOIUrl":null,"url":null,"abstract":"Humans use all kinds of information characterizing their current situation, like time, location and identity of persons nearby, to adapt their behavior to the situation and to make decisions. For example, when we speak to a person, we adapt what we say and how we say it to the social rank of the person (e.g., most people would not say “that’s nonsense” to their boss, but would to a friend). All this information is not easily captured, represented and processed by a computer. However, this information can help to build more user-friendly applications that adapt and respond to the user’s current situation. If the computer were aware of the user’s context and its interpretation, it would be able to make decisions on behalf of the user, anticipating user needs like another human would. For example, it would be possible to provide the user only with information relevant to the current situation and thus reduce the cognitive load. This is especially necessary in the area of ubiquitous computing (UC), where the user has to deal with a multitude of different computers, and thus with a multitude of possible distractions. To enable all these devices disappear into background, they have to anticipate the user’s future demands and adapt to the user’s context to reduce the amount of interaction needed. abstract","PeriodicalId":443285,"journal":{"name":"Handbook of Research on Ubiquitous Computing Technology for Real Time Enterprises","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Context Models and Context Awareness\",\"authors\":\"M. Hartmann, Gerhard Austaller\",\"doi\":\"10.4018/978-1-59904-832-1.CH011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Humans use all kinds of information characterizing their current situation, like time, location and identity of persons nearby, to adapt their behavior to the situation and to make decisions. For example, when we speak to a person, we adapt what we say and how we say it to the social rank of the person (e.g., most people would not say “that’s nonsense” to their boss, but would to a friend). All this information is not easily captured, represented and processed by a computer. However, this information can help to build more user-friendly applications that adapt and respond to the user’s current situation. If the computer were aware of the user’s context and its interpretation, it would be able to make decisions on behalf of the user, anticipating user needs like another human would. For example, it would be possible to provide the user only with information relevant to the current situation and thus reduce the cognitive load. This is especially necessary in the area of ubiquitous computing (UC), where the user has to deal with a multitude of different computers, and thus with a multitude of possible distractions. To enable all these devices disappear into background, they have to anticipate the user’s future demands and adapt to the user’s context to reduce the amount of interaction needed. abstract\",\"PeriodicalId\":443285,\"journal\":{\"name\":\"Handbook of Research on Ubiquitous Computing Technology for Real Time Enterprises\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Handbook of Research on Ubiquitous Computing Technology for Real Time Enterprises\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/978-1-59904-832-1.CH011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Handbook of Research on Ubiquitous Computing Technology for Real Time Enterprises","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-59904-832-1.CH011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Humans use all kinds of information characterizing their current situation, like time, location and identity of persons nearby, to adapt their behavior to the situation and to make decisions. For example, when we speak to a person, we adapt what we say and how we say it to the social rank of the person (e.g., most people would not say “that’s nonsense” to their boss, but would to a friend). All this information is not easily captured, represented and processed by a computer. However, this information can help to build more user-friendly applications that adapt and respond to the user’s current situation. If the computer were aware of the user’s context and its interpretation, it would be able to make decisions on behalf of the user, anticipating user needs like another human would. For example, it would be possible to provide the user only with information relevant to the current situation and thus reduce the cognitive load. This is especially necessary in the area of ubiquitous computing (UC), where the user has to deal with a multitude of different computers, and thus with a multitude of possible distractions. To enable all these devices disappear into background, they have to anticipate the user’s future demands and adapt to the user’s context to reduce the amount of interaction needed. abstract