{"title":"智能辅导系统中的经典规划(海报环节)","authors":"A. Wheeldon, J. Reye","doi":"10.1145/359369.359413","DOIUrl":null,"url":null,"abstract":"we store hash values that represent chunkg. Not only do we expect false positives because l~h functions can produce the same value for different chunks but also because the number of possible cbnnk.~ in Interuet-docomants onmumber the available mmaber of hash values. There are two ways to reduce index-space. We can either reduce the number ofchunk~ to be kept, which increases the chance of false negatives, or we can reduce the size of the hash value we calculate on each chunk, which increases the chance of false positives. False negatives are harder to handle because we have already missed potential documents. We propose a method that is able to ellm/nate false positives from a given set of documents. The comparison is completed in two phases. In the :first phase we define candiclate documents using the aforementioned methods and the second stage ellrn/nates false positives. Our algorithm for eliminating false positives uses a suffix tree built on the suspicious document to compare candidate documents and elim/nate accidental matches. Comparison of the chnnking methods and our algorithm are presented in this poster. Maintenance can be defined as the single most expensive activity in large software engineering projects, requiring 65% to 75% of total effort. Hence software engineering can be termed software evolution. The subject Software Engineering Practice (CSE2201) taught in the School of Computer Science and Software Engineering at Monash University is a second year core subject in an undergraduate degree program and comprises about 250 students per year. CSE2201 introduces software engineering concepts to students and expects students to view software engineering as an evolutionary process. Students are additionally introduced to and expected to implement the practical aspects of the Personal Software Process (PSP). We demonstrate the practice of teaching software evolution to undergraduate computing students in the Bachelor of Computing degree, and show how the four maintenance activities of corrective, adaptive, perfective and preventative can be included into the practical component of a software engineering course, providing students with a much more realistic view of software engineering. Expla-ations are needed to give feedback to students, as part of an Intelligent Tutoring System (ITS). A student submits an SQL query to the ITS as a solution to a question. When the query is incorrect the student receives an explanation, from the ITS, of how they can improve it so that it satisfies the task at hand. An expert enters an SQL …","PeriodicalId":435916,"journal":{"name":"African Conference on Software Engineering","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classical planning in an intelligent tutoring system (poster session)\",\"authors\":\"A. Wheeldon, J. Reye\",\"doi\":\"10.1145/359369.359413\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"we store hash values that represent chunkg. Not only do we expect false positives because l~h functions can produce the same value for different chunks but also because the number of possible cbnnk.~ in Interuet-docomants onmumber the available mmaber of hash values. There are two ways to reduce index-space. We can either reduce the number ofchunk~ to be kept, which increases the chance of false negatives, or we can reduce the size of the hash value we calculate on each chunk, which increases the chance of false positives. False negatives are harder to handle because we have already missed potential documents. We propose a method that is able to ellm/nate false positives from a given set of documents. The comparison is completed in two phases. In the :first phase we define candiclate documents using the aforementioned methods and the second stage ellrn/nates false positives. Our algorithm for eliminating false positives uses a suffix tree built on the suspicious document to compare candidate documents and elim/nate accidental matches. Comparison of the chnnking methods and our algorithm are presented in this poster. Maintenance can be defined as the single most expensive activity in large software engineering projects, requiring 65% to 75% of total effort. Hence software engineering can be termed software evolution. The subject Software Engineering Practice (CSE2201) taught in the School of Computer Science and Software Engineering at Monash University is a second year core subject in an undergraduate degree program and comprises about 250 students per year. CSE2201 introduces software engineering concepts to students and expects students to view software engineering as an evolutionary process. Students are additionally introduced to and expected to implement the practical aspects of the Personal Software Process (PSP). We demonstrate the practice of teaching software evolution to undergraduate computing students in the Bachelor of Computing degree, and show how the four maintenance activities of corrective, adaptive, perfective and preventative can be included into the practical component of a software engineering course, providing students with a much more realistic view of software engineering. Expla-ations are needed to give feedback to students, as part of an Intelligent Tutoring System (ITS). A student submits an SQL query to the ITS as a solution to a question. When the query is incorrect the student receives an explanation, from the ITS, of how they can improve it so that it satisfies the task at hand. An expert enters an SQL …\",\"PeriodicalId\":435916,\"journal\":{\"name\":\"African Conference on Software Engineering\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"African Conference on Software Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/359369.359413\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"African Conference on Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/359369.359413","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classical planning in an intelligent tutoring system (poster session)
we store hash values that represent chunkg. Not only do we expect false positives because l~h functions can produce the same value for different chunks but also because the number of possible cbnnk.~ in Interuet-docomants onmumber the available mmaber of hash values. There are two ways to reduce index-space. We can either reduce the number ofchunk~ to be kept, which increases the chance of false negatives, or we can reduce the size of the hash value we calculate on each chunk, which increases the chance of false positives. False negatives are harder to handle because we have already missed potential documents. We propose a method that is able to ellm/nate false positives from a given set of documents. The comparison is completed in two phases. In the :first phase we define candiclate documents using the aforementioned methods and the second stage ellrn/nates false positives. Our algorithm for eliminating false positives uses a suffix tree built on the suspicious document to compare candidate documents and elim/nate accidental matches. Comparison of the chnnking methods and our algorithm are presented in this poster. Maintenance can be defined as the single most expensive activity in large software engineering projects, requiring 65% to 75% of total effort. Hence software engineering can be termed software evolution. The subject Software Engineering Practice (CSE2201) taught in the School of Computer Science and Software Engineering at Monash University is a second year core subject in an undergraduate degree program and comprises about 250 students per year. CSE2201 introduces software engineering concepts to students and expects students to view software engineering as an evolutionary process. Students are additionally introduced to and expected to implement the practical aspects of the Personal Software Process (PSP). We demonstrate the practice of teaching software evolution to undergraduate computing students in the Bachelor of Computing degree, and show how the four maintenance activities of corrective, adaptive, perfective and preventative can be included into the practical component of a software engineering course, providing students with a much more realistic view of software engineering. Expla-ations are needed to give feedback to students, as part of an Intelligent Tutoring System (ITS). A student submits an SQL query to the ITS as a solution to a question. When the query is incorrect the student receives an explanation, from the ITS, of how they can improve it so that it satisfies the task at hand. An expert enters an SQL …