A n authoring tool is software that simplifies the creation of data or documents. Such software varies from text editors (emacs, vi) to software development environments (Microsoft's Visual Studio). However, a search for authoring tools on the World Wide Web suggests that the majority of authoring tools are HTML editors, course-ware creation tools, and multimedia presentation editors. Multimedia tools are tools that are used to create applications that use a variety of media (e.g., music, video, graphics, text, speech) to communicate their message. Courseware is software used in computer-based training to improve or replace stu-dent–teacher interaction. Document production tools include HTML editors and XML tools, which are often used to create document formats that are portable to different platforms or to enhance a document's semantic or structural information. Authoring tools are important for artificial intelligence because much of the " intelligence " in an application lies in its use of content or data (i. For example, my current research in building intelligent dialog systems for a tutoring system (see intelligence 10(1) 14–23, or at http://
{"title":"Links: authoring tools for AI","authors":"Syed S. Ali","doi":"10.1145/318964.318966","DOIUrl":"https://doi.org/10.1145/318964.318966","url":null,"abstract":"A n authoring tool is software that simplifies the creation of data or documents. Such software varies from text editors (emacs, vi) to software development environments (Microsoft's Visual Studio). However, a search for authoring tools on the World Wide Web suggests that the majority of authoring tools are HTML editors, course-ware creation tools, and multimedia presentation editors. Multimedia tools are tools that are used to create applications that use a variety of media (e.g., music, video, graphics, text, speech) to communicate their message. Courseware is software used in computer-based training to improve or replace stu-dent–teacher interaction. Document production tools include HTML editors and XML tools, which are often used to create document formats that are portable to different platforms or to enhance a document's semantic or structural information. Authoring tools are important for artificial intelligence because much of the \" intelligence \" in an application lies in its use of content or data (i. For example, my current research in building intelligent dialog systems for a tutoring system (see intelligence 10(1) 14–23, or at http://","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"81 1","pages":"11-12"},"PeriodicalIF":0.0,"publicationDate":"1999-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81633846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Backtracking: still garbage collecting","authors":"Chris Welty, L. Hoebel","doi":"10.1145/318964.318975","DOIUrl":"https://doi.org/10.1145/318964.318975","url":null,"abstract":"","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"8 1","pages":"48"},"PeriodicalIF":0.0,"publicationDate":"1999-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81100319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
uch of the discussion on teaching artificial intelligence (AI) tends to be centered on the introductory course. Typically, introductory AI courses are offered in undergraduate programs at the junior or senior level. The underlying assumption is that such a course serves as a capstone to the learning experiences of a computer science student. In this installment, I would like to examine undergraduate courses that go beyond the standard introductory AI course. It is important to recognize the diversity of computer science programs. Some undergraduate programs are part of an established graduate program; however, many programs are stand-alone undergraduate programs. Even programs that offer graduate degrees restrict the number of AI courses to more or less an introductory AI course, which is often cross-listed as a graduate-level course. This is true even in programs that are strong in AI research. Most AI courses that cover topics beyond the introductory course are designed for graduate students, but motivated undergraduate students can enroll in these courses. Occasionally, undergraduate students also undertake advanced work in AI research labs. Working together on a research project alongside graduate students is one of the most rewarding experiences for undergraduates. For the majority of programs that offer only undergraduate-level instruction in computer science, the possibility of offering even an introductory course in AI can be an issue. There may be limited resources, high demands of faculty on other areas of the curriculum, or the limited availability of faculty who are willing to teach AI. The school may not have faculty whose research area is AI. Here, the definition of a core computer science curriculum plays an important role. If AI is prescribed by a standard curriculum (for instance, the ACM/IEEE Curriculum 1991 lists several AI and AI-related knowledge units), the likelihood of finding an AI course is greater. Another parameter that can play an important part in determining the range of AI courses offered is the size of the program. Larger programs tend to have larger class enrollments. Sometimes, larger class sizes can limit the range of advanced courses offered. For instance, the use of LEGO-based robot labs (see " Curriculum Descant, " SIGART Bulletin, Fall 1998) has been found to be more feasible in schools with smaller class sizes. Smaller class sizes also enable the creation of interdiscipli-nary AI courses that require active class participation. For example, I offer a course entitled Biologically Inspired Computational Models of …
{"title":"Curriculum descant: beyond introductory AI","authors":"Deepak Kumar","doi":"10.1145/318964.318967","DOIUrl":"https://doi.org/10.1145/318964.318967","url":null,"abstract":"uch of the discussion on teaching artificial intelligence (AI) tends to be centered on the introductory course. Typically, introductory AI courses are offered in undergraduate programs at the junior or senior level. The underlying assumption is that such a course serves as a capstone to the learning experiences of a computer science student. In this installment, I would like to examine undergraduate courses that go beyond the standard introductory AI course. It is important to recognize the diversity of computer science programs. Some undergraduate programs are part of an established graduate program; however, many programs are stand-alone undergraduate programs. Even programs that offer graduate degrees restrict the number of AI courses to more or less an introductory AI course, which is often cross-listed as a graduate-level course. This is true even in programs that are strong in AI research. Most AI courses that cover topics beyond the introductory course are designed for graduate students, but motivated undergraduate students can enroll in these courses. Occasionally, undergraduate students also undertake advanced work in AI research labs. Working together on a research project alongside graduate students is one of the most rewarding experiences for undergraduates. For the majority of programs that offer only undergraduate-level instruction in computer science, the possibility of offering even an introductory course in AI can be an issue. There may be limited resources, high demands of faculty on other areas of the curriculum, or the limited availability of faculty who are willing to teach AI. The school may not have faculty whose research area is AI. Here, the definition of a core computer science curriculum plays an important role. If AI is prescribed by a standard curriculum (for instance, the ACM/IEEE Curriculum 1991 lists several AI and AI-related knowledge units), the likelihood of finding an AI course is greater. Another parameter that can play an important part in determining the range of AI courses offered is the size of the program. Larger programs tend to have larger class enrollments. Sometimes, larger class sizes can limit the range of advanced courses offered. For instance, the use of LEGO-based robot labs (see \" Curriculum Descant, \" SIGART Bulletin, Fall 1998) has been found to be more feasible in schools with smaller class sizes. Smaller class sizes also enable the creation of interdiscipli-nary AI courses that require active class participation. For example, I offer a course entitled Biologically Inspired Computational Models of …","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"9 1","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"1999-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91294777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
15 What Use Is Knowledge? Intelligence requires knowledge. Knowledge is used to determine behavior and to derive new knowledge from old. In software systems, we can code knowledge implicitly or explicitly. Implicit coding (called procedural knowledge) is knowledge about how to do things. Implicit encodings are typically coded as a set of instructions for performing a task (also known as a software program). Explicit codings (called declarative knowledge) use (usually taskspecific) computerized knowledge with a single program (called a knowledge representation system) that manipulates knowledge to perform services such as deriving new knowledge (called reasoning), planning, and acting. Programmers have been writing code that, quite successfully, automates a wide range of tasks. So, why should software developers consider using declarative knowledge representation in their intelligent software? One important answer lies in the high cost of updating software. When you need to change the behavior of your software, you must change its code. The Year 2000 (Y2K) problem is a clear example of this. By contrast, for software that makes use of a knowledge representation system, changing behavior can be as easy as changing the knowledge (in the case of the Y2K problem, the meaning of the date concept) in the knowledge representation system. Software that uses procedural knowledge is often brittle; that is, it will break if any of the assumptions made when coding the software change. (In the case of Y2K, the assumption was that the software would have long since been replaced by Y2K!) Another advantage of knowledge representation is in knowledge discovery, the discovery of new information from old. For example, you may have a large quantity of data (say a corpus or transaction log) and want to find useful correlations (a simple example of this is Amazon.com’s incitement to spend more money by pointing out that people that bought a book also bought other books). Writing special-purpose software for each type of discovery problem is inefficient and can be avoided by the use of knowledge representation software. Not all tasks require a full knowledge representation system; for those that do, the advantages are significant. Problems wherein the task knowledge of the domain does not change (or changes little) are more efficiently solved with procedural encoding (for example, a payroll system). Problems wherein the task knowledge changes (or is unknown) are candidates for the use of knowledge representation systems. For lots of examples of such problems (and knowledge representation systems) see Peter Clark’s list of knowledge-based projects and groups at http://www.cs.utexas.edu/users/mfkb/ related.html.
{"title":"Links: What use is knowledge?","authors":"Syed S. Ali","doi":"10.1145/309697.309701","DOIUrl":"https://doi.org/10.1145/309697.309701","url":null,"abstract":"15 What Use Is Knowledge? Intelligence requires knowledge. Knowledge is used to determine behavior and to derive new knowledge from old. In software systems, we can code knowledge implicitly or explicitly. Implicit coding (called procedural knowledge) is knowledge about how to do things. Implicit encodings are typically coded as a set of instructions for performing a task (also known as a software program). Explicit codings (called declarative knowledge) use (usually taskspecific) computerized knowledge with a single program (called a knowledge representation system) that manipulates knowledge to perform services such as deriving new knowledge (called reasoning), planning, and acting. Programmers have been writing code that, quite successfully, automates a wide range of tasks. So, why should software developers consider using declarative knowledge representation in their intelligent software? One important answer lies in the high cost of updating software. When you need to change the behavior of your software, you must change its code. The Year 2000 (Y2K) problem is a clear example of this. By contrast, for software that makes use of a knowledge representation system, changing behavior can be as easy as changing the knowledge (in the case of the Y2K problem, the meaning of the date concept) in the knowledge representation system. Software that uses procedural knowledge is often brittle; that is, it will break if any of the assumptions made when coding the software change. (In the case of Y2K, the assumption was that the software would have long since been replaced by Y2K!) Another advantage of knowledge representation is in knowledge discovery, the discovery of new information from old. For example, you may have a large quantity of data (say a corpus or transaction log) and want to find useful correlations (a simple example of this is Amazon.com’s incitement to spend more money by pointing out that people that bought a book also bought other books). Writing special-purpose software for each type of discovery problem is inefficient and can be avoided by the use of knowledge representation software. Not all tasks require a full knowledge representation system; for those that do, the advantages are significant. Problems wherein the task knowledge of the domain does not change (or changes little) are more efficiently solved with procedural encoding (for example, a payroll system). Problems wherein the task knowledge changes (or is unknown) are candidates for the use of knowledge representation systems. For lots of examples of such problems (and knowledge representation systems) see Peter Clark’s list of knowledge-based projects and groups at http://www.cs.utexas.edu/users/mfkb/ related.html.","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"8 1","pages":"15-16"},"PeriodicalIF":0.0,"publicationDate":"1999-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90245537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research themes and trends in artificial intelligence: an author co-citation analysis","authors":"W. Raghupathi, S. Nerur","doi":"10.1145/309697.309703","DOIUrl":"https://doi.org/10.1145/309697.309703","url":null,"abstract":"","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"4 1","pages":"18-23"},"PeriodicalIF":0.0,"publicationDate":"1999-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89950308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
e live in a time when funding for basic research, especially research on artificial intelligence (AI), also includes an evaluation or deliverable component. In most cases, the deliverable is a demonstration , a proof of concept, or an implementation of a prototype. Generally relegated to use within research labs/groups and reporting of results in various symposia, such artifacts tend to live a minimal existence. Some artifacts barely make it to the final demo. Several artifacts are being constantly used and serve as platforms for further research. Some of them have existed now for several years and have undergone enhancements, rewrites, and even complete reimplementations. I want to bring to your attention the arti-facts that you or your colleagues may have created and that are used in your research labs. I would like to appeal to you to bring these arti-facts into your classrooms. Incorporate them into your lab assignments and have your AI students get some experience with them. AI artifacts that exist in research labs can serve as excellent tools to help bring research into the classroom. They can be used in various ways: as demos that show off the state of the art, as working artifacts of theories discussed in texts, as laboratory exercises where students learn to use them, as case studies for studying concepts, as platforms for developing other AI artifacts. All together, a rich set of pedagogical devices can be available to enhance students' experience with AI. My proposal is not necessarily novel. Most instructors use some AI artifacts in one way or another. My appeal here is to focus energies into extending the boundaries of use of these artifacts. If you or your research group has produced an AI artifact, it would be worthwhile examining its use in the classroom. For example, is it something you can share with undergraduate students? With graduate stu-dents? In what form? Can you give a demo during a lecture? Would a short video clip suf-fice? Could the students operate it themselves? What types of lab assignment would highlight the main features of the artifact? Could it be used for students to do development work? The use of AI artifacts in the classroom requires planning and effort at different levels. The primary responsibility rests with the creators. First they have to try to answer some of the preceding questions in order to help create appropriate pedagogical materials. Next the …
{"title":"Curriculum descant: A new life for AI artifacts","authors":"Deepak Kumar","doi":"10.1145/309697.309700","DOIUrl":"https://doi.org/10.1145/309697.309700","url":null,"abstract":"e live in a time when funding for basic research, especially research on artificial intelligence (AI), also includes an evaluation or deliverable component. In most cases, the deliverable is a demonstration , a proof of concept, or an implementation of a prototype. Generally relegated to use within research labs/groups and reporting of results in various symposia, such artifacts tend to live a minimal existence. Some artifacts barely make it to the final demo. Several artifacts are being constantly used and serve as platforms for further research. Some of them have existed now for several years and have undergone enhancements, rewrites, and even complete reimplementations. I want to bring to your attention the arti-facts that you or your colleagues may have created and that are used in your research labs. I would like to appeal to you to bring these arti-facts into your classrooms. Incorporate them into your lab assignments and have your AI students get some experience with them. AI artifacts that exist in research labs can serve as excellent tools to help bring research into the classroom. They can be used in various ways: as demos that show off the state of the art, as working artifacts of theories discussed in texts, as laboratory exercises where students learn to use them, as case studies for studying concepts, as platforms for developing other AI artifacts. All together, a rich set of pedagogical devices can be available to enhance students' experience with AI. My proposal is not necessarily novel. Most instructors use some AI artifacts in one way or another. My appeal here is to focus energies into extending the boundaries of use of these artifacts. If you or your research group has produced an AI artifact, it would be worthwhile examining its use in the classroom. For example, is it something you can share with undergraduate students? With graduate stu-dents? In what form? Can you give a demo during a lecture? Would a short video clip suf-fice? Could the students operate it themselves? What types of lab assignment would highlight the main features of the artifact? Could it be used for students to do development work? The use of AI artifacts in the classroom requires planning and effort at different levels. The primary responsibility rests with the creators. First they have to try to answer some of the preceding questions in order to help create appropriate pedagogical materials. Next the …","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"16 1","pages":"13-16"},"PeriodicalIF":0.0,"publicationDate":"1999-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84410922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Instances and classes in software engineering","authors":"Chris Welty, D. Ferrucci","doi":"10.1145/309697.309705","DOIUrl":"https://doi.org/10.1145/309697.309705","url":null,"abstract":"","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"400 1","pages":"24-28"},"PeriodicalIF":0.0,"publicationDate":"1999-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80153192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. McRoy, Syed S. Ali, Angelo C. Restificar, S. Channarukul
We overview our recent work in specifying and building intelligent dialog systems that collaborate with users for a task. As part of this work we have specied and built systems for: giving medical students an opportunity to practice their decision making skills in English (B2); performing template-based natural language generation (YAG); detecting and rebutting arguments (ARGUER); recognizing and repairing misunderstandings (RRM); and assessing and augmenting patients’ health knowledge (PEAS). All of these systems make use of rich models of dialog for human-computer communication.
{"title":"Building intelligent dialog systems","authors":"S. McRoy, Syed S. Ali, Angelo C. Restificar, S. Channarukul","doi":"10.1145/298475.298484","DOIUrl":"https://doi.org/10.1145/298475.298484","url":null,"abstract":"We overview our recent work in specifying and building intelligent dialog systems that collaborate with users for a task. As part of this work we have specied and built systems for: giving medical students an opportunity to practice their decision making skills in English (B2); performing template-based natural language generation (YAG); detecting and rebutting arguments (ARGUER); recognizing and repairing misunderstandings (RRM); and assessing and augmenting patients’ health knowledge (PEAS). All of these systems make use of rich models of dialog for human-computer communication.","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"8 1","pages":"14-23"},"PeriodicalIF":0.0,"publicationDate":"1999-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90764806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Backtracking: the Chinese food problem","authors":"L. Hoebel, Chris Welty","doi":"10.1145/298475.298496","DOIUrl":"https://doi.org/10.1145/298475.298496","url":null,"abstract":"","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"99 5","pages":"48-49"},"PeriodicalIF":0.0,"publicationDate":"1999-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91431594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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