Pub Date : 2023-01-01DOI: 10.1007/s10489-022-03830-8
Jian-chun Lu, Junming Shao, Chunling Wu
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Fortunately for people seeking to learn more about artificial intelligence, a wide range of general and introductory educational materials related to AI is available on the Web. The Web resources described below, available both in electronic form and as pointers to conventional media such as books or videos, are not, however, of interest just to university educators and their students. Many of them are of broad scope and are useful to information technology specialists or AI practitioners seeking a starting-off point from which to improve their knowledge of the field. Some of the materials listed here will also be of use to educators in high schools or middle schools where AI topics may play a role in course curricula.
{"title":"Introductory AI educational resources on the web","authors":"R. Amant, R. Young","doi":"10.1145/504313.504319","DOIUrl":"https://doi.org/10.1145/504313.504319","url":null,"abstract":"Fortunately for people seeking to learn more about artificial intelligence, a wide range of general and introductory educational materials related to AI is available on the Web. The Web resources described below, available both in electronic form and as pointers to conventional media such as books or videos, are not, however, of interest just to university educators and their students. Many of them are of broad scope and are useful to information technology specialists or AI practitioners seeking a starting-off point from which to improve their knowledge of the field. Some of the materials listed here will also be of use to educators in high schools or middle schools where AI topics may play a role in course curricula.","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"10 1","pages":"15-17"},"PeriodicalIF":0.0,"publicationDate":"2001-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87572228","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}
Musings Inspired by Sun, Lemonade, and Lost Students
灵感来自太阳、柠檬水和迷失的学生
{"title":"Is AI abstract and impractical? isn't the answer obvious?","authors":"Lisa C. Kaczmarczyk","doi":"10.1145/504313.504321","DOIUrl":"https://doi.org/10.1145/504313.504321","url":null,"abstract":"Musings Inspired by Sun, Lemonade, and Lost Students","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"58 1","pages":"19-20"},"PeriodicalIF":0.0,"publicationDate":"2001-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86976207","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}
Introduction Many computer applications are concerned with interpreting or producing instructions and fostering education, for example, 1. Animated agents that execute instructions [Webber 1995], 2. Systems that automatically produce instructional text (see Excerpt A in Figure 1) [Paris 1995], 3. Intelligent tutoring systems (ITS) that help a student master a certain subject [Anderson 1995, Schulze 2000], and 4. Systems that facilitate student collaboration [Soller 2001]. Of these four examples, only example two is a true natural-language (NL) system: The animated agents in example one may be instructed via a menu; an ITS may provide feedback to the student via graphics. However, all the systems in examples one, three, and four potentially benefit from a natural-language interface. For instance, consider the learning gain, that is, how much a student learns in a certain setting (S). Generally, the learning gain is the difference between the student's score on the same test, before and after (S). It has been shown that the learning gain of students interacting with an ITS is halfway between the learning gain of students that were exposed to the material in the usual classroom setting (lowest) and students that interact with a human tutor (highest) [Anderson 1995]. The difference in learning gain between students interacting with an ITS and those interacting with a human tutor is attributed conversation between tutor and student Figure 1 (see page 24) presents two samples of NL instructions: The first is taken from an online help file, the second from a tutoring dialogue. These two examples illustrate some of the problems faced by systems that must support the interpretation and generation of instructions. Instructions in a technical manual, online help, or home repair manual such as those under (A) in teach how to perform a task mainly by describing the steps to be performed. They often include descriptions of what will happen as the result of a certain step (e.g., the window expands to show the Alarm options in step two) as a way to inform the user whether he or she is on the right track. The structure of the text closely reflects the structure of the task, as has long been known regarding task-oriented discourse [Grosz and Sidner 1986]. Tutorial dialogues such as (B) in Figure 1 present a completely different approach to instruction. (Note: This excerpt is taken from a dialogue in the BEESIM project corpus [Rosé, Di Eugenio, and Moore …
许多计算机应用与解释或生成指令以及促进教育有关，例如:执行指令的动画代理[Webber 1995]， 2。自动生成教学文本的系统(参见图1中的摘录A) [Paris 1995]， 3。智能辅导系统(ITS)，帮助学生掌握某一学科[Anderson 1995, Schulze 2000]，等。促进学生合作的系统[Soller 2001]。在这四个例子中，只有例子二是一个真正的自然语言系统:例子一中的动画代理可以通过菜单来指示;智能交通系统可以通过图形向学生提供反馈。然而，示例1、3和4中的所有系统都可能受益于自然语言接口。例如,考虑学习获得,也就是说,有多少学生学习在某些环境中(S)。一般来说,学习获得的区别是学生的分数相同的测试,之前和之后(年代)。它已经表明,学生的学习获得与它是介于交互学习获得的学生接触到的材料在平时的课堂环境与人类交互的(最低)和学生导师(最高)安德森[1995]。与人工智能互动的学生与与人类导师互动的学生之间的学习收益差异归因于导师与学生之间的对话。图1(见第24页)提供了两个NL指令示例:第一个取自在线帮助文件，第二个来自辅导对话。这两个例子说明了必须支持解释和生成指令的系统所面临的一些问题。技术手册、在线帮助或家庭维修手册中的说明，例如(a)项中的说明，主要通过描述要执行的步骤来教授如何执行任务。它们通常包括对某一步骤的结果的描述(例如，窗口展开以显示第二步中的警报选项)，以告知用户他或她是否在正确的轨道上。文本的结构紧密地反映了任务的结构，这一点在任务导向语篇中早已为人所知[Grosz and Sidner 1986]。图1中的(B)等教程对话框呈现了一种完全不同的教学方法。(注:本文节选自BEESIM项目语料库中的一段对话[ros， Di Eugenio, and Moore…]
{"title":"Natural-language processing for computer-supported instruction","authors":"Barbara Maria Di Eugenio","doi":"10.1145/504313.504323","DOIUrl":"https://doi.org/10.1145/504313.504323","url":null,"abstract":"Introduction Many computer applications are concerned with interpreting or producing instructions and fostering education, for example, 1. Animated agents that execute instructions [Webber 1995], 2. Systems that automatically produce instructional text (see Excerpt A in Figure 1) [Paris 1995], 3. Intelligent tutoring systems (ITS) that help a student master a certain subject [Anderson 1995, Schulze 2000], and 4. Systems that facilitate student collaboration [Soller 2001]. Of these four examples, only example two is a true natural-language (NL) system: The animated agents in example one may be instructed via a menu; an ITS may provide feedback to the student via graphics. However, all the systems in examples one, three, and four potentially benefit from a natural-language interface. For instance, consider the learning gain, that is, how much a student learns in a certain setting (S). Generally, the learning gain is the difference between the student's score on the same test, before and after (S). It has been shown that the learning gain of students interacting with an ITS is halfway between the learning gain of students that were exposed to the material in the usual classroom setting (lowest) and students that interact with a human tutor (highest) [Anderson 1995]. The difference in learning gain between students interacting with an ITS and those interacting with a human tutor is attributed conversation between tutor and student Figure 1 (see page 24) presents two samples of NL instructions: The first is taken from an online help file, the second from a tutoring dialogue. These two examples illustrate some of the problems faced by systems that must support the interpretation and generation of instructions. Instructions in a technical manual, online help, or home repair manual such as those under (A) in teach how to perform a task mainly by describing the steps to be performed. They often include descriptions of what will happen as the result of a certain step (e.g., the window expands to show the Alarm options in step two) as a way to inform the user whether he or she is on the right track. The structure of the text closely reflects the structure of the task, as has long been known regarding task-oriented discourse [Grosz and Sidner 1986]. Tutorial dialogues such as (B) in Figure 1 present a completely different approach to instruction. (Note: This excerpt is taken from a dialogue in the BEESIM project corpus [Rosé, Di Eugenio, and Moore …","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"5 1","pages":"22-32"},"PeriodicalIF":0.0,"publicationDate":"2001-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80075456","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 demise of “!”","authors":"Chris Welty","doi":"10.1145/383824.383834","DOIUrl":"https://doi.org/10.1145/383824.383834","url":null,"abstract":"","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"7 1","pages":"56"},"PeriodicalIF":0.0,"publicationDate":"2001-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73035779","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}
A I has relied on L i s p almost since the inception of the field. Other languages (e.g. Java [1]) may provide larger libraries of reusable code, but unlike Lisp libraries, their functionality is not necessarily tailored to the needs of AI developers. Many of the building blocks of an AI system, large or small, can be retrieved from the Web, giving Lisp developers a significant boost in their ability to focus on problem specifics and to prototype solutions. If we need, for example, ✶ basic search algorithms [2], ✶ regular expression processing [3], ✶ theorem proving [4], ✶ constraint satisfaction or logic programming [5], ✶ machine learning algorithms [6], ✶ planning algorithms [7], ✶ cross-platform user interfaces [8], ✶ statistical analysis and instrumentation [9], or any number of other functionality-enhancing toolkits, they are all available over the Web. In this column we take a brief tour of Web resources devoted to Common Lisp. (Common Lisp is an official ANSI Standard language, as of 1994, the first object-oriented language specification to be so approved, and is now the most widely used general-purpose dialect of the Lisp family of languages .) Much of the information presented here can also be found on the Web site for the Association of Lisp Users (ALU) [10]. One good place for information is the ALU Web site [10], which is a rich compendium of information about the language: reference materials, programming tools, implementations, history, and more. The Usenet group comp.lang.lisp is a gathering of knowledgeable and helpful experts, some of whom were closely involved in the development of Common Lisp. The Frequently Asked Questions list for comp.lang.lisp [11] is somewhat outdated with respect to software offerings and implementations, but also gives useful general information. Perhaps surprisingly, Lisp programming is not difficult; for example, Logo is a dialect of Lisp that has been used to teach children to program [12]. Experienced programmers sometimes face more difficult hurdles in " unlearning'' programming practices appropriate for other languages, to take advantage of the full power of Lisp. David Lamkins has written an online tutorial on programming in Common Lisp that should be accessible to programmers at all levels of experi-ence[13].
{"title":"Links: Common Lisp resources on the Web","authors":"R. Amant, R. Young","doi":"10.1145/383824.383828","DOIUrl":"https://doi.org/10.1145/383824.383828","url":null,"abstract":"A I has relied on L i s p almost since the inception of the field. Other languages (e.g. Java [1]) may provide larger libraries of reusable code, but unlike Lisp libraries, their functionality is not necessarily tailored to the needs of AI developers. Many of the building blocks of an AI system, large or small, can be retrieved from the Web, giving Lisp developers a significant boost in their ability to focus on problem specifics and to prototype solutions. If we need, for example, ✶ basic search algorithms [2], ✶ regular expression processing [3], ✶ theorem proving [4], ✶ constraint satisfaction or logic programming [5], ✶ machine learning algorithms [6], ✶ planning algorithms [7], ✶ cross-platform user interfaces [8], ✶ statistical analysis and instrumentation [9], or any number of other functionality-enhancing toolkits, they are all available over the Web. In this column we take a brief tour of Web resources devoted to Common Lisp. (Common Lisp is an official ANSI Standard language, as of 1994, the first object-oriented language specification to be so approved, and is now the most widely used general-purpose dialect of the Lisp family of languages .) Much of the information presented here can also be found on the Web site for the Association of Lisp Users (ALU) [10]. One good place for information is the ALU Web site [10], which is a rich compendium of information about the language: reference materials, programming tools, implementations, history, and more. The Usenet group comp.lang.lisp is a gathering of knowledgeable and helpful experts, some of whom were closely involved in the development of Common Lisp. The Frequently Asked Questions list for comp.lang.lisp [11] is somewhat outdated with respect to software offerings and implementations, but also gives useful general information. Perhaps surprisingly, Lisp programming is not difficult; for example, Logo is a dialect of Lisp that has been used to teach children to program [12]. Experienced programmers sometimes face more difficult hurdles in \" unlearning'' programming practices appropriate for other languages, to take advantage of the full power of Lisp. David Lamkins has written an online tutorial on programming in Common Lisp that should be accessible to programmers at all levels of experi-ence[13].","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"23 1","pages":"21-23"},"PeriodicalIF":0.0,"publicationDate":"2001-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91275479","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}
be to go through our mailbag and look for general themes that could be addressed en masse by creating a series of FAQs. Although there is real value in FAQs, we recognized that they are based on the premise that the authors are able to anticipate the types of questions that will typically be asked with accuracy. Moreover, once written, they require continued updating to retain their currency. In addition , we came to feel that a site built around FAQs had the potential to convey a closed-end, pigeonhole attitude as opposed to the open-ended sense of excitement that we wanted to permeate the site. Another thing we realized during our initial benchmarking forays online was that not only were people already posting FAQs addressing various AI subjects, but people were making available an amazing array of material and resources. Given the free access to online material that covered the spectrum from classic articles of the 1950s to the latest interactive demos and virtual museums, it was decided that rather than reinvent the proverbial wheel, the AI Topics site would assume the role of an intermediary poised between these resources and the non-professional information seeker. Since this vast pool of information can be both overwhelming (just as it often is for professionals too) and susceptible to considerable redundancy and misin-formation, we believed that there would be real value in mediating this relationship in at least four ways: 1. identifying readable overview articles that 1 curriculum descant T he challenge: Design a Web site to respond to the lay public's undifferenti-ated interest in artificial intelligence, their need for relevant, accurate resources and their quest for " the answer. " Our solution: Build an online library with several doorways to basic, understandable information selected for a target audience and presented in an environment that celebrates the vibrancy of AI and encourages further exploration. The AI Topics Web site was born of necessity. The year was 1998 and the American Association for Artificial Intelligence (AAAI) needed an effective and efficient means of responding to the questions beginning to flow in from students, journalists and others outside of AI. 3 The tenor of these inquiries typically ran along the lines of: ✱ I'm doing a story on intelligent agents. Can you tell me what they are and who is working on them? Oh, by the way, my deadline is tomorrow. ✱ I have a …
{"title":"Curriculum descant: AI topics: organizing online knowledge sources about AI for the lay public","authors":"Deepak Kumar","doi":"10.1145/383824.383830","DOIUrl":"https://doi.org/10.1145/383824.383830","url":null,"abstract":"be to go through our mailbag and look for general themes that could be addressed en masse by creating a series of FAQs. Although there is real value in FAQs, we recognized that they are based on the premise that the authors are able to anticipate the types of questions that will typically be asked with accuracy. Moreover, once written, they require continued updating to retain their currency. In addition , we came to feel that a site built around FAQs had the potential to convey a closed-end, pigeonhole attitude as opposed to the open-ended sense of excitement that we wanted to permeate the site. Another thing we realized during our initial benchmarking forays online was that not only were people already posting FAQs addressing various AI subjects, but people were making available an amazing array of material and resources. Given the free access to online material that covered the spectrum from classic articles of the 1950s to the latest interactive demos and virtual museums, it was decided that rather than reinvent the proverbial wheel, the AI Topics site would assume the role of an intermediary poised between these resources and the non-professional information seeker. Since this vast pool of information can be both overwhelming (just as it often is for professionals too) and susceptible to considerable redundancy and misin-formation, we believed that there would be real value in mediating this relationship in at least four ways: 1. identifying readable overview articles that 1 curriculum descant T he challenge: Design a Web site to respond to the lay public's undifferenti-ated interest in artificial intelligence, their need for relevant, accurate resources and their quest for \" the answer. \" Our solution: Build an online library with several doorways to basic, understandable information selected for a target audience and presented in an environment that celebrates the vibrancy of AI and encourages further exploration. The AI Topics Web site was born of necessity. The year was 1998 and the American Association for Artificial Intelligence (AAAI) needed an effective and efficient means of responding to the questions beginning to flow in from students, journalists and others outside of AI. 3 The tenor of these inquiries typically ran along the lines of: ✱ I'm doing a story on intelligent agents. Can you tell me what they are and who is working on them? Oh, by the way, my deadline is tomorrow. ✱ I have a …","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"1 1","pages":"25-30"},"PeriodicalIF":0.0,"publicationDate":"2001-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82923860","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}
The increasingly distributed nature of sales order systems results in some nagging, pressing, and crucial issues. These can be solved by the use of agent-based architectures. Growth of the Internet also has opened up possibilities for improving production logistics. Embracing the Internet for order processing can lead to cost-effective, fast, and flexible operations. So the trend now is to treat each customer order separately and make the best use of the Internet to process the order. This is easier said than done. The behavior of elements in the supply chain can no longer be preplanned. Consequently, these elements should have the intelligence to interpret the order, the ability to perceive the resource environment, the autonomy to take rational decisions, and reactivity to change their behavior to adapt to the environment. This is where agent technology can help the manufacturing community. At Infosys work is going on to develop an agent-oriented framework for sales order processing. The framework, which we call Agent-Based Sales Order Processing System (AESOPS), allows logistics personnel to conceptualize , design, and build a production environment as a set of loosely coupled distributed units over a number of physical locations. These production units can interact with each other to process any order to completion in a flexible yet consistent and efficient manner. We will discuss a few salient features of the framework. Desirable Features In a typical sales order processing application a number of stages are involved. The process is initiated with a customer placing an order. The order is reviewed by the Logistics department (See Figure 1). If the product is in Introduction " Customer is king. " This is more evident now than ever before for all industries, manufacturing in particular. Traditionally, manufacturing companies have based their production facilities at a small number of locations. The demand of the product was forecast using sophisticated forecasting methods, and a production plan was prepared accordingly. As long as customers were willing to select from a predefined set of models manufactured by the company, things went smoothly. In today's global and more competitive markets, customers have started demanding customized products. This has led to a situation where erstwhile planning methods go haywire. inventory it is shipped; if not Logistics prepares a plan for raw material inventory and production units for different processing stages taking into account the capacity, resource and time constraints. Jobs are often grouped to reduce cost. …
{"title":"A multi-agent system for sales order processing","authors":"A. S. Mondal, A. Jain","doi":"10.1145/383824.383831","DOIUrl":"https://doi.org/10.1145/383824.383831","url":null,"abstract":"The increasingly distributed nature of sales order systems results in some nagging, pressing, and crucial issues. These can be solved by the use of agent-based architectures. Growth of the Internet also has opened up possibilities for improving production logistics. Embracing the Internet for order processing can lead to cost-effective, fast, and flexible operations. So the trend now is to treat each customer order separately and make the best use of the Internet to process the order. This is easier said than done. The behavior of elements in the supply chain can no longer be preplanned. Consequently, these elements should have the intelligence to interpret the order, the ability to perceive the resource environment, the autonomy to take rational decisions, and reactivity to change their behavior to adapt to the environment. This is where agent technology can help the manufacturing community. At Infosys work is going on to develop an agent-oriented framework for sales order processing. The framework, which we call Agent-Based Sales Order Processing System (AESOPS), allows logistics personnel to conceptualize , design, and build a production environment as a set of loosely coupled distributed units over a number of physical locations. These production units can interact with each other to process any order to completion in a flexible yet consistent and efficient manner. We will discuss a few salient features of the framework. Desirable Features In a typical sales order processing application a number of stages are involved. The process is initiated with a customer placing an order. The order is reviewed by the Logistics department (See Figure 1). If the product is in Introduction \" Customer is king. \" This is more evident now than ever before for all industries, manufacturing in particular. Traditionally, manufacturing companies have based their production facilities at a small number of locations. The demand of the product was forecast using sophisticated forecasting methods, and a production plan was prepared accordingly. As long as customers were willing to select from a predefined set of models manufactured by the company, things went smoothly. In today's global and more competitive markets, customers have started demanding customized products. This has led to a situation where erstwhile planning methods go haywire. inventory it is shipped; if not Logistics prepares a plan for raw material inventory and production units for different processing stages taking into account the capacity, resource and time constraints. Jobs are often grouped to reduce cost. …","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"18 1","pages":"32-42"},"PeriodicalIF":0.0,"publicationDate":"2001-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75309266","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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