I attended the annual meeting of IFIP TC12 on August 6, 2001, during IJCAI in Seattle, as the ACM representative. IFIP is the International Federation for Information Processing. It was established in 1960 under the auspices of UNESCO, and is a society of societies. That is, the members of IFIP are not people, but societies and associations. Mostly, there is one member from each country, serving as the representative of computing activities and people in that country. The United States is an exception. Instead of a single society representing the United States, ACM and IEEE are members of IFIP, even though both of these are international, not national organizations. IFIP technical work is managed by a set of 12 Technical Committees, devoted to various aspects of the field of computing. I am the ACM representative to TC12, Artificial Intelligence. The stated aim of TC12 is, “Research in AI and promotion of interdisciplinary exchange between AI and other fields of information processing,” and its scope is to, “Further develop the foundation for AI on the basis of computer science principles. Contribute techniques from AI to the enrichment of computer science and information processing. Develop AI techniques as part of information processing technologies to enable and advance practical applications.” [www.ifip.or.at/bulletin/bulltcs/tc12_aim.htm] IFIP TCs, in turn, have working groups as part of them. TC12 has two active working groups: WC 12.5, KnowledgeOriented Development of Applications and WG 12.6 Intelligent Information Management. TC12 meets annually during an international AI meeting. As I said above, this year’s meeting was held during IJCAI in Seattle. Nine of the 24 members were present, including the chair, Bernd Neumann, representing Germany’s Gesellschaft für Informatik, and the Secretary, Dan O’Leary, representing IEEE. At the meeting, there was some discussion of what IFIP does. The main message I got from this is that IFIP will cooperate with international conferences, and is especially good at marketing and publications. It usually wants a share of the revenues, but this is negotiable. In fact, it may be possible to get financial support from IFIP. If you are planning a conference, it would be a good idea to discuss it with them. You should start discussions with Prof. Neumann or me, as TC support is important. We specifically discussed support for student attendance, and, especially, given the UNESCO connection, support for attendance from underdeveloped countries. A major part of the meeting was devoted to the upcoming international conference on Intelligent Information Processing (IIP-2002), part of the IFIP World Computer Congress, to be held in Montreal, August 25-30, 2002. The IIP conferences are organized by TC12. IIP-2002 will have two tracks: Knowledge-Based System Architecture, and Intelligent Information Management. It will also have five linkage sessions, which are being organized in cooperation with other TCs. The link
2001年8月6日，在西雅图IJCAI期间，我作为ACM代表参加了IFIP TC12年会。IFIP是国际信息处理联合会。它于1960年在联合国教科文组织的主持下成立，是一个社团的社团。也就是说，IFIP的成员不是人，而是社会和协会。大多数情况下，每个国家都有一名成员，作为该国计算活动和人员的代表。美国是个例外。ACM和IEEE都是IFIP的成员，而不是代表美国的单一协会，尽管这两个组织都是国际性的，而不是全国性的组织。IFIP的技术工作由一组12个技术委员会管理，专门负责计算领域的各个方面。我是ACM在TC12人工智能领域的代表。TC12的既定目标是“研究人工智能并促进人工智能与其他信息处理领域之间的跨学科交流”，其范围是“在计算机科学原理的基础上进一步发展人工智能的基础”。为丰富计算机科学和信息处理贡献人工智能技术。开发人工智能技术作为信息处理技术的一部分，以实现和推进实际应用。“[www.ifip.or.at/bulletin/bulltcs/tc12_aim.htm] IFIP技术委员会又有工作组作为其一部分。TC12有两个活跃的工作组:wc12.5面向知识的应用开发工作组和wc12.6智能信息管理工作组。TC12每年在国际人工智能会议期间举行会议。正如我上面所说，今年的会议是在西雅图IJCAI期间举行的。24名成员中有9人出席了会议，其中包括代表德国Gesellschaft fr Informatik的主席Bernd Neumann和代表IEEE的秘书Dan O 'Leary。在会议上，对IFIP的作用进行了一些讨论。我从中得到的主要信息是，IFIP将与国际会议合作，并特别擅长营销和出版。它通常想要收入分成，但这是可以协商的。事实上，有可能从国际投资计划获得财政支持。如果你计划开一个会议，和他们讨论一下是个好主意。你应该先和Neumann教授或我讨论，因为TC的支持很重要。我们特别讨论了对学生出勤的支持，特别是考虑到与教科文组织的联系，对欠发达国家出勤的支持。会议的主要部分是关于即将到来的智能信息处理国际会议(IIP-2002)，这是iip世界计算机大会的一部分，将于2002年8月25日至30日在蒙特利尔举行。IIP会议由TC12组织。IIP-2002将分为两个主题:基于知识的系统架构和智能信息管理。它还将与其他技术转让公司合作举办五次联系会议。联动会议将包括:自主代理-控制和安全;智能信息系统的创新软件架构语义网;个性化Web交互;网络学习。TC12的下一届年会将在IIP-2002期间在蒙特利尔举行。
{"title":"Conference review: IFIP TC12","authors":"S. Shapiro","doi":"10.1145/383824.383826","DOIUrl":"https://doi.org/10.1145/383824.383826","url":null,"abstract":"I attended the annual meeting of IFIP TC12 on August 6, 2001, during IJCAI in Seattle, as the ACM representative. IFIP is the International Federation for Information Processing. It was established in 1960 under the auspices of UNESCO, and is a society of societies. That is, the members of IFIP are not people, but societies and associations. Mostly, there is one member from each country, serving as the representative of computing activities and people in that country. The United States is an exception. Instead of a single society representing the United States, ACM and IEEE are members of IFIP, even though both of these are international, not national organizations. IFIP technical work is managed by a set of 12 Technical Committees, devoted to various aspects of the field of computing. I am the ACM representative to TC12, Artificial Intelligence. The stated aim of TC12 is, “Research in AI and promotion of interdisciplinary exchange between AI and other fields of information processing,” and its scope is to, “Further develop the foundation for AI on the basis of computer science principles. Contribute techniques from AI to the enrichment of computer science and information processing. Develop AI techniques as part of information processing technologies to enable and advance practical applications.” [www.ifip.or.at/bulletin/bulltcs/tc12_aim.htm] IFIP TCs, in turn, have working groups as part of them. TC12 has two active working groups: WC 12.5, KnowledgeOriented Development of Applications and WG 12.6 Intelligent Information Management. TC12 meets annually during an international AI meeting. As I said above, this year’s meeting was held during IJCAI in Seattle. Nine of the 24 members were present, including the chair, Bernd Neumann, representing Germany’s Gesellschaft für Informatik, and the Secretary, Dan O’Leary, representing IEEE. At the meeting, there was some discussion of what IFIP does. The main message I got from this is that IFIP will cooperate with international conferences, and is especially good at marketing and publications. It usually wants a share of the revenues, but this is negotiable. In fact, it may be possible to get financial support from IFIP. If you are planning a conference, it would be a good idea to discuss it with them. You should start discussions with Prof. Neumann or me, as TC support is important. We specifically discussed support for student attendance, and, especially, given the UNESCO connection, support for attendance from underdeveloped countries. A major part of the meeting was devoted to the upcoming international conference on Intelligent Information Processing (IIP-2002), part of the IFIP World Computer Congress, to be held in Montreal, August 25-30, 2002. The IIP conferences are organized by TC12. IIP-2002 will have two tracks: Knowledge-Based System Architecture, and Intelligent Information Management. It will also have five linkage sessions, which are being organized in cooperation with other TCs. The link","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"51 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2001-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90243435","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}
read additional material on other forms of machine learning to broaden their view of what the field includes, while not becoming overwhelmed with details of implementation. Useful data sets can often be obtained from faculty members in other departments on campus. Faculty members in other departments are likely to use other approaches (such as logistic regression) when analyzing their data, and some will welcome having a student try an entirely different approach to gain insight into the problem. The students, in turn, are able to see their computer science talents as useful relative to questions in another discipline. Another reason the course is attractive is that a teacher can give students the opportunity to use and modify large software systems. Several machine learning systems are available for downloading, for example, Students can be exposed to a culture where data sets and code are shared and can be expected to become oriented to a large system and make changes or extensions to a specific part of such a system. An undergraduate course in machine learning can be offered to a wide range of students, with minimal prerequisites, for example, to students who have completed a data structures course. Not even an artificial intelligence course need be a prerequisite; however, key topics such as search, heuristics, and representation must be introduced. Whereas a graduate level machine learning course is often structured to read scores of journal papers that describe different systems (perhaps working with three or four systems), an undergraduate-level course can eschew the " breadth " of machine learning and adopt a specific focus, Machine Learning for the Masses curriculum descant F ormerly considered an esoteric subfield of computer science, machine learning is now seeing broad use in computer science applications. It is used, for example, in search engines, computer games, adaptive user interfaces, personalized assistants, Web bots, and scientific applications. However, few colleges and universities require a course in machine learning as part of an undergraduate major in computer science. It is time for us as computer science educators to recast an introduction to machine learning concepts as a staple of a computer science education. Many possible flavors of machine learning might be emphasized, and in a one-course introduction to the field, a student must chart a course consistent with the educational environment and the instructor's background. For example, you might focus a course on a particular approach to machine …
{"title":"Curriculum descant: machine learning for the masses","authors":"C. Congdon, Deepak Kumar","doi":"10.1145/378116.378118","DOIUrl":"https://doi.org/10.1145/378116.378118","url":null,"abstract":"read additional material on other forms of machine learning to broaden their view of what the field includes, while not becoming overwhelmed with details of implementation. Useful data sets can often be obtained from faculty members in other departments on campus. Faculty members in other departments are likely to use other approaches (such as logistic regression) when analyzing their data, and some will welcome having a student try an entirely different approach to gain insight into the problem. The students, in turn, are able to see their computer science talents as useful relative to questions in another discipline. Another reason the course is attractive is that a teacher can give students the opportunity to use and modify large software systems. Several machine learning systems are available for downloading, for example, Students can be exposed to a culture where data sets and code are shared and can be expected to become oriented to a large system and make changes or extensions to a specific part of such a system. An undergraduate course in machine learning can be offered to a wide range of students, with minimal prerequisites, for example, to students who have completed a data structures course. Not even an artificial intelligence course need be a prerequisite; however, key topics such as search, heuristics, and representation must be introduced. Whereas a graduate level machine learning course is often structured to read scores of journal papers that describe different systems (perhaps working with three or four systems), an undergraduate-level course can eschew the \" breadth \" of machine learning and adopt a specific focus, Machine Learning for the Masses curriculum descant F ormerly considered an esoteric subfield of computer science, machine learning is now seeing broad use in computer science applications. It is used, for example, in search engines, computer games, adaptive user interfaces, personalized assistants, Web bots, and scientific applications. However, few colleges and universities require a course in machine learning as part of an undergraduate major in computer science. It is time for us as computer science educators to recast an introduction to machine learning concepts as a staple of a computer science education. Many possible flavors of machine learning might be emphasized, and in a one-course introduction to the field, a student must chart a course consistent with the educational environment and the instructor's background. For example, you might focus a course on a particular approach to machine …","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"137 1","pages":"15-16"},"PeriodicalIF":0.0,"publicationDate":"2001-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86616533","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 nine lives of the AI","authors":"Syed S. Ali","doi":"10.1145/378116.378128","DOIUrl":"https://doi.org/10.1145/378116.378128","url":null,"abstract":"","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"14 1","pages":"56-"},"PeriodicalIF":0.0,"publicationDate":"2001-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74506325","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":"Creating natural language ouput for real-time applications","authors":"S. McRoy, S. Channarukul, Syed S. Ali","doi":"10.1145/378116.378122","DOIUrl":"https://doi.org/10.1145/378116.378122","url":null,"abstract":"","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"91 1","pages":"21-34"},"PeriodicalIF":0.0,"publicationDate":"2001-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85148018","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 s John Laird pointed out in his IAAI/AAAI invited talk last year, artificial intelligence (AI) research and computer gaming have quite a bit to offer each other. Although many commercially successful computer games have been rather vis-ceral and violent, AI techniques offer the promise of creating engaging and dynamic interactive entertainment with strong narrative components. For AI researchers working in the context of computer games, research challenges are as complex and compelling as many real-world problem areas; gaming environments offer unique interfaces and modes of use and an extensive existing base of potential users. In this article, we introduce some aspects of the application of artificial intelligence research to interactive entertainment. Although intelligent techniques certainly apply to a wide range of computer games, here we will focus on games that simulate or create highly interactive virtual envi-ronments—games in which one or more users control various aspects of the game's world, either in discrete steps (for example, turn-taking) or in continuous real-time modes. These kinds of computer games are excellent environments for artificial intelligence researchers to explore for several reasons. First, as testbeds for AI systems computer games provide a unique combination of simulation and reality. That is, the environment in which a computer game user interacts is virtual, but that environment is not a simulation of the problem domain; it is the problem domain. As a result, AI researchers can choose to side-step issues such as noisy sensor data, imperfect effectors, or other complications often found in real-world problems and still address realistic problems in the game environment. Second, gaming environments pose a range of problems, at both the strategic and interface levels. Strategic-level challenges in computer games can involve mapping or choosing between complex strategies, refining components of a strategy by formulating context-specific move sequences, and detecting and responding to human users' actions. At the interface level, intelligent components inside a game must control how the game world is presented to the users. Perhaps a unique property of 3-D game environments is that, in many aspects, they are their own interface. That is, every aspect of a game's virtual e n v i r o n m e n t — i t s objects, characters, lighting , sound, and camera— can be exploited by the system to create an overall effective interaction. Recent research has addressed many of the issues at the interface level (for example, the …
{"title":"Links: artificial intelligence and interactive entertainment","authors":"R. Amant, R. Young","doi":"10.1145/378116.378120","DOIUrl":"https://doi.org/10.1145/378116.378120","url":null,"abstract":"A s John Laird pointed out in his IAAI/AAAI invited talk last year, artificial intelligence (AI) research and computer gaming have quite a bit to offer each other. Although many commercially successful computer games have been rather vis-ceral and violent, AI techniques offer the promise of creating engaging and dynamic interactive entertainment with strong narrative components. For AI researchers working in the context of computer games, research challenges are as complex and compelling as many real-world problem areas; gaming environments offer unique interfaces and modes of use and an extensive existing base of potential users. In this article, we introduce some aspects of the application of artificial intelligence research to interactive entertainment. Although intelligent techniques certainly apply to a wide range of computer games, here we will focus on games that simulate or create highly interactive virtual envi-ronments—games in which one or more users control various aspects of the game's world, either in discrete steps (for example, turn-taking) or in continuous real-time modes. These kinds of computer games are excellent environments for artificial intelligence researchers to explore for several reasons. First, as testbeds for AI systems computer games provide a unique combination of simulation and reality. That is, the environment in which a computer game user interacts is virtual, but that environment is not a simulation of the problem domain; it is the problem domain. As a result, AI researchers can choose to side-step issues such as noisy sensor data, imperfect effectors, or other complications often found in real-world problems and still address realistic problems in the game environment. Second, gaming environments pose a range of problems, at both the strategic and interface levels. Strategic-level challenges in computer games can involve mapping or choosing between complex strategies, refining components of a strategy by formulating context-specific move sequences, and detecting and responding to human users' actions. At the interface level, intelligent components inside a game must control how the game world is presented to the users. Perhaps a unique property of 3-D game environments is that, in many aspects, they are their own interface. That is, every aspect of a game's virtual e n v i r o n m e n t — i t s objects, characters, lighting , sound, and camera— can be exploited by the system to create an overall effective interaction. Recent research has addressed many of the issues at the interface level (for example, the …","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"39 1","pages":"17-19"},"PeriodicalIF":0.0,"publicationDate":"2001-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78725665","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}
P. Constantopoulos, V. Christophides, D. Plexousakis
i n t e l l i g e n c e • S u m m e r 2 0 0 1 39 Introduction In 1998 the World-Wide Web Consortium (W3C) inaugurated a research initiative centered on the idea of providing semantics for and facilitating the extraction of knowledge from the World-Wide Web. The Semantic Web is a vision of the creator of the WWW, Tim Berners-Lee, who describes it as “a Web of data, documents, or portions of documents, that can be processed directly or indirectly by machines” not just for display purposes, but for automation, integration and reuse across various applications. The primary goal of the Semantic Web is to define infrastructure, standards, and policies facilitating an explicit description of the meaning of Web resources that can be processed both by automated tools and people. This effort towards the next evolution step of the Web has given rise to a large number of research problems that relate to models, architectures, applications, and services for the Semantic Web. The following is a list of research issues that were put forth as themes for soliciting workshop submissions. ✦ Formal Foundations of Web Metadata Standards ✦ Semantic Interoperability Frameworks ✦ Information and Services Brokering Architectures ✦ Metadata Creation, Extraction, and Storage ✦ Query Languages for the Semantic Web ✦ Distributed Inference Services ✦ Digital Signatures and Web of Trust ✦ Advanced Resource Discovery Interfaces ✦ Automated Classification of Web Resources ✦ Superimposed Web Resource Annotation & RecommendationTools ✦ Personalization and Intellectual Property Rights ✦ Semantic Web Applications: Knowledge Portals, Electronic Commerce The objective of the workshop was the creation of a forum for presenting research results in developing infrastructure for the Semantic Web and for enabling and fostering interaction among international researchers. The collocation of the workshop with the European Conference on Digital Libraries broadened the intended scope of the workshop and attracted participation and interaction from industry in addition to the academic and research communities. The workshop’s audience comprised researchers and practitioners in the areas of databases, intelligent information integration, knowledge representation, knowledge management, information retrieval, metadata, Web standards, digital libraries, and others. The workshop was organized as a post-conference one-day workshop at ECDL 2000 in Lisbon, Portugal. Panos Constantopoulos chaired the workshop committee with Vassilis Christophides and Dimitris Plexousakis as Program Committee Co-Chairs. A total of 29 papers were submitted to the workshop. Each paper was peer-reviewed by at least two referees. Despite the overall high quality of the submissions, only nine papers were accepted by the program committee for presentation at the single-day event. Overall, the workshop drew considerable attention at ECDL 2000: 63 registered participants from 22 countries. The workshop was sponsored by ERCIM (the
{"title":"Conference review: Semantic Web Workshop:: models, architectures and management","authors":"P. Constantopoulos, V. Christophides, D. Plexousakis","doi":"10.1145/378116.378124","DOIUrl":"https://doi.org/10.1145/378116.378124","url":null,"abstract":"i n t e l l i g e n c e • S u m m e r 2 0 0 1 39 Introduction In 1998 the World-Wide Web Consortium (W3C) inaugurated a research initiative centered on the idea of providing semantics for and facilitating the extraction of knowledge from the World-Wide Web. The Semantic Web is a vision of the creator of the WWW, Tim Berners-Lee, who describes it as “a Web of data, documents, or portions of documents, that can be processed directly or indirectly by machines” not just for display purposes, but for automation, integration and reuse across various applications. The primary goal of the Semantic Web is to define infrastructure, standards, and policies facilitating an explicit description of the meaning of Web resources that can be processed both by automated tools and people. This effort towards the next evolution step of the Web has given rise to a large number of research problems that relate to models, architectures, applications, and services for the Semantic Web. The following is a list of research issues that were put forth as themes for soliciting workshop submissions. ✦ Formal Foundations of Web Metadata Standards ✦ Semantic Interoperability Frameworks ✦ Information and Services Brokering Architectures ✦ Metadata Creation, Extraction, and Storage ✦ Query Languages for the Semantic Web ✦ Distributed Inference Services ✦ Digital Signatures and Web of Trust ✦ Advanced Resource Discovery Interfaces ✦ Automated Classification of Web Resources ✦ Superimposed Web Resource Annotation & RecommendationTools ✦ Personalization and Intellectual Property Rights ✦ Semantic Web Applications: Knowledge Portals, Electronic Commerce The objective of the workshop was the creation of a forum for presenting research results in developing infrastructure for the Semantic Web and for enabling and fostering interaction among international researchers. The collocation of the workshop with the European Conference on Digital Libraries broadened the intended scope of the workshop and attracted participation and interaction from industry in addition to the academic and research communities. The workshop’s audience comprised researchers and practitioners in the areas of databases, intelligent information integration, knowledge representation, knowledge management, information retrieval, metadata, Web standards, digital libraries, and others. The workshop was organized as a post-conference one-day workshop at ECDL 2000 in Lisbon, Portugal. Panos Constantopoulos chaired the workshop committee with Vassilis Christophides and Dimitris Plexousakis as Program Committee Co-Chairs. A total of 29 papers were submitted to the workshop. Each paper was peer-reviewed by at least two referees. Despite the overall high quality of the submissions, only nine papers were accepted by the program committee for presentation at the single-day event. Overall, the workshop drew considerable attention at ECDL 2000: 63 registered participants from 22 countries. The workshop was sponsored by ERCIM (the","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"22 1","pages":"39-44"},"PeriodicalIF":0.0,"publicationDate":"2001-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76754868","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}
W ith the introduction of new, efficient techniques for planning, interest in the field has risen sharply in recent years. Planning systems have come a long way since the days of Blocks World. Today, planning and scheduling techniques are being used to solve problems in military campaign planning, robot navigation, industrial equipment scheduling, human-computer interaction, and many other real-world domains. Planning, which is the process of finding a sequence of actions that meets an agents goal, is a thriving area of artificial intelligence (AI) research and is growing almost faster than one can keep track. Fortunately, an abundance of information about planning is available, in both paper and electronic forms; answers to most of the questions a newcomer to the field might ask can easily be found. Early approaches to planning , as exemplified by STRIPS (Stanford Research Institute Problem Solver), viewed the planning problem as that of generating a sequence of operators that will transform the current state of the environment into a goal state. This approach is sometimes referred to in the planning literature as " state space search. " Eventually a different perspective gained dominance , in which states represented not properties of the environment but the plans being considered. That is, rather than searching through a space of world states, planning systems searched through a space of partially elaborated plans. Penberthy and Weld's UCPOP, a partial-order planner, is one of the best known examples of this approach. More recently, with the development of the Graphplan algorithm by Blum and Furst, another conceptual shift has taken place. Many systems now treat planning as a form of constraint satisfaction, exploiting new work on efficient algorithms in this area. These are just a few high points in a rich and very branchy history of planning systems. A good historical introduction to planning starts with an article in AI Magazine titled " AI Planning: Systems and Techniques " (Hendler et al. 1990). Beginning with a description of the planning problem as " designing systems which can describe a set of actions (or plan) which can be expected to allow the system to reach a desired goal, " Hendler et al. discuss common techniques used for planning, a chronology of planning systems, and some of the problems that planning addresses: reasoning about time, physical constraints on solutions, execution uncertainty, perception, and multi-agent systems. In addition to Hendler et al.'s discussion of …
近年来，随着新的、有效的规划技术的引入，人们对这一领域的兴趣急剧上升。自Blocks World时代以来，规划系统已经走过了漫长的道路。今天，计划和调度技术正被用于解决军事行动计划、机器人导航、工业设备调度、人机交互和许多其他现实世界领域的问题。规划是寻找满足代理目标的一系列行动的过程，是人工智能(AI)研究的一个蓬勃发展的领域，其发展速度几乎快得让人无法追踪。幸运的是，有大量关于规划的信息，有纸质的，也有电子的;这个领域的新手可能会问的大多数问题的答案都很容易找到。早期的规划方法，如斯坦福研究所问题解决器(strip)所示，将规划问题视为生成一系列将环境当前状态转换为目标状态的操作符的问题。这种方法有时在规划文献中被称为“状态空间搜索”。最终，一种不同的观点占据了主导地位，在这种观点中，国家代表的不是环境的属性，而是正在考虑的计划。也就是说，计划系统不是在世界状态的空间中搜索，而是在部分详细计划的空间中搜索。Penberthy和Weld的偏序规划器UCPOP就是这种方法最著名的例子之一。最近，随着Blum和Furst的Graphplan算法的发展，另一个概念上的转变发生了。许多系统现在将规划视为约束满足的一种形式，在这一领域开发了高效算法的新工作。这些只是规划系统丰富而分支的历史中的几个亮点。关于规划的一个很好的历史介绍始于AI杂志上的一篇题为“AI规划:系统和技术”的文章(Hendler et al. 1990)。Hendler等人首先将规划问题描述为“设计能够描述一系列行动(或计划)的系统，这些行动(或计划)可以使系统达到预期目标”，然后讨论了用于规划的常用技术，规划系统的年表，以及规划解决的一些问题:时间推理，解决方案的物理约束，执行不确定性，感知和多代理系统。除了Hendler等人关于…
{"title":"Links: AI planning resources on the Web","authors":"R. Amant, R. Young","doi":"10.1145/376451.376464","DOIUrl":"https://doi.org/10.1145/376451.376464","url":null,"abstract":"W ith the introduction of new, efficient techniques for planning, interest in the field has risen sharply in recent years. Planning systems have come a long way since the days of Blocks World. Today, planning and scheduling techniques are being used to solve problems in military campaign planning, robot navigation, industrial equipment scheduling, human-computer interaction, and many other real-world domains. Planning, which is the process of finding a sequence of actions that meets an agents goal, is a thriving area of artificial intelligence (AI) research and is growing almost faster than one can keep track. Fortunately, an abundance of information about planning is available, in both paper and electronic forms; answers to most of the questions a newcomer to the field might ask can easily be found. Early approaches to planning , as exemplified by STRIPS (Stanford Research Institute Problem Solver), viewed the planning problem as that of generating a sequence of operators that will transform the current state of the environment into a goal state. This approach is sometimes referred to in the planning literature as \" state space search. \" Eventually a different perspective gained dominance , in which states represented not properties of the environment but the plans being considered. That is, rather than searching through a space of world states, planning systems searched through a space of partially elaborated plans. Penberthy and Weld's UCPOP, a partial-order planner, is one of the best known examples of this approach. More recently, with the development of the Graphplan algorithm by Blum and Furst, another conceptual shift has taken place. Many systems now treat planning as a form of constraint satisfaction, exploiting new work on efficient algorithms in this area. These are just a few high points in a rich and very branchy history of planning systems. A good historical introduction to planning starts with an article in AI Magazine titled \" AI Planning: Systems and Techniques \" (Hendler et al. 1990). Beginning with a description of the planning problem as \" designing systems which can describe a set of actions (or plan) which can be expected to allow the system to reach a desired goal, \" Hendler et al. discuss common techniques used for planning, a chronology of planning systems, and some of the problems that planning addresses: reasoning about time, physical constraints on solutions, execution uncertainty, perception, and multi-agent systems. In addition to Hendler et al.'s discussion of …","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"7 1","pages":"17-19"},"PeriodicalIF":0.0,"publicationDate":"2001-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75790588","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}
emphasize writing and critical-thinking skills. For instance, at Bryn Mawr college, we require all our incoming students to take two such courses, designated as College Seminars. I quote from the college's prospectus about a description of these courses: " The College Seminars are discussion-oriented , reading-and writing-intensive courses for first-and second-year students. Topics (of these courses) vary from year to year, but all seminars are designed and taught by faculty from several different fields and are intended to engage broad, fundamental issues and questions. These courses have a predisciplinary rather than an interdisci-plinary intent: their aim is to revisit and revitalize questions that tend to be taken as settled by existing disciplines. Course materials include books and essays but also films, material objects, social practices, scientific observations and experiments. " For more information on this program, and specific course description visit Inspired by sentiments expressed in the two pieces above, I would like to propose the creation of a pre-disciplinary writing intensive course that centers around the issues of artificial intelligence and philosophy of mind. Such a course can use a combination of materials taken from a selection of classic papers, videos of AI systems, movies (AI documentaries as well as Hollywood-style productions), and articles on AI as reported in the popular press. When team-taught by faculty from other disciplines , one quickly discovers an exciting array of readings that could be used to formulate the course content. I am also thinking of I n an earlier issue of this column, (" Interdisciplinary AI, " intelligence, Volume 11, Number 1, 2000) Richard Wyatt wrote: " Artificial intelligence, as a course offered within computer science programs, should be an interdisciplinary course. Stated more carefully , the correct design for an undergraduate artificial intelligence course for a computer science department is such that it should be able to be taken by any student possessing good analytic skills but lacking programming skills. The interdisciplinary nature of a well-designed artificial intelligence course is not itself a goal of the preferred course design, but is a consequence of it. " Computer science programs are not the ideal training grounds for artificial intelligence. There are of course exceptions, but in general, computer science students lack an understanding of philosophical issues. " In short, artificial intelligence should be an interdisciplinary course and we, as instructors , should consciously conceive of it as such. " If we ourselves …
{"title":"Curriculum descant: pre-disciplinary AI","authors":"Deepak Kumar","doi":"10.1145/376451.376461","DOIUrl":"https://doi.org/10.1145/376451.376461","url":null,"abstract":"emphasize writing and critical-thinking skills. For instance, at Bryn Mawr college, we require all our incoming students to take two such courses, designated as College Seminars. I quote from the college's prospectus about a description of these courses: \" The College Seminars are discussion-oriented , reading-and writing-intensive courses for first-and second-year students. Topics (of these courses) vary from year to year, but all seminars are designed and taught by faculty from several different fields and are intended to engage broad, fundamental issues and questions. These courses have a predisciplinary rather than an interdisci-plinary intent: their aim is to revisit and revitalize questions that tend to be taken as settled by existing disciplines. Course materials include books and essays but also films, material objects, social practices, scientific observations and experiments. \" For more information on this program, and specific course description visit Inspired by sentiments expressed in the two pieces above, I would like to propose the creation of a pre-disciplinary writing intensive course that centers around the issues of artificial intelligence and philosophy of mind. Such a course can use a combination of materials taken from a selection of classic papers, videos of AI systems, movies (AI documentaries as well as Hollywood-style productions), and articles on AI as reported in the popular press. When team-taught by faculty from other disciplines , one quickly discovers an exciting array of readings that could be used to formulate the course content. I am also thinking of I n an earlier issue of this column, (\" Interdisciplinary AI, \" intelligence, Volume 11, Number 1, 2000) Richard Wyatt wrote: \" Artificial intelligence, as a course offered within computer science programs, should be an interdisciplinary course. Stated more carefully , the correct design for an undergraduate artificial intelligence course for a computer science department is such that it should be able to be taken by any student possessing good analytic skills but lacking programming skills. The interdisciplinary nature of a well-designed artificial intelligence course is not itself a goal of the preferred course design, but is a consequence of it. \" Computer science programs are not the ideal training grounds for artificial intelligence. There are of course exceptions, but in general, computer science students lack an understanding of philosophical issues. \" In short, artificial intelligence should be an interdisciplinary course and we, as instructors , should consciously conceive of it as such. \" If we ourselves …","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"1 1","pages":"15-16"},"PeriodicalIF":0.0,"publicationDate":"2001-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88291832","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 current slate of ACM journals does not provide much outlet for AI research. So any proposal to start an ACM journal in an area of AI merits serious consideration. Just such a proposal is about to be made to the ACM Publications Board by Dr. Kishore Papineni of IBM' s T.J. Watson Research Center. Dr. Papineni is proposing a new journal, tentatively titled " Transactions on Speech and Language Processing. " I think that SIGART should give strong support to his proposal. The proposed journal will focus on the theory, design, development, and evaluation of practical systems that process human language in text or spoken form. The scope is intentionally broad: A subset of possible topics for papers includes natural language understanding, natural language generation, machine translation, document summarization, question-answer systems , speech recognition, speech generation, and audio indexing. Submitted papers will be subjected to reviewing standards typical for ACM journals. About 20 high-quality papers will be published per year, starting in 2003. Because of its broad coverage and its emphasis on practical methods and systems, the proposed journal differs significantly from existing journals such as " Computational Linguistics, " " Natural Language Engineering, " " Machine Translation, " " Computer Speech and Language, " IEEE' s " Transactions on Speech and Audio Processing. " And of course the new journal would carry the ACM imprimatur, which is useful to authors and readers alike as an implicit indicator of quality. If you have any thoughts on Dr. Papineni's proposal, I encourage you to share them with him (papineni@us.ibm.com) and with me (marks@merl.com). In future letters I will summarize any comments received and keep you apprised of future developments regarding the proposed journal. P.S. Chris Welty, the Editor-in-Chief of our magazine, lost a brother in the September 11th attack. Timothy Welty was a member of the New York City Fire Department and was in the World Trade Center when it collapsed. We offer our support and sympathy to Chris in these difficult days.
目前的ACM期刊并没有为人工智能研究提供太多的出口。因此，任何在人工智能领域创办ACM期刊的提议都值得认真考虑。IBM T.J. Watson研究中心的Kishore Papineni博士即将向ACM出版委员会提出这样的建议。帕皮尼尼博士提议创办一份新期刊，暂定名为《语音和语言处理学报》。我认为SIGART应该大力支持他的建议。拟建的期刊将侧重于理论、设计、发展和评估以文本或口语形式处理人类语言的实用系统。范围是有意广泛的:论文的可能主题的一个子集包括自然语言理解，自然语言生成，机器翻译，文档摘要，问答系统，语音识别，语音生成和音频索引。提交的论文将受到评审标准典型的ACM期刊。从2003年开始，每年将发表大约20篇高质量的论文。由于其广泛的覆盖范围和对实用方法和系统的强调，拟议的期刊与现有的期刊如“计算语言学”，“自然语言工程”，“机器翻译”，“计算机语音和语言”，IEEE的“语音和音频处理交易”有很大的不同。当然，新期刊将带有ACM的授权，这对作者和读者都很有用，因为这是质量的隐含指标。如果您对Papineni博士的建议有任何想法，我鼓励您与他(papineni@us.ibm.com)和我(marks@merl.com)分享。在以后的信件中，我将总结收到的任何意见，并随时向您通报拟议期刊的未来发展。附言:克里斯·韦尔蒂，我们杂志的主编，在911袭击中失去了一个兄弟。蒂莫西·韦尔蒂是纽约市消防局的一名成员，世贸中心倒塌时他就在里面。在这艰难的日子里，我们向克里斯表示支持和同情。
{"title":"Letter from the chair","authors":"J. Marks","doi":"10.1145/504313.504315","DOIUrl":"https://doi.org/10.1145/504313.504315","url":null,"abstract":"The current slate of ACM journals does not provide much outlet for AI research. So any proposal to start an ACM journal in an area of AI merits serious consideration. Just such a proposal is about to be made to the ACM Publications Board by Dr. Kishore Papineni of IBM' s T.J. Watson Research Center. Dr. Papineni is proposing a new journal, tentatively titled \" Transactions on Speech and Language Processing. \" I think that SIGART should give strong support to his proposal. The proposed journal will focus on the theory, design, development, and evaluation of practical systems that process human language in text or spoken form. The scope is intentionally broad: A subset of possible topics for papers includes natural language understanding, natural language generation, machine translation, document summarization, question-answer systems , speech recognition, speech generation, and audio indexing. Submitted papers will be subjected to reviewing standards typical for ACM journals. About 20 high-quality papers will be published per year, starting in 2003. Because of its broad coverage and its emphasis on practical methods and systems, the proposed journal differs significantly from existing journals such as \" Computational Linguistics, \" \" Natural Language Engineering, \" \" Machine Translation, \" \" Computer Speech and Language, \" IEEE' s \" Transactions on Speech and Audio Processing. \" And of course the new journal would carry the ACM imprimatur, which is useful to authors and readers alike as an implicit indicator of quality. If you have any thoughts on Dr. Papineni's proposal, I encourage you to share them with him (papineni@us.ibm.com) and with me (marks@merl.com). In future letters I will summarize any comments received and keep you apprised of future developments regarding the proposed journal. P.S. Chris Welty, the Editor-in-Chief of our magazine, lost a brother in the September 11th attack. Timothy Welty was a member of the New York City Fire Department and was in the World Trade Center when it collapsed. We offer our support and sympathy to Chris in these difficult days.","PeriodicalId":8272,"journal":{"name":"Appl. Intell.","volume":"310 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2001-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79956258","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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