Pub Date : 2012-07-22DOI: 10.1609/aaai.v26i2.18963
S. Kelling, Jeff Gerbracht, D. Fink, C. Lagoze, Weng-Keen Wong, Jun Yu, T. Damoulas, C. Gomes
In this paper we describe eBird, a citizen science project that takes advantage of human observational capacity and machine learning methods to explore the synergies between human computation and mechanical computation. We call this model a Human/Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. Human/Computer Learning Networks leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.
{"title":"eBird: A Human/Computer Learning Network for Biodiversity Conservation and Research","authors":"S. Kelling, Jeff Gerbracht, D. Fink, C. Lagoze, Weng-Keen Wong, Jun Yu, T. Damoulas, C. Gomes","doi":"10.1609/aaai.v26i2.18963","DOIUrl":"https://doi.org/10.1609/aaai.v26i2.18963","url":null,"abstract":"\u0000 \u0000 \u0000In this paper we describe eBird, a citizen science project that takes advantage of human observational capacity and machine learning methods to explore the synergies between human computation and mechanical computation. We call this model a Human/Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. Human/Computer Learning Networks leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts. \u0000 \u0000 \u0000","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121672362","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}
Pub Date : 2012-07-22DOI: 10.1609/aaai.v26i2.18962
Tatsuya Kawahara
This article describes a new automatic transcription system in the Japanese Parliament which deploys our automatic speech recognition (ASR) technology. To achieve high recognition performance in spontaneous meeting speech, we have investigated an efficient training scheme with minimal supervision which can exploit a huge amount of real data. Specifically, we have proposed a lightly-supervised training scheme based on statistical language model transformation, which fills the gap between faithful transcripts of spoken utterances and final texts for documentation. Once this mapping is trained, we no longer need faithful transcripts for training both acoustic and language models. Instead, we can fully exploit the speech and text data available in Parliament as they are. This scheme also realizes a sustainable ASR system which evolves, i.e. update/re-train the models, only with speech and text generated during the system operation. The ASR system has been deployed in the Japanese Parliament since 2010, and consistently achieved character accuracy of nearly 90%, which is useful for streamlining the transcription process.
{"title":"Transcription System Using Automatic Speech Recognition for the Japanese Parliament (Diet)","authors":"Tatsuya Kawahara","doi":"10.1609/aaai.v26i2.18962","DOIUrl":"https://doi.org/10.1609/aaai.v26i2.18962","url":null,"abstract":"This article describes a new automatic transcription system in the Japanese Parliament which deploys our automatic speech recognition (ASR) technology. To achieve high recognition performance in spontaneous meeting speech, we have investigated an efficient training scheme with minimal supervision which can exploit a huge amount of real data. Specifically, we have proposed a lightly-supervised training scheme based on statistical language model transformation, which fills the gap between faithful transcripts of spoken utterances and final texts for documentation. Once this mapping is trained, we no longer need faithful transcripts for training both acoustic and language models. Instead, we can fully exploit the speech and text data available in Parliament as they are. This scheme also realizes a sustainable ASR system which evolves, i.e. update/re-train the models, only with speech and text generated during the system operation. The ASR system has been deployed in the Japanese Parliament since 2010, and consistently achieved character accuracy of nearly 90%, which is useful for streamlining the transcription process.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121933524","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}
Pub Date : 2012-07-22DOI: 10.1609/aaai.v26i2.18958
T. Drew, Maria L. Gini
This case study concerns the use of software agent advisors to improve efficiency and quality in issue tracking activities of development teams at the world’s largest medical device manufacturer. Each software agent monitors, interacts with, and learns from its environment and user, recognizing when and how to provide different kinds of advice and support to facilitate issue tracking activities without directly modifying anything or otherwise violating domain constraints. The deployed software agent has not only enjoyed regular and growing use, but contributed to significant improvements. Issue rejection was significantly reduced and more focused, yielding significant quality and efficiency gains such as fewer reviews by quality assurance. This success reflects the benefits of the underlying AI technology.
{"title":"Advisor Agent Support for Issue Tracking in Medical Device Development","authors":"T. Drew, Maria L. Gini","doi":"10.1609/aaai.v26i2.18958","DOIUrl":"https://doi.org/10.1609/aaai.v26i2.18958","url":null,"abstract":"This case study concerns the use of software agent advisors to improve efficiency and quality in issue tracking activities of development teams at the world’s largest medical device manufacturer. Each software agent monitors, interacts with, and learns from its environment and user, recognizing when and how to provide different kinds of advice and support to facilitate issue tracking activities without directly modifying anything or otherwise violating domain constraints. The deployed software agent has not only enjoyed regular and growing use, but contributed to significant improvements. Issue rejection was significantly reduced and more focused, yielding significant quality and efficiency gains such as fewer reviews by quality assurance. This success reflects the benefits of the underlying AI technology.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126901582","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}
Pub Date : 2011-08-04DOI: 10.1609/aaai.v25i2.18847
M. Stefik, L. Good
With the growth of online information, many people are challenged in finding and reading the information most important for their interests. From 2008-2010 we built an experimental personalized news system where readers can subscribe to organized channels of information that are curated by experts. AI technology was employed to radically reduce the work load of curators and to efficiently present information to readers. The system has gone through three implementation cycles and processed over 16 million news stories from about 12,000 RSS feeds on over 8000 topics organized by 160 curators for over 600 registered readers. This paper describes the approach, engineering and AI technology of the system.
{"title":"The News that Matters to You: Design and Deployment of a Personalized News Service","authors":"M. Stefik, L. Good","doi":"10.1609/aaai.v25i2.18847","DOIUrl":"https://doi.org/10.1609/aaai.v25i2.18847","url":null,"abstract":"\u0000 \u0000 \u0000With the growth of online information, many people are challenged in finding and reading the information most important for their interests. From 2008-2010 we built an experimental personalized news system where readers can subscribe to organized channels of information that are curated by experts. AI technology was employed to radically reduce the work load of curators and to efficiently present information to readers. The system has gone through three implementation cycles and processed over 16 million news stories from about 12,000 RSS feeds on over 8000 topics organized by 160 curators for over 600 registered readers. This paper describes the approach, engineering and AI technology of the system. \u0000 \u0000 \u0000","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114767112","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}
Pub Date : 2011-08-04DOI: 10.1609/aaai.v25i2.18860
Steven Minton, Sofus A. Macskassy, P. LaMonica, Kane See, Craig A. Knoblock, Greg Barish, M. Michelson, R. Liuzzi
This paper describes a system to help intelligence analysts track and analyze information being published in multiple sources, particularly open sources on the Web. The system integrates technology for Web harvesting, natural language extraction, and network analytics, and allows analysts to view and explore the results via a Web application. One of the difficult problems we address is the entity resolution problem, which occurs when there are multiple, differing ways to refer to the same entity. The problem is particularly complex when noisy data is being aggregated over time, there is no clean master list of entities, and the entities under investigation are intentionally being deceptive. Our system must not only perform entity resolution with noisy data, but must also gracefully recover when entity resolution mistakes are subsequently corrected. We present a case study in arms trafficking that illustrates the issues, and describe how they are addressed.
{"title":"Monitoring Entities in an Uncertain World: Entity Resolution and Referential Integrity","authors":"Steven Minton, Sofus A. Macskassy, P. LaMonica, Kane See, Craig A. Knoblock, Greg Barish, M. Michelson, R. Liuzzi","doi":"10.1609/aaai.v25i2.18860","DOIUrl":"https://doi.org/10.1609/aaai.v25i2.18860","url":null,"abstract":"\u0000 \u0000 \u0000This paper describes a system to help intelligence analysts track and analyze information being published in multiple sources, particularly open sources on the Web. The system integrates technology for Web harvesting, natural language extraction, and network analytics, and allows analysts to view and explore the results via a Web application. One of the difficult problems we address is the entity resolution problem, which occurs when there are multiple, differing ways to refer to the same entity. The problem is particularly complex when noisy data is being aggregated over time, there is no clean master list of entities, and the entities under investigation are intentionally being deceptive. Our system must not only perform entity resolution with noisy data, but must also gracefully recover when entity resolution mistakes are subsequently corrected. We present a case study in arms trafficking that illustrates the issues, and describe how they are addressed. \u0000 \u0000 \u0000","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133550539","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}
Pub Date : 2010-07-05DOI: 10.1609/aaai.v24i2.18808
Karsten Bsufka, Rainer Bye, Joël Chinnow, Stephan Schmidt, L. Batyuk
Network management is a complex task involving various challenges, such as the heterogeneity of the infrastructure or the information flood caused by billions of log messages from different systems and operated by different organizational units. All of these messages and systems may contain information relevant to other operational units. For example, in order to ensure reliable DSL connections for IPTV customers, optimal customer traffic path assignments for the current network state and traffic demands need to be evaluated. Currently reassignments are only manually performed during routine maintenance or as a response to reported problems. In this paper we present a decision support system for this task. In addition, the system predicts future possible demands and allows reconfigurations of a DSL access network before congestions may occur.
{"title":"Agent-Based Decision Support: A Case-Study on DSL Access Networks","authors":"Karsten Bsufka, Rainer Bye, Joël Chinnow, Stephan Schmidt, L. Batyuk","doi":"10.1609/aaai.v24i2.18808","DOIUrl":"https://doi.org/10.1609/aaai.v24i2.18808","url":null,"abstract":"Network management is a complex task involving various challenges, such as the heterogeneity of the infrastructure or the information flood caused by billions of log messages from different systems and operated by different organizational units. All of these messages and systems may contain information relevant to other operational units. For example, in order to ensure reliable DSL connections for IPTV customers, optimal customer traffic path assignments for the current network state and traffic demands need to be evaluated. Currently reassignments are only manually performed during routine maintenance or as a response to reported problems. In this paper we present a decision support system for this task. In addition, the system predicts future possible demands and allows reconfigurations of a DSL access network before congestions may occur. \u0000 ","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127203445","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}
Pub Date : 2010-07-05DOI: 10.1609/aaai.v24i2.18816
N. Khandaker, Leen-Kiat Soh
Wikis are being increasingly used as a tool for conducting colla-borative writing assignments in today’s classrooms. However, Wikis in general (1) do not provide group formation methods to more specifically facilitate collaborative learning of the students and (2) suffer from typical problems of collaborative learning like detection of free-riding (earning credit without contribution). To improve the state of the art of the use of Wikis as a collaborative writing tool, we have designed and implemented ClassroomWiki - a Web-based collaborative Wiki that utilizes a set of learner pedagogy theories to provide multiagent-based tracking, modeling, and group formation functionalities. For the students, ClassroomWiki provides a Web interface for writing and revising their group’s Wiki and a topic-based forum for discussing their ideas during collaboration. When the students collaborate, ClassroomWiki’s agents track all student activities to learn a model of the students and use a Bayesian Network to learn a probabilistic mapping that describes the ability of a group of students with a specific set of models to work together. For the teacher, Clas-sroomWiki provides a framework that uses the learned student models and the mapping to form student groups to improve the collaborative learning of students. ClassroomWiki was deployed in three university-level courses and the results suggest that ClassroomWiki can (1) form better student groups that improve stu-dent learning and collaboration and (2) alleviate free-riding and allow the instructor to provide scaffolding by its multiagent-based tracking and modeling.
{"title":"A Wiki with Multiagent Tracking, Modeling, and Coalition Formation","authors":"N. Khandaker, Leen-Kiat Soh","doi":"10.1609/aaai.v24i2.18816","DOIUrl":"https://doi.org/10.1609/aaai.v24i2.18816","url":null,"abstract":"Wikis are being increasingly used as a tool for conducting colla-borative writing assignments in today’s classrooms. However, Wikis in general (1) do not provide group formation methods to more specifically facilitate collaborative learning of the students and (2) suffer from typical problems of collaborative learning like detection of free-riding (earning credit without contribution). To improve the state of the art of the use of Wikis as a collaborative writing tool, we have designed and implemented ClassroomWiki - a Web-based collaborative Wiki that utilizes a set of learner pedagogy theories to provide multiagent-based tracking, modeling, and group formation functionalities. For the students, ClassroomWiki provides a Web interface for writing and revising their group’s Wiki and a topic-based forum for discussing their ideas during collaboration. When the students collaborate, ClassroomWiki’s agents track all student activities to learn a model of the students and use a Bayesian Network to learn a probabilistic mapping that describes the ability of a group of students with a specific set of models to work together. For the teacher, Clas-sroomWiki provides a framework that uses the learned student models and the mapping to form student groups to improve the collaborative learning of students. ClassroomWiki was deployed in three university-level courses and the results suggest that ClassroomWiki can (1) form better student groups that improve stu-dent learning and collaboration and (2) alleviate free-riding and allow the instructor to provide scaffolding by its multiagent-based tracking and modeling.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123220039","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}
In recent years, the study of trading in electronic markets has received significant amount of attention, particularly in the areas of artificial intelligence and electronic commerce. With increasingly sophisticated technologies being applied in analyzing information and making decisions, fully autonomous software agents are expected to take up significant roles in many important fields. This trend is most obvious in the financial domain, where speed of reaction is highly valued and significant investments have been made in information and communication technologies.
{"title":"An Agent-based Commodity Trading Simulation","authors":"Shih-Fen Cheng, Yee Pin Lim","doi":"10.1145/1558109.1558303","DOIUrl":"https://doi.org/10.1145/1558109.1558303","url":null,"abstract":"In recent years, the study of trading in electronic markets has received significant amount of attention, particularly in the areas of artificial intelligence and electronic commerce. With increasingly sophisticated technologies being applied in analyzing information and making decisions, fully autonomous software agents are expected to take up significant roles in many important fields. This trend is most obvious in the financial domain, where speed of reaction is highly valued and significant investments have been made in information and communication technologies.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132079943","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}
R. Hill, J. Douglas, A. Gordon, Frédéric H. Pighin, Martin Van Velsen
Think Like a Commander ‐ Excellence in Leadership (TLAC-XL) is an application designed for learning leadership skills both from the experiences of others and through a structured dialogue about issues raised in a vignette. The participant watches a movie, interacts with a synthetic mentor and interviews characters in the story. The goal is to enable leaders to learn the human dimensions of leadership, addressing a gap in the training tools currently available to the U.S. Army. The TLAC-XL application employs a number of Artificial Intelligence technologies, including the use of a coordination architecture, a machine learning approach to natural language processing, and an algorithm for the automated animation of rendered human faces.
{"title":"Guided Conversations about Leadership: Mentoring with Movies and Interactive Characters","authors":"R. Hill, J. Douglas, A. Gordon, Frédéric H. Pighin, Martin Van Velsen","doi":"10.21236/ada460365","DOIUrl":"https://doi.org/10.21236/ada460365","url":null,"abstract":"Think Like a Commander ‐ Excellence in Leadership (TLAC-XL) is an application designed for learning leadership skills both from the experiences of others and through a structured dialogue about issues raised in a vignette. The participant watches a movie, interacts with a synthetic mentor and interviews characters in the story. The goal is to enable leaders to learn the human dimensions of leadership, addressing a gap in the training tools currently available to the U.S. Army. The TLAC-XL application employs a number of Artificial Intelligence technologies, including the use of a coordination architecture, a machine learning approach to natural language processing, and an algorithm for the automated animation of rendered human faces.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126456234","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}
Pub Date : 1900-01-01DOI: 10.1007/978-3-030-58292-0_190019
W. C. Branley, Earnest D. Harris
{"title":"Sabre","authors":"W. C. Branley, Earnest D. Harris","doi":"10.1007/978-3-030-58292-0_190019","DOIUrl":"https://doi.org/10.1007/978-3-030-58292-0_190019","url":null,"abstract":"","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126437436","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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