Page 1. A Computational Framework for Emotion-Based Control Juan D. Velásquez MIT Artificial Inte... more Page 1. A Computational Framework for Emotion-Based Control Juan D. Velásquez MIT Artificial Intelligence Laboratory 545 Technology Square, NE43-935 Cambridge, Massachusetts 02139 jvelas@ai.mit.edu Abstract This ...
During emergencies, decision making is a challenging task requiring immediate and effective actio... more During emergencies, decision making is a challenging task requiring immediate and effective action from responders under the pressures of incomplete and erroneous information. Identification of appropriate resources and personnel, proper lines of communication, and timely accessibility to relevant procedures can minimize after effects. To achieve emergency response and recovery effectiveness, responders need to be prepared and trained for various emergency situations and decision support systems. To address some of the decision making needs experienced by responders, a low-cost computer computer-based training prototype with a decision support system tool was developed. The emergency training prototype was designed for the Indiana Department of Transportation. Emergency responders' capabilities, collaboration with other agencies, deployment of resources and personnel, implementation of response plans, and use of the chain of command were evaluated. The usefulness of prototype and potential decision making systems for the transportation agency are validated based on several mock drills.
By applying web mining tools, significant patterns about the visitor behavior can be extracted fr... more By applying web mining tools, significant patterns about the visitor behavior can be extracted from data originated in web sites. Supported by a domain expert, the patterns are validated or rejected and rules about how to use the patterns are created. This results in discovering new knowledge about the visitor behavior to the web site. But, due to frequent changes in the visitor's interests, as well as in the web site itself, the discovered knowledge may become obsolete in a short period of time. In this paper, we introduce a Knowledge Base (KB), which consists of a database-type repository for maintaining the patterns, and rules, as an independent program that consults the pattern repository. Using the proposed architecture, an artificial system or a human user can consult the KB in order to improve the relation between the web site and its visitors. The proposed structure was tested using data from a Chilean virtual bank, which proved the effectiveness of our approach.
International Conference on Intelligent RObots and Systems - IROS, 1999
Page 1. Proceedings of the 1999 IEEVRSJ International Conference on Intelligent Robots and System... more Page 1. Proceedings of the 1999 IEEVRSJ International Conference on Intelligent Robots and Systems An Emotion-Based Approach to Robotics Juan D. Velasquez MIT Artificial Intelligence Laboratory 545 Technology Square ...
... Acknowledgments Work on the Yuppy robot is carried out in conjunction with Professor Rodney B... more ... Acknowledgments Work on the Yuppy robot is carried out in conjunction with Professor Rodney Brooks, Charles Kemp, and Makoto Yoshida. References Arafa, Y., et al. 1998. Designing and Building PSAs with Personality. ...
Page 1. 1549 Collaborative 88. Collaborative e-Work, e-Business, and e-Service Juan D. Velásquez,... more Page 1. 1549 Collaborative 88. Collaborative e-Work, e-Business, and e-Service Juan D. Velásquez, Shimon Y. Nof A major part of automation today is repre-sented by collaborative e-Work, e-Business, and e-Service. In this ...
ABSTRACT This chapter contains automation information collected from many different sources aroun... more ABSTRACT This chapter contains automation information collected from many different sources around the world, among them the US Census Bureau, the International Federation of Automatic Control (IFAC), the American Automatic Control Council (AACC), the American Bankers Association, World Robotics, and many more. Section 1 introduces automation statistical data according to its application domain, such as: financial and e-commerce, industrial, healthcare, and the service industries. In Section 2 a list of worldwide automation, control, and robotics associations is provided. Section 3 provides the reader with a list of automation labs around the world and lastly, in Section 4 a list of automation related journals and publications is presented. The statistics, organizations, and journals included in this chapter open a window into the current and emerging state of automation. While attempting to be comprehensive, because of the broadness of automation, the authors recognize that there may be additional relevant items, but none were omitted intentionally. The scope of the information is meant to highlight and expose the broad span, applications, concerns and benefits of automation, and clearly, cannot completely include all areas of automation influence. Beyond this chapter, other chapters in this handbook provide additional statistical data directly related to their specific topic. For the complete review see Zbl 1218.93004.
The information footprints of a rapidly increasing influx of Internet users present us with an im... more The information footprints of a rapidly increasing influx of Internet users present us with an immense source of information that ultimately contributes to the construction of innovative web technology suitable for the future generations. Likewise, Web Intelligence has been presented as the usage of advanced techniques in Artificial Intelligence and Information Technology for the purpose of exploring, analysing, and extracting knowledge from Web data. In this chapter, the use of Web Intelligence is discussed together with ways in which a wide range of research is benefiting this area for the long-term. Also the books' purpose and structure are introduced, together with all resources used in its construction.
End users leave traces of behavior all over the Web all times. From the explicit or implicit feed... more End users leave traces of behavior all over the Web all times. From the explicit or implicit feedback of a multimedia document or a comment in an online social network, to a simple click in a relevant link in a search engine result, the information that we as users pour into the Web defines its actual representation, which is independent for each user. Our usage can be represented by different sources of data, for which different collection strategies must be considered, as well as the merging and cleaning techniques for Web usage data. Once the data is properly preprocessed, the identification of an individual user within the Web can be a complex task. Understanding the whole life of a user within a session in a Web site and the path that was pursued involves advanced data modeling and a set of assumptions which are modified every day, as new ways to interact with the online content are created. The objective is to understand the behaviour and preferences of a web user, also when several privacy issues are involved, which, as of today, are not clear how to be properly addressed. In this chapter, all previous topics regarding the processing of Web usage data are extensively discussed.
Web Opinion Mining (WOM) is a new concept in Web Intelligence. It embraces the problem of extract... more Web Opinion Mining (WOM) is a new concept in Web Intelligence. It embraces the problem of extracting, analyzing and aggregating web data about opinions. Studying users' opinions is relevant because through them it is possible to determine how people feel about a product or service and know how it was received by the market. In this chapter, we show an overview about what Opinion Mining is and give some approaches about how to do it. Also, we distinguish and discuss four resources from where opinions can be extracted from, analyzing in each case the main issues that could alter the mining process. One last interesting topic related to WOM and discussed in this chapter is the summarization and visualization of the WOM results. We consider these techniques to be important because they offer a real chance to understand and find a real value for a huge set of heterogeneous opinions collected. Finally, having given enough conceptual background, a practical example is presented using Twitter as a platform for Web Opinion Mining. Results show how an opinion is spread through the network and describes how users influence each other.
Page 1. Modeling Emotion-Based Decision-Making Juan D. Velásquez MIT Artificial Intelligence Labo... more Page 1. Modeling Emotion-Based Decision-Making Juan D. Velásquez MIT Artificial Intelligence Laboratory 545 Technology Square, NE43-935 Cambridge, Massachusetts 02139 jvelas@ai.mit.edu Abstract This paper presents ...
Page 1. A Computational Framework for Emotion-Based Control Juan D. Velásquez MIT Artificial Inte... more Page 1. A Computational Framework for Emotion-Based Control Juan D. Velásquez MIT Artificial Intelligence Laboratory 545 Technology Square, NE43-935 Cambridge, Massachusetts 02139 jvelas@ai.mit.edu Abstract This ...
During emergencies, decision making is a challenging task requiring immediate and effective actio... more During emergencies, decision making is a challenging task requiring immediate and effective action from responders under the pressures of incomplete and erroneous information. Identification of appropriate resources and personnel, proper lines of communication, and timely accessibility to relevant procedures can minimize after effects. To achieve emergency response and recovery effectiveness, responders need to be prepared and trained for various emergency situations and decision support systems. To address some of the decision making needs experienced by responders, a low-cost computer computer-based training prototype with a decision support system tool was developed. The emergency training prototype was designed for the Indiana Department of Transportation. Emergency responders' capabilities, collaboration with other agencies, deployment of resources and personnel, implementation of response plans, and use of the chain of command were evaluated. The usefulness of prototype and potential decision making systems for the transportation agency are validated based on several mock drills.
By applying web mining tools, significant patterns about the visitor behavior can be extracted fr... more By applying web mining tools, significant patterns about the visitor behavior can be extracted from data originated in web sites. Supported by a domain expert, the patterns are validated or rejected and rules about how to use the patterns are created. This results in discovering new knowledge about the visitor behavior to the web site. But, due to frequent changes in the visitor's interests, as well as in the web site itself, the discovered knowledge may become obsolete in a short period of time. In this paper, we introduce a Knowledge Base (KB), which consists of a database-type repository for maintaining the patterns, and rules, as an independent program that consults the pattern repository. Using the proposed architecture, an artificial system or a human user can consult the KB in order to improve the relation between the web site and its visitors. The proposed structure was tested using data from a Chilean virtual bank, which proved the effectiveness of our approach.
International Conference on Intelligent RObots and Systems - IROS, 1999
Page 1. Proceedings of the 1999 IEEVRSJ International Conference on Intelligent Robots and System... more Page 1. Proceedings of the 1999 IEEVRSJ International Conference on Intelligent Robots and Systems An Emotion-Based Approach to Robotics Juan D. Velasquez MIT Artificial Intelligence Laboratory 545 Technology Square ...
... Acknowledgments Work on the Yuppy robot is carried out in conjunction with Professor Rodney B... more ... Acknowledgments Work on the Yuppy robot is carried out in conjunction with Professor Rodney Brooks, Charles Kemp, and Makoto Yoshida. References Arafa, Y., et al. 1998. Designing and Building PSAs with Personality. ...
Page 1. 1549 Collaborative 88. Collaborative e-Work, e-Business, and e-Service Juan D. Velásquez,... more Page 1. 1549 Collaborative 88. Collaborative e-Work, e-Business, and e-Service Juan D. Velásquez, Shimon Y. Nof A major part of automation today is repre-sented by collaborative e-Work, e-Business, and e-Service. In this ...
ABSTRACT This chapter contains automation information collected from many different sources aroun... more ABSTRACT This chapter contains automation information collected from many different sources around the world, among them the US Census Bureau, the International Federation of Automatic Control (IFAC), the American Automatic Control Council (AACC), the American Bankers Association, World Robotics, and many more. Section 1 introduces automation statistical data according to its application domain, such as: financial and e-commerce, industrial, healthcare, and the service industries. In Section 2 a list of worldwide automation, control, and robotics associations is provided. Section 3 provides the reader with a list of automation labs around the world and lastly, in Section 4 a list of automation related journals and publications is presented. The statistics, organizations, and journals included in this chapter open a window into the current and emerging state of automation. While attempting to be comprehensive, because of the broadness of automation, the authors recognize that there may be additional relevant items, but none were omitted intentionally. The scope of the information is meant to highlight and expose the broad span, applications, concerns and benefits of automation, and clearly, cannot completely include all areas of automation influence. Beyond this chapter, other chapters in this handbook provide additional statistical data directly related to their specific topic. For the complete review see Zbl 1218.93004.
The information footprints of a rapidly increasing influx of Internet users present us with an im... more The information footprints of a rapidly increasing influx of Internet users present us with an immense source of information that ultimately contributes to the construction of innovative web technology suitable for the future generations. Likewise, Web Intelligence has been presented as the usage of advanced techniques in Artificial Intelligence and Information Technology for the purpose of exploring, analysing, and extracting knowledge from Web data. In this chapter, the use of Web Intelligence is discussed together with ways in which a wide range of research is benefiting this area for the long-term. Also the books' purpose and structure are introduced, together with all resources used in its construction.
End users leave traces of behavior all over the Web all times. From the explicit or implicit feed... more End users leave traces of behavior all over the Web all times. From the explicit or implicit feedback of a multimedia document or a comment in an online social network, to a simple click in a relevant link in a search engine result, the information that we as users pour into the Web defines its actual representation, which is independent for each user. Our usage can be represented by different sources of data, for which different collection strategies must be considered, as well as the merging and cleaning techniques for Web usage data. Once the data is properly preprocessed, the identification of an individual user within the Web can be a complex task. Understanding the whole life of a user within a session in a Web site and the path that was pursued involves advanced data modeling and a set of assumptions which are modified every day, as new ways to interact with the online content are created. The objective is to understand the behaviour and preferences of a web user, also when several privacy issues are involved, which, as of today, are not clear how to be properly addressed. In this chapter, all previous topics regarding the processing of Web usage data are extensively discussed.
Web Opinion Mining (WOM) is a new concept in Web Intelligence. It embraces the problem of extract... more Web Opinion Mining (WOM) is a new concept in Web Intelligence. It embraces the problem of extracting, analyzing and aggregating web data about opinions. Studying users' opinions is relevant because through them it is possible to determine how people feel about a product or service and know how it was received by the market. In this chapter, we show an overview about what Opinion Mining is and give some approaches about how to do it. Also, we distinguish and discuss four resources from where opinions can be extracted from, analyzing in each case the main issues that could alter the mining process. One last interesting topic related to WOM and discussed in this chapter is the summarization and visualization of the WOM results. We consider these techniques to be important because they offer a real chance to understand and find a real value for a huge set of heterogeneous opinions collected. Finally, having given enough conceptual background, a practical example is presented using Twitter as a platform for Web Opinion Mining. Results show how an opinion is spread through the network and describes how users influence each other.
Page 1. Modeling Emotion-Based Decision-Making Juan D. Velásquez MIT Artificial Intelligence Labo... more Page 1. Modeling Emotion-Based Decision-Making Juan D. Velásquez MIT Artificial Intelligence Laboratory 545 Technology Square, NE43-935 Cambridge, Massachusetts 02139 jvelas@ai.mit.edu Abstract This paper presents ...
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