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AAAI 2008 workshop reports

AAAI 2008 workshop reports

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作者： Anand, Sarabjot Singh Bunescu, Razvan Carvcdho, Vitor Chomicki, Jan Conitzer, Vincent Cox, Michael T. Dignum, Virginia Dodds, Zachary Dredze, Mark Furcy, David Gabrilovich, Evgeniy Göker, Mehmet H. Guesgen, Hans Hirsh, Haym Jannach, Dietmar Junker, Ulrich Ketter, Wolfgang Kobsa, Alfred Koenig, Sven Lau, Tessa Lewis, Lundy Matson, Eric Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Pineau, Joelle Poupart, Pascal Raja, Anita Ruml, Wheeler Sadeh, Norman Shani, Guy Shapiro, Daniel Smith, Trey Taylor, Matthew E. Wagstaff, Kiri Walsh, William Zhou, Rong Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering and Computer Science Ohio University United States Microsoft Live Labs United States computer science and engineering University at Buffalo United States Computer science and economics Duke University United States Intelligent Computing group of BBN Technologies Utrecht University Netherlands Department of Computer science Harvey Mudd College United States University of Pennsylvania United States University of Wisconsin United States Yahoo Research PricewaterhouseCoopers Center for Advanced Research United States Department of Computer science School of Engineering and Advanced Technology Massey University New Zealand Departent of Computer science Rutgers University United States Department of Computer Science Dortmund University of Technology. Germany ILOG Rotterdam School of Management Erasmus University Netherlands Donald Bren School of Information and Computer Sciences University of California Irvine United States Depatrment of Computer science University of Southern California United States IBM Almaden Research Center United States Department of Computer Information Technology Southern New Hampshire University United States Computer and Information Technology Program College of Technology Purdue University United States Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computer Science and Engineering University of North Texas United States School of Computing DePaul University United States Department of Computer Science McGill University Canada School of Computer Science University of Waterloo Canada Department of Software and Information Systems University of North Carolina Charlotte United States University of New Hampshire United States Microsoft Research Institute for the Study of Learning and Expertise Applied Reactivity Inc. United States NASA Ames Research Center Carnegie Mellon University West

AAAI was pleased to present the AAAI-08 Workshop program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers;AI Education Workshop Colloquium;Coordination, Organizations, Institutions, and Norms in Agent systems, Enhanced Messaging;Human Implications of Human-Robot Interaction;intelligent Techniques for Web Personalization and Recommender systems;Metareasoning: Thinking about Thinking;Multidisciplinary Workshop on Advances in Preference Handling;Search in Artificial Intelligence and Robotics;Spatial and Temporal Reasoning;Trading Agent Design and Analysis;Transfer Learning for Complex Tasks;What Went Wrong and Why: Lessons from AI Research and Applications;and Wikipedia and Artificial Intelligence: An Evolving Synergy. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Improving biomedical document retrieval using domain knowledge

Improving biomedical document retrieval using domain knowled...

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31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM SIGIR 2008

作者： Shuguang, Wang Hauskrecht, Milos Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Department of Computer Science University of Pittsburgh Pittsburgh PA 15260 United States

ISBN: (纸本)9781605581644

When research articles introduce new results or findings they typically relate them only to knowledge entities of immediate relevance. However, a large body of context knowledge related to the results is often not explicitly mentioned in the article. To overcome this limitation the state-of-the-art information retrieval approaches rely on the latent semantic analysis in which terms in articles are projected to a lower dimensional latent space and best possible matches in this space are identified. However, this approach may not perform well enough if the number of explicit knowledge entities in the articles is too small compared to the amount of knowledge in the domain. We address the problem by exploiting a domain knowledge layer, a rich network of relations among knowledge entities in the domain extracted from a large corpus of documents. The knowledge layer supplies the context knowledge that lets us relate different knowledge entities and hence improve the information retrieval performance. We develop and study a new framework for i) learning and aggregating the relations in the knowledge layer from the literature corpus;ii) and for exploiting these relations to improve the information-retrieval of relevant documents. We demonstrate the benefit of the method on biomedical text retrievals.

关键词： Information retrieval

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Preface

引用

CEUR Workshop Proceedings 2009年 467卷

作者： Cena, Federica Farzan, Rosta Lops, Pasquale Department of Computer Science University of Turin Corso Svizzera 185 10149 Torino Italy Intelligent Systems Program University of Pittsburgh 5108 Sennott Square Building 210 S. Bouquet St. Pittsburgh United States Department of Computer Science University of Bari Via E. Orabona 4 70126 Bari Italy

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Discourse level opinion interpretation

Discourse level opinion interpretation

引用

22nd International Conference on Computational Linguistics, Coling 2008

作者： Somasundaran, Swapna Wiebe, Janyce Ruppenhofer, Josef Dept. of Computer Science University of Pittsburgh Pittsburgh PA 15260 United States Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States

ISBN: (纸本)9781905593446

This work proposes opinion frames as a representation of discourse-level associations which arise from related opinion topics. We illustrate how opinion frames help gather more information and also assist disambiguation. Finally we present the results of our experiments to detect these associations. © 2008. Licensed under the Creative Commons.

关键词： Computational linguistics

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Discourse level opinion relations: An annotation study 9

Discourse level opinion relations: An annotation study

引用

9th SIGdial Workshop on Discourse and Dialogue

作者： Somasundaran, Swapna Ruppenhofer, Josef Wiebe, Janyce Dept. of Computer Science University of Pittsburgh Pittsburgh PA 15260 United States Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States

ISBN: (纸本)9781932432176

This work proposes opinion frames as a representation of discourse-level associations that arise from related opinion targets and which are common in task-oriented meeting dialogs. We define the opinion frames and explain their interpretation. Additionally we present an annotation scheme that realizes the opinion frames and via human annotation studies, we show that these can be reliably identified. © 2008 Association for Computational Linguistics.

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Insensitivity of constraint-based causal discovery algorithms to violations of the assumption of multivariate normality

Insensitivity of constraint-based causal discovery algorithm...

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21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21

作者： Voortman, Mark Druzdzel, Marek J. Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Faculty of Computer Science Bialystok Technical University Wiejska 45A 15-351 Bialystok Poland

ISBN: (纸本)9781577353652

Constraint-based causal discovery algorithms, such as the PC algorithm, rely on conditional independence tests and are otherwise independent of the actual distribution of the data. In case of continuous variables, the most popular conditional independence test used in practice is the partial correlation test, applicable to variables that are multivariate Normal. Many researchers assume multivariate Normal distributions when dealing with continuous variables, based on a common wisdom that minor departures from multivariate Normality do not have a too strong effect on the reliability of partial correlation tests. We subject this common wisdom to a test in the context of causal discovery and show that partial correlation tests are indeed quite insensitive to departures from multivariate Normality. They, therefore, provide conditional independence tests that are applicable to a wide range of multivariate continuous distributions. Copyright © 2008, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Normal distribution

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Argument graph classification via genetic programming and C4.5

Argument graph classification via genetic programming and C4...

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1st International Conference on Educational Data Mining, EDM 2008

作者： Lynch, Collin Ashley, Kevin D. Pinkwart, Niels Aleven, Vincent Intelligent Systems Program University of Pittsburgh United States School of Law University of Pittsburgh United States Department of Informatics Clausthal University of Technology Germany HCII School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9780615306292

In well-defined domains there exist well-accepted criteria for detecting good and bad student solutions. Many ITS implement these criteria characterize solutions and to give immediate feedback. While this has been shown to promote learning, it is not always possible in ill-defined domains that typically lack well-accepted criteria. In this paper we report on the induction of classification rules for student solutions in an ill-defined domain. 1 We compare the viability of classifications using statistical measures with classification trees induced via C4.5 and Genetic programming.

关键词： Genetic programming

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The Third International Conference on human-robot interaction

The Third International Conference on human-robot interactio...

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作者： Fong, Terry Dautenhahn, Kerstin Scheutz, Matthias Demiris, Yiannis Intelligent Robotics Group NASA Ames Research Center Department of Artificial Intelligence School of Computer Science University of Herfordshire Herfordshire United Kingdom Adaptive Systems Research Group University of Herfordshire Herfordshire United Kingdom Department of Cognitive Science Computer Science and Informatics Cognitive Science Program Indiana University Bloomington IN United States Intelligent Robotics Team Department of Electrical Engineering Imperial College London London United Kingdom

The third international conference on Human-Robot Interaction (HRI-2008) was held in Amsterdam, The Netherlands, March 12-15, 2008. The theme of HRI-2008, "living with robots," highlights the importance of the technical and social issues underlying human-robot interaction with companion and assistive robots for long-term use in everyday life and work activities. More than 250 researchers, practitioners, and exhibitors attended the conference, and many more contributed to the conference as authors or reviewers. HRI-2009 will be held in San Diego, California, from March 11-13, 2009. Copyright © 2008, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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The impact of overconfidence bias on practical accuracy of Bayesian network models: An empirical study

The impact of overconfidence bias on practical accuracy of B...

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6th UAI Bayesian Modelling Applications Workshop, BMAW 2008

作者： Druzdzel, Marek J. Oniśko, Agnieszka Faculty of Computer Science Bialystok Technical University 15-351 Bialystok Wiejska 45A Poland Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Magee Womens Hospital University of Pittsburgh Medical Center Pittsburgh PA 15260 United States

In this paper, we examine the influence of overconfidence in parameter specification on the performance of a Bayesian network model in the context of Hepar II, a sizeable Bayesian network model for diagnosis of liver disorders. We enter noise in the parameters in such a way that the resulting distributions become biased toward extreme probabilities. We believe that this offers a systematic way of modeling expert overconfidence in probability estimates. It appears that the diagnostic accuracy of Hepar II is less sensitive to overconfidence in probabilities than it is to underconfidence and to random noise, especially when noise is very large.

关键词： Bayesian networks

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Localization of difficult-to-translate phrases 2

Localization of difficult-to-translate phrases

引用

2nd Workshop on Statistical Machine Translation, WMT 2007 at the 45th Annual Meeting of the Association of Computational Linguistics, ACL 2007

作者： Mohit, Behrang Hwa, Rebecca Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Computer Science University of Pittsburgh PittsburghPA15260 United States

This paper studies the impact that difficult-to-translate source-language phrases might have on the machine translation process. We formulate the notion of difficulty as a measurable quantity;we show that a classifier can be trained to predict whether a phrase might be difficult to translate;and we develop a framework that makes use of the classifier and external resources (such as human translators) to improve the overall translation quality. Through experimental work, we verify that by isolating difficult-to-translate phrases and processing them as special cases, their negative impact on the translation of the rest of the sentences can be reduced. © 2007 Association for Computational Linguistics

关键词： Machine translation

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