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作者： Aha, David W. Boddy, Mark Bulitko, Vadim D'Avila Garcez, Artur S. Doshi, Prashant Edelkamp, Stefan Geib, Christopher Gmytrasiewicz, Piotr Goldman, Robert P. Halevy, Alon Hitzler, Pascal Isbell, Charles Josyula, Darsana Kaelbling, Leslie Pack Kersting, Kristian Kunda, Maithilee Lamb, Luis C. Marthi, Bhaskara McGreggor, Keith Mihalkova, Lilyana Nastase, Vivi Natarajan, Sriraam Provan, Gregory Raja, Anita Ram, Ashwin Riedl, Mark Russell, Stuart Sabharwal, Ashish Smaus, Jan-Georg Sukthankar, Gita Tuyls, Karl Van Der Meyden, Ron Adaptive Systems Section Naval Research Laboratory Washington DC United States Adventium Labs. Minneapolis MN United States Department of Computer Science University of Alberta Canada City University London United Kingdom University of Georgia United States University of Bremen Germany School of Informatics University of Edinburgh United Kingdom University of Illinois Chicago United States Smart Information Flow Technologies LLC Minneapolis MN United States Structured Data Group Google Research United States Department of Computer Science Wright State University Dayton OH United States School of Interactive Computing Georgia Institute of Technology Atlanta GA United States Department of Computer Science Bowie State University United States Department of Computer Science and Engineering Massachusetts Institute of Technology United States Statistical Relational Mining Group Fraunhofer IAIS Germany Department of Computer Science Federal University of Rio Grande Do sul Brazil Willow Garage Inc. CA United States Computer Science Department University of Maryland College Park United States Heidelberg Institute for Theoretical Studies Heidelberg Germany Department of Computer Science University of Wisconsin Madison United States University College Cork Ireland Cork Complex Systems Lab. Ireland Department of Software and Information Systems University of North Carolina Charlotte United States Department of Computer Science and Engineering University of California Berkeley United States Department of Computer Science Cornell University United States Institute for Computational Sustainability United States University of Freiburg Germany School of Electrical Engineering and Computer Science University of Central Florida United States Maastricht University Netherlands University of New South Wales NICTA Australia

The AAAI-10 workshop program was held on July 11-12, 2010, at the Westin Peachtree Plaza in Atlanta, Georgia. The workshop program included 13 workshops covering a wide range of topics in artificial intelligence. There were several presentations on particular formalisms for combining the levels of planning from the perspectives of classical planning, motion planning, POMDPs, search-based planning, and temporal logic-based constraints. Henry Lieberman from Massachusetts Institute of Technology Media Laboratory presented work developing a platform for knowledge acquisition and usage from expert users, built upon experience with eliciting information from nonexperts in a collaborative manner. The objective of the AAAI Workshop on Goal-Directed Autonomy (GDA) was to encourage discussion and novel contributions on intelligent agents that can self-select their goals. Jeremy Baxter from QinetiQ focused on user interaction, while Nick Hawes described a goal-directed reasoning framework for controlling a mobile robot.

关键词： Motion planning

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Resource management of enterprise cloud systems using layered queuing and historical performance models

Resource management of enterprise cloud systems using layere...

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2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010

作者： Bacigalupo, David A. Van Hemert, Jano Usmani, Asif Dillenberger, Donna N. Wills, Gary B. Jarvis, Stephen A. School of Electronics and Computer Science University of Southampton SO17 1BJ United Kingdom Data-Intensive Research Group School of Informatics University of Edinburgh EH8 9AB United Kingdom BRE Centre for Fire Safety Engineering School of Engineering University of Edinburgh EH9 3JL United Kingdom IBM T.J. Watson Research Centre Yorktown Heights NY 10598 United States Department of Computer Science High Performance Systems Group University of Warwick CV4 7AL United Kingdom

ISBN: (纸本)9781424465347

The automatic allocation of enterprise workload to resources can be enhanced by being able to make 'whatif' response time predictions, whilst different allocations are being considered. It is important to quantitatively compare the effectiveness of different prediction techniques for use in cloud infrastructures. To help make the comparison of relevance to a wide range of possible cloud environments it is useful to consider the following. 1.) urgent cloud customers such as the emergency services that can demand cloud resources at short notice (e.g. for our FireGrid emergency response software). 2.) dynamic enterprise systems, that must rapidly adapt to frequent changes in workload, system configuration and/or available cloud servers. 3.) The use of the predictions in a coordinated manner by both the cloud infrastructure and cloud customer management systems. 4.) A broad range of criteria for evaluating each technique. However, there have been no previous comparisons meeting these requirements. This paper, meeting the above requirements, quantitatively compares the layered queuing and ("HYDRA") historical techniques - including our initial thoughts on how they could be combined. Supporting results and experiments include the following: i.) defining, investigating and hence providing guidelines on the use of a historical and layered queuing model;ii.) using these guidelines showing that both techniques can make low overhead and typically over 70% accurate predictions, for new server architectures for which only a small number of benchmarks have been run;and iii.) defining and investigating tuning a prediction-based cloud workload and resource management algorithm. © 2010 IEEE.

关键词： Forecasting

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Resource management of enterprise cloud systems using layered queuing and historical performance models

Resource management of enterprise cloud systems using layere...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： David A. Bacigalupo Jano van Hemert Asif Usmani Donna N. Dillenberger Gary B. Wills Stephen A. Jarvis School of Electronics and Computer Science University of Southampton UK Data-Intensive Research Group School of Informatics University of Edinburgh UK BRE Centre of Fire Safety Engineering School of Engineering University of Edinburgh UK IBM Thomas J. Watson Research Center Yorktown Heights NY USA High Performance Systems Group Department of Computer Science University of Warwick UK

ISBN: (纸本)9781424465330

The automatic allocation of enterprise workload to resources can be enhanced by being able to make `what-if' response time predictions, whilst different allocations are being considered. It is important to quantitatively compare the effectiveness of different prediction techniques for use in cloud infrastructures. To help make the comparison of relevance to a wide range of possible cloud environments it is useful to consider the following. 1.) urgent cloud customers such as the emergency services that can demand cloud resources at short notice (e.g. for our FireGrid emergency response software). 2.) dynamic enterprise systems, that must rapidly adapt to frequent changes in workload, system configuration and/or available cloud servers. 3.) The use of the predictions in a coordinated manner by both the cloud infrastructure and cloud customer management systems. 4.) A broad range of criteria for evaluating each technique. However, there have been no previous comparisons meeting these requirements. This paper, meeting the above requirements, quantitatively compares the layered queuing and (¿HYDRA¿) historical techniques - including our initial thoughts on how they could be combined. Supporting results and experiments include the following: i.) defining, investigating and hence providing guidelines on the use of a historical and layered queuing model; ii.) using these guidelines showing that both techniques can make low overhead and typically over 70% accurate predictions, for new server architectures for which only a small number of benchmarks have been run; and iii.) defining and investigating tuning a prediction-based cloud workload and resource management algorithm.

关键词： Resource management Clouds Delay Predictive models computer science data engineering Emergency services Disaster management Guidelines Quality of service

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Evaluation of ontology mapping representations

Evaluation of ontology mapping representations

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23rd Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB 2009

作者： Thomas, Hendrik O'Sullivan, Declan Brennan, Rob Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (纸本)1902956850

A common approach to mitigate the effects of ontology heterogeneity is to discover and express the specific correspondences between different ontologies. An open research question is: how should such ontology mappings be represented. In recent years several proposals for an ontology mapping representation have been published, but till today no format is officially standardized or generally accepted in the community. In this paper we will present a new evaluation framework for ontology mapping representations for a pragmatic state of the art overview of their characteristics. In particular we are interested how current ontology mapping representations can support the management of ontology mappings (sharing, re-use, alteration) as well as how suitable they are for different mapping tasks.

关键词： Ontology

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Multilingual ontology mapping: Challenges and a proposed framework

Multilingual ontology mapping: Challenges and a proposed fra...

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23rd Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB 2009

作者： Fu, Bo Brennan, Rob O'Sullivan, Declan Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (纸本)1902956850

A key problem in supporting multilingual information retrieval and digital content management is reasoning about overlapping context domains. Ontologies are currently emerging as representation techniques for overlapping complimentary context domains. To date, research has focused on the mappings of monolingual ontologies, however, the issue of mapping ontologies written in different natural languages is relatively unexplored at the moment. This paper discusses challenges in the area of multilingual ontology mapping and proposes the semantic oriented mapping for multilingual ontologies (SOMMO) framework to advance the state of the art in multilingual ontology mapping. The SOMMO framework aims to improve multilingual ontology mapping results generated from existing monolingual ontology matching techniques by evaluating the semantics embedded in both the source and target ontologies.

关键词： Ontology

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Feature-Oriented Nonfunctional Requirement Analysis for Software Product Line

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Journal of computer science & Technology 2009年第2期24卷 319-338页

作者：彭鑫 Seok-Won Lee 赵文耘 School of Computer Science Fudan University Knowledge-Intensive Software Engineering Research Group Department of Software and Information Systems College of Computing and Informatics University of North Carolina at Charlotte

Domain analysis in software product line （SPL） development provides a basis for core assets design and imple- mentation by a systematic and comprehensive commonality/variability analysis. In feature-oriented SPL methods, products of the domain analysis are domain feature models and corresponding feature decision models to facilitate application-oriented customization. As in requirement analysis for a single system, the domain analysis in the SPL development should con- sider both flmctional and nonfunctional domain requirements. However, the nonfunctional requirements （NFRs） are often neglected in the existing domain analysis methods. In this paper, we propose a context-based method of the NFR analysis for the SPL development. In the method, NFRs are materialized by connecting nonfunctional goals with real-world context, thus NFR elicitation and variability analysis can be performed by context analysis for the whole domain with the assistance of NFR templates and NFR graphs. After the variability analysis, our method integrates both functional and nonfunc- tional perspectives by incorporating the nonfunctional goals and operationalizations into an initial functional feature model. NFR-related constraints are also elicited and integrated. Finally, a decision model with both functional and nonfunctional perspectives is constructed to facilitate application-oriented feature model customization. A computer-aided grading system （CAGS） product line is employed to demonstrate the method throughout the paper.

关键词： software product line nonfunctional requirement domain analysis feature-oriented method variability analysis

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A simulation-based approach to highly iterative prototyping of ubiquitous computing systems 2

A simulation-based approach to highly iterative prototyping ...

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2nd International ICST Conference on Simulation Tools and Techniques, SIMUTools 2009

作者： O'neill, Eleanor Lewis, David Conlan, Owen Knowledge and Data Engineering Group KDEG School of Computer Science Statistics Trinity College Dublin Dublin Ireland

ISBN: (纸本)9789639799455

Ubiquitous computing (ubicomp), as envisaged by Weiser [22], is heavily user-centric and largely concerned with applications specifically designed to meet end-user needs. Sensor populated ubicomp environments differentiate these applications from existing mobile and distributed systems through context awareness. For the system developer, the problems of heterogeneity and scalability are felt most keenly when designing this adaptive behaviour. A context-aware ubicomp system needs to operate reliably over the wide variety of situations that may be encountered. In this paper we present a technical architecture which has been implemented to support scalable, cost-effective, runtime experimentation using a framework of models to support informed decision making in an iterative design cycle.

关键词： Ubiquitous computing

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Ontology mapping representations: A pragmatic evaluation

Ontology mapping representations: A pragmatic evaluation

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作者： Thomas, Hendrik O'Sullivan, Declan Brennan, Rob Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (纸本)1891706241

A common approach to mitigate the effects of ontology heterogeneity is to discover and express the specific correspondences (mappings) between different ontologies. An open research question is: how should such ontology mappings be represented? In recent years several proposals for an ontology mapping representation have been published, but as yet no format is officially standardised or generally accepted in the community. In this paper we will present the results of a systematic analysis of ontology mapping representations to provide a pragmatic state of the art overview of their characteristics. In particular we are interested how current ontology mapping representations can support the management of ontology mappings (sharing, re-use, alteration) as well as how suitable they are for different mapping tasks.

关键词： Mapping

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A tag recommendation system based on contents

A tag recommendation system based on contents

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International Workshop on Discovery Challenge, DC 2009 at the European Conference on Machine Learning and Principles and Practice of knowledge Discovery in databases, ECML PKDD 2009

作者： Zhang, Ning Zhang, Yuan Tang, Jie Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University Beijing China

Social bookmarking tools become more and more popular nowadays and tagging is used to organize information and allow users to recall or search the resources. Users need to type the tags whenever they post a resource, so that a good tag recommendation system can ease the process of finding some useful and relevant keywords for users. Researchers have made lots of relevant work for recommendation system, but those traditional collaborative systems do not fit to our tag recommendation. In this paper, we present two different methods: a simple language model and an adaption of topic model. We evaluate and compare these two approaches and show that a combination of these two methods will perform better results for the task one of PKDD Challenge 2009.

关键词： Recommender systems

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A collaborative filtering tag recommendation system based on graph

A collaborative filtering tag recommendation system based on...

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International Workshop on Discovery Challenge, DC 2009 at the European Conference on Machine Learning and Principles and Practice of knowledge Discovery in databases, ECML PKDD 2009

作者： Zhang, Yuan Zhang, Ning Tang, Jie Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University Beijing China

With the rapid development of web2.0 technologies, tagging become much more important today to organize information and help users search the information they need with social bookmarking tools. In order to finish the second task of ECML PKDD challenge 2009, we propose a graph-based collaborative filtering tag recommendation system. We also refer to an algorithm called FolkRank, which is an adaptation of the famous Page Rank. We evaluate and compare these two approaches and show that a combination of these two methods will perform better results for our task.

关键词： Collaborative filtering

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