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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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An associative memory system for incremental learning and temporal sequence

An associative memory system for incremental learning and te...

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International Joint Conference on Neural Networks (IJCNN)

作者： Furao Shen Hui Yu Wataru Kasai Osamu Hasegawa State Key Laboratory for Novel Software Technology Jiangyin Information Technology Research Institute Nanjing University Nanjing China Department of Computer Intelligence and Systems Science Tokyo Institute of Technology Yokohama Japan Imaging Science and Engineering Laboratory Tokyo Institute of Technology Yokohama Japan

An associative memory (AM) system is proposed to realize incremental learning and temporal sequence learning. The proposed system is constructed with three layer networks: The input layer inputs key vectors, response vectors, and the associative relation between vectors. The memory layer stores input vectors incrementally to corresponding classes. The associative layer builds associative relations between classes. The proposed method can incrementally learn key vectors and response vectors; store and recall both static information and temporal sequence information; and recall information from incomplete or noise-polluted inputs. Experiments using binary data, real-value data, and temporal sequences show that the proposed method works well.

关键词： Associative memory Training Context Indexes Prototypes Humans Topology

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Performance Tuning Towards a KVM-Based Low Latency Virtualization System

Performance Tuning Towards a KVM-Based Low Latency Virtualiz...

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International Conference on Information engineering and computer science (ICIECS)

作者： Baojing Zuo Kai Chen Alei Liang Haibing Guan Jun Zhang Ruhui Ma Hongbo Yang School of Software Shanghai Jiaotong University Shanghai China School of Information Security Engineering Shanghai Jiaotong University Shanghai China Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China

Utilizing virtualization technology to combine real-time operating system(RTOS)and off-the-shelf time-sharing general purpose operating system (GPOS)is attracting much more interest *** combination has the potential to provide a large application base,and to guarantee timely deterministic response to real-time applications,yet there is no convincible experimental result about its real-time *** this paper,we analyze the interrupt latency of RTOS running on Linux KVM based on some preliminary tunings,and find out System Management Interrupt (SMI) is the main factor which makes the maximum latency unideal, so we propose a method to limit the worst-case interrupt latency in an acceptable ***,we also find out that boosting priority may result in wastes of CPU resources when RTOS is not executing real-time tasks,so we design a co-scheduling mechanism to improve the CPU throughput of the GPOS system.

关键词： Linux Real time systems Kernel Tuning Throughput Hardware

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A Translation Framework for Virtual Execution Environment on CPU/GPU Architecture

A Translation Framework for Virtual Execution Environment on...

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Guoxing Dong Kai Chen Erzhou Zhu Yichao Zhang Zhengwei Qi Haibing Guan School of Software Shanghai Jiaotong University Shanghai China School of Information Security Engineering Shanghai Jiaotong University Shanghai China Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China

GPUs are many-core processors with tremendous computational power. However, as automatic parallelization has not been realized yet, developing high-performance parallel code for GPUs is still very challenging. The paper presents a novel translation framework designed for virtual execution environment based on CPU/GPU architecture. It addresses two major challenges of taking advantage of general purpose computation on graphics processing units (GPGPU) to improve performance: no rewriting the existing source code and resolving binary compatibility issues between different GPUs. The translation framework uses semi-automatic parallelization technology to port existing code to explicitly parallel programming models. It not only offers a mapping strategy from X86 platform to CUDA programming model, but also synchronizes the execution between the CPU and the GPUs. The input to our translation framework is parallelizable part of the program within binary code. With an additional information related to the parallelizable part, the translation framework transforms the sequential code into PTX code and execute it on GPUs. Experimental results on several programs from CUDA SDK Code Samples and Parboil Benchmark Suite show that our translation framework could achieve very high performance, even up to several tens of times speedup over the X86 native version.

关键词： Graphics processing unit Registers Kernel Instruction sets Driver circuits Programming computer architecture

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Quantum State Reduction for Universal Measurement Based Computation

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Physical Review Letters 2010年第2期105卷 020502-020502页

作者： Xie Chen Runyao Duan Zhengfeng Ji Bei Zeng Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts USA Centre for Quantum Computation and Intelligent Systems (QCIS) Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia Department of Computer Science and Technology Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China Perimeter Institute for Theoretical Physics Waterloo Ontario Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Institute for Quantum Computing University of Waterloo Waterloo Ontario Canada Department of Combinatorics and Optimization University of Waterloo Waterloo Ontario Canada

Measurement based quantum computation, which requires only single particle measurements on a universal resource state to achieve the full power of quantum computing, has been recognized as one of the most promising models for the physical realization of quantum computers. Despite considerable progress in the past decade, it remains a great challenge to search for new universal resource states with naturally occurring Hamiltonians and to better understand the entanglement structure of these kinds of states. Here we show that most of the resource states currently known can be reduced to the cluster state, the first known universal resource state, via adaptive local measurements at a constant cost. This new quantum state reduction scheme provides simpler proofs of universality of resource states and opens up plenty of space to the search of new resource states.

关键词： Quantum computers

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Multi-error-correcting amplitude damping codes

Multi-error-correcting amplitude damping codes

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IEEE International Symposium on Information Theory

作者： Runyao Duan Markus Grassl Zhengfeng Ji Bei Zeng State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Centre for Quantum Computation and Intelligent Systems (QCIS) Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia Centre for Quantum Technologies National University of Singapore Singapore Perimeter Institute of Theoretical Physics Waterloo ONT Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy and Sciences Beijing China Institute for Quantum Computing and the Department of Combinatorics and Optimization University of Waterloo Waterloo ONT Canada

ISBN: (纸本)9781424478903

We construct new families of multi-error-correcting quantum codes for the amplitude damping channel. Our key observation is that, with proper encoding, two uses of the amplitude damping channel simulate a quantum erasure channel. This allows us to use concatenated codes with quantum erasure-correcting codes as outer codes for correcting multiple amplitude damping errors. Our new codes are degenerate stabilizer codes and have parameters which are better than the amplitude damping codes obtained by any previously known construction.

关键词： Damping Quantum mechanics Quantum computing Error correction codes Noise level Laboratories Phase noise Physics computing Intelligent systems computer science

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Delay-dependent conditions for monotonic convergence of uncertain ILC systems: An LMI approach

Delay-dependent conditions for monotonic convergence of unce...

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IEEE Conference on Decision and Control

作者： Deyuan Meng Yingmin Jia Junping Du Fashan Yu Seventh Research Division the Department of Systems and Control Beihang University Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo Henan China

ISBN: (纸本)9781424477456

This paper deals with the iterative learning control (ILC) problem for uncertain time-delay systems (TDS). In order to ensure monotonic convergence of the ILC process, a sufficient condition is developed using an H ∞ -based framework. It shows that under this framework, the convergence condition is enabled to be delay-dependent and have a formulation in terms of linear matrix inequalities (LMIs). Moreover, formulas for the updating law design can be derived by directly solving LMIs. A numerical example is provided to verify that the delay-dependent condition in LMI forms is effective in producing monotonically convergent ILC algorithms.

关键词： Convergence Symmetric matrices Robustness Delay Uncertainty Performance analysis Linear matrix inequalities

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Observer-Based L_(2)-L_(infinity) Control for a Class of Stochastic systems with Time-Varying Delay

Observer-Based L_(2)-L_(infinity) Control for a Class of Sto...

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2010 49th IEEE Conference on Decision and Control

作者： Lin Li Yingmin Jia Junping Du Fashan Yu Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

ISBN: (纸本)9781424477456

This paper is devoted to the problem of L_(2)-L_(infinity) control for a class of stochastic time-delay systems via observer-based feedback control. The considered observer contains no time-delay. The purpose is to design an observer-based feedback stabilizing controller such that the augmented closed-loop system is stochastically asymptotically stable with a prescribed L_(2)-L_(infinity) performance satisfied. Based on the stochastic stability theory, a delay-dependent sufficient condition is derived in terms of linear matrix inequalities (LMIs). The observer and controller design method is proposed, while the corresponding explicit expression for the observer and controller gain matrices is also given. Finally, numerical examples and simulation results are included to illustrate the effectiveness of the proposed results.

关键词： stochastic systems system clock time varying stability theory Stochastic delay systems controller gain Closed loop systems

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A New Resampling Algorithm for Generic Particle Filters

A New Resampling Algorithm for Generic Particle Filters

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American Control Conference

作者： X. Fu Y. Jia J. Du F. Yu Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

ISBN: (纸本)9781424474264

This paper is devoted to the resampling problem of particle filters. We firstly demonstrate the performance of classical Resampling algorithm (also called as systematic resampling algorithm) using a novel metaphor, through which the existing defects of Resampling algorithm is vividly reflected simultaneously. In order to avoid these defects, the exquisite resampling (ER) algorithm is induced which involves some exquisite actions such as comparing the weights by stages and generating the new particles based on quasi-Monte Carlo method. Simulations indicate that the proposed ER algorithm can reduce the sample impoverishment effectively and improve the accuracy of estimation evidently, which confirm that ER algorithm is a competitive alternative to Resampling algorithm.

关键词： resampling algorithm Particle filters

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Analysis of Two Robust Learning Control Schemes in the Presence of Random Iteration-Varying Noise

Analysis of Two Robust Learning Control Schemes in the Prese...

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American Control Conference

作者： Deyuan Meng Yingmin Jia Junping Du Fashan Yu Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

ISBN: (纸本)9781424474264

This paper deals with the design problem of robust iterative learning control (ILC), in the presence of noise that is varying randomly from iteration to iteration. Two ILC schemes are considered: one adopts the previous iteration tracking error (PITE) and the other adopts the current iteration tracking error (CITE), in the updating law. For both schemes, the convergence results are obtained by using a frequency-domain approach, and a comparison between them is presented from the viewpoints of the convergence condition, robustness against plant uncertainty, and delay compensation. It shows that sufficient conditions can be derived to bound the tracking error and make its expectation monotonically convergent in the sense of L_(2)-norm, which work effectively with robustness for all admissible plant uncertainties. Furthermore, the sufficient conditions for both schemes can also be formulated in terms of two complementary functions, which do not depend on the delay time as well as the plant uncertainty and, thus, make them convenient to be checked and solved using the frequency-domain tools. Numerical simulations are included to illustrate the effectiveness of the two proposed ILC schemes.

关键词： Iterative learning control monotonic convergence previous iteration tracking error current iteration tracking error random iteration-varying noise delay compensation delay compensation monotone convergence iterative methods Iterative learning control follow error Scheme design problems Delay time Sufficient conditions learning control

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