Dynamic programming

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In mathematics and computer science, dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. (Wikipedia.org)






Conferences related to Dynamic programming

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2017 IEEE Power & Energy Society General Meeting

The annual IEEE Power & Energy Society General Meeting will bring together over 2000 attendees for technical sessions, student program, awards ceremony, committee meetings, and tutorials.

  • 2015 IEEE Power & Energy Society General Meeting

    The annual IEEE PES General Meeting will bring together over 2500 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutorials and more PLEASE NOTE: Abstracts are not accepted for the 2015 IEEE PES General Meeting, full papers only can be submitted to the submission site 24 October 2014 through 21 November 2014.  The site will be available from the PES home page www.ieee-pes.org

  • 2014 IEEE Power & Energy Society General Meeting

    The annual IEEE PES General Meeting will bring together over 2500 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutotials and more

  • 2013 IEEE Power & Energy Society General Meeting

    The annual IEEE Power & Energy Society General Meeting will bring together over 2000 attendees for technical sessions, student program, awards ceremony, committee meetings, and tutorials.

  • 2012 IEEE Power & Energy Society General Meeting

    The annual IEEE Power & Energy Society General Meeting will bring together over 2000 attendees for technical sessions, student program, awards ceremony, committee meetings, and tutorials.

  • 2011 IEEE Power & Energy Society General Meeting

    IEEE Power & Energy Annual Meeting --Papers --Awards --Plenary --Committee Meetings --Governing Board --Receptions --Tech tours --Tutorials --Companions Program

  • 2010 IEEE Power & Energy Society General Meeting

    IEEE Power & Energy Society Annual Meeting --Technical Sessions --Committee Meetings --Plenary Session --Gove Board Meeting --Awards Banquet --Tutorials --Student Activities --Social Events --Companions Program


2014 American Control Conference - ACC 2014

All areas of the theory and practice of automatic control, including but not limited to network control systems, model predictive control, systems analysis in biology and medicine, hybrid and switched systems, aerospace systems, power and energy systems and control of nano- and micro-systems.

  • 2013 American Control Conference (ACC)

    Control systems theory and practice. Conference themes on sustainability, societal challenges for control, smart healthcare systems. Conference topics include biological systems, vehicle dynamics and control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2012 American Control Conference - ACC 2012

    All areas of control engineering and science.

  • 2011 American Control Conference - ACC 2011

    ACC provides a forum for bringing industry and academia together to discuss the latest developments in the area of Automatic Control Systems, from new control theories, to the advances in sensors and actuator technologies, and to new applications areas for automation.

  • 2010 American Control Conference - ACC 2010

    Theory and practice of automatic control

  • 2009 American Control Conference - ACC 2009

    The 2009 ACC technical program will cover new developments related to theory, application, and education in control science and engineering. In addition to regular technical sessions the program will also feature interactive and tutorial sessions and preconference workshops.

  • 2008 American Control Conference - ACC 2008

  • 2007 American Control Conference - ACC 2007

  • 2006 American Control Conference - ACC 2006 (Silver Anniversary)


2013 21st Mediterranean Conference on Control & Automation (MED)

MED aims at providing a unique opportunity for the academic and industrial community, federal and state government, funding agencies, the private sector, qualified representatives from other organizations, to address new challenges, share solutions and discuss future research directions in the area of control and automation. Focused interests of diverse groups involved in basic and applied research and development will be discussed. A broad range of topics will be covered in the Conference, following current trends of combining control/systems theory with software/communication technologies, as well as new developments in robotics and mechatronics, with the goal of strengthening cooperation of control and automation scientists with industry

  • 2012 20th Mediterranean Conference on Control & Automation (MED 2012)

    The focus of the conference is on new directions in control and automation and to promote ideas and collaboration among researchers in the Mediterranean region and throughout the globe.

  • 2011 19th Mediterranean Conference on Control & Automation (MED 2011)

    The scope of this 19th MED is to bring together researchers in Systems and Control from the Mediterranean region and elsewhere. The emphasis is on theoretical developments which are motivated by practical needs

  • 2010 18th Mediterranean Conference on Control & Automation (MED 2010)

    The conference, through its technical program, will provide a unique opportunity for the academic and industrial community to address new challenges, share solutions and discuss future research directions. A broad range of topics is proposed, following current trends of combining control/systems theory with software/communication technologies.

  • 2009 17th Mediterranean Conference on Control & Automation (MED 2009)

    The conference, through its technical program, will provide a unique opportunity for the academic and industrial community to address new challenges, share solutions and discuss future research directions. A broad range of topics is proposed, following current trends of combining control/systems theory with software/communication technologies.


2013 IEEE 10th International Conference on Networking, Sensing and Control (ICNSC)

The main theme of the conference is Technology for efficient green networks . It will provide a remarkable opportunity for the academic and industrial communities to address new challenges and share solutions, and discuss future research directions.

  • 2012 9th IEEE International Conference on Networking, Sensing and Control (ICNSC)

    This conference will provide a remarkable opportunity for the academic and industrial community to address new challenges and share solutions, and discuss future research directions in the area of intelligent transportation systems and networks as well other areas of networking, sensing and control. It will feature plenary speeches, industrial panel sessions, funding agency panel sessions, interactive sessions, and invited/special sessions. Contributions are expected from academia, industry, and government agencies.

  • 2011 IEEE International Conference on Networking, Sensing and Control (ICNSC)

    The main theme of the conference is Next Generation Infrastructures . Infrastructures are the backbone of the economy and society. Especially the network bound infrastructures operated by public utilities and network industries provide essential services that are enabling for almost every economic and social activity. The crucial role of the infrastructure networks for energy and water supply, transportation of people and goods, and provision of telecommunication and information services.

  • 2010 International Conference on Networking, Sensing and Control (ICNSC)

    Provide a remarkable opportunity for the academic and industrial community to address new challenges and share solutions, and discuss future research directions.

  • 2009 International Conference on Networking, Sensing and Control (ICNSC)

    The main theme of the conference is advanced technologies for safety and functional maintenance. The real challenge is to obtain advanced control technology for safety and management technology and to construct an information system to share information on safetytechnology and on investigated accidents.


2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

Adaptive (or Approximate) dynamic programming (ADP) is a general and effective approach for solving optimal control problems by adapting to uncertain environments over time. ADP optimizes a user-defined cost function with respect to an adaptive control law, conditioned on prior knowledge of the system, and its state, in the presence of system uncertainties. A numerical search over the present value of the control minimizes a nonlinear cost function forward-in-time providing a basis for real-time, approximate optimal control. The ability to improve performance over time subject to new or unexplored objectives or dynamics has made ADP an attractive approach in a number of application domains including optimal control and estimation, operation research, and computational intelligence. ADP is viewed as a form of reinforcement learning based on an actor-critic architecture that optimizes a user-prescribed value online and obtains the resulting optimal control policy.


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Periodicals related to Dynamic programming

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Parallel and Distributed Systems, IEEE Transactions on

IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. Topic areas include, but are not limited to the following: a) architectures: design, analysis, and implementation of multiple-processor systems (including multi-processors, multicomputers, and networks); impact of VLSI on system design; interprocessor communications; b) software: parallel languages and compilers; scheduling and task partitioning; databases, operating systems, and programming environments for ...



Most published Xplore authors for Dynamic programming

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Xplore Articles related to Dynamic programming

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A New Approach to Solve a Class of Continuous-Time Nonlinear Quadratic Zero-Sum Game Using ADP

Qinglai Wei; Huaguang Zhang 2008 IEEE International Conference on Networking, Sensing and Control, 2008

In this paper, a new approach is presented to solve a class of continuous-time nonlinear two-person zero-sum game. The idea is using approximate dynamic programming (ADP) technique to obtain the optimal control pair where the saddle point value of the zero-sum may not exist. It is shown that the control pair will stabilize the nonlinear system and the convergence of ...


Unitary Space–Time Constellation Analysis: An Upper Bound for the Diversity

G. Han; J. Rosenthal IEEE Transactions on Information Theory, 2006

The diversity product and the diversity sum are two very important parameters for a good-performing unitary space-time constellation. A basic question is what the maximal diversity product (or sum) is. In this correspondence, we are going to derive general upper bounds on the diversity sum and the diversity product for unitary constellations of any dimension n and any size m ...


Multiple stopping time POMDPs: Structural results

Vikram Krishnamurthy; Anup Aprem; Sujay Bhatt 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016

This paper considers a multiple stopping problem on a Hidden Markov model sample path of infinite horizon; where a reward, dependent on the underlying state, is associated with each stop. The decision maker stops L times to maximize the total expected revenue. The aim is to determine the structure of the optimal multiple stopping policy. The formulation generalizes the classical ...


A note on scheduling the two-machine flexible flowshop

T. C. E. Cheng; Guoqing Wang IEEE Transactions on Robotics and Automation, 1999

In this note we consider the NP-complete one-setup version of the two-machine flexible manufacturing cell scheduling problem studied by Lee and Mirchandani (1988). We provide a pseudopolynomial dynamic programming algorithm to solve the problem, thus establishing that the problem is NP-complete in the ordinary sense. We derive a tight worst-case error bound for the heuristic presented by Lee and Mirchandani, ...


A novel kernel for sequences classification

Chun Yan; Zheng-Zhi Wang; Qing-Bin Gao; Yao-Hua Du 2005 International Conference on Natural Language Processing and Knowledge Engineering, 2005

In this paper, a novel kernel, called position weight subsequences kernel (PWSK), is introduced for identifying gene sequences. String subsequences kernel (SSK), which is based on string alignment, performs well for text categorization problems. For gene sequences identification, not only the comprised subsequences but also the positions of them are important. To integrate the position information, the decay factor of ...


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Educational Resources on Dynamic programming

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eLearning

A New Approach to Solve a Class of Continuous-Time Nonlinear Quadratic Zero-Sum Game Using ADP

Qinglai Wei; Huaguang Zhang 2008 IEEE International Conference on Networking, Sensing and Control, 2008

In this paper, a new approach is presented to solve a class of continuous-time nonlinear two-person zero-sum game. The idea is using approximate dynamic programming (ADP) technique to obtain the optimal control pair where the saddle point value of the zero-sum may not exist. It is shown that the control pair will stabilize the nonlinear system and the convergence of ...


Unitary Space–Time Constellation Analysis: An Upper Bound for the Diversity

G. Han; J. Rosenthal IEEE Transactions on Information Theory, 2006

The diversity product and the diversity sum are two very important parameters for a good-performing unitary space-time constellation. A basic question is what the maximal diversity product (or sum) is. In this correspondence, we are going to derive general upper bounds on the diversity sum and the diversity product for unitary constellations of any dimension n and any size m ...


Multiple stopping time POMDPs: Structural results

Vikram Krishnamurthy; Anup Aprem; Sujay Bhatt 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016

This paper considers a multiple stopping problem on a Hidden Markov model sample path of infinite horizon; where a reward, dependent on the underlying state, is associated with each stop. The decision maker stops L times to maximize the total expected revenue. The aim is to determine the structure of the optimal multiple stopping policy. The formulation generalizes the classical ...


A note on scheduling the two-machine flexible flowshop

T. C. E. Cheng; Guoqing Wang IEEE Transactions on Robotics and Automation, 1999

In this note we consider the NP-complete one-setup version of the two-machine flexible manufacturing cell scheduling problem studied by Lee and Mirchandani (1988). We provide a pseudopolynomial dynamic programming algorithm to solve the problem, thus establishing that the problem is NP-complete in the ordinary sense. We derive a tight worst-case error bound for the heuristic presented by Lee and Mirchandani, ...


A novel kernel for sequences classification

Chun Yan; Zheng-Zhi Wang; Qing-Bin Gao; Yao-Hua Du 2005 International Conference on Natural Language Processing and Knowledge Engineering, 2005

In this paper, a novel kernel, called position weight subsequences kernel (PWSK), is introduced for identifying gene sequences. String subsequences kernel (SSK), which is based on string alignment, performs well for text categorization problems. For gene sequences identification, not only the comprised subsequences but also the positions of them are important. To integrate the position information, the decay factor of ...


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IEEE-USA E-Books

  • Elementary Solution Methods

    Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

  • Dynamic Programming

    This chapter contains sections titled: Policy Evaluation, Policy Improvement, Policy Iteration, Value Iteration, Asynchronous Dynamic Programming, Generalized Policy Iteration, Efficiency of Dynamic Programming, Summary, Bibliographical and Historical Remarks

  • Machine Learning Algorithms

    This chapter contains sections titled: Introduction, Dynamic Programming, Gradient Descent, EM/GEM Algorithms, Markov-Chain Monte-Carlo Methods, Simulated Annealing, Evolutionary and Genetic Algorithms, Learning Algorithms: Miscellaneous Aspects

  • Dynamic Time Warping

    This chapter contains sections titled: Introduction Dynamic Programming Dynamic Time Warping Applied to IWR DTW Applied to CSR Training Issues in DTW Algorithms Conclusions Problems

  • Optimal Learning and Approximate Dynamic Programming

    This chapter contains sections titled: Introduction Modeling The Four Classes of Policies Basic Learning Policies for Policy Search Optimal Learning Policies for Policy Search Learning with a Physical State References

  • Adaptive Feature Pursuit: Online Adaptation of Features in Reinforcement Learning

    This chapter contains sections titled: Introduction The Framework The Feature Adaptation Scheme Convergence Analysis Application to Traffic Signal Control Conclusions References

  • Hierarchical Approaches to Concurrency, Multiagency, and Partial Observability

    In this chapter the authors summarize their research in hierarchical probabilistic models for decision making involving concurrent action, multiagent coordination, and hidden state estimation in stochastic environments. A hierarchical model for learning concurrent plans is first described for observable single agent domains, which combines compact state representations with temporal process abstractions to determine how to parallelize multiple threads of activity. A hierarchical model for multiagent coordination is then presented, where primitive joint actions and joint states are hidden. Here, high-level coordination is learned by exploiting overall task structure, which greatly speeds up convergence by abstracting from low- level steps that do not need to be synchronized. Finally, a hierarchical frame-work for hidden state estimation and action is presented, based on multi-resolution statistical modeling of the past history of observations and actions.

  • A Unified View

    Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

  • Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks

    This paper presents U-Tree, a reinforcement learning algorithm that uses selective attention and short-term memory to simultaneously address the intertwined problems of large perceptual state spaces and hidden state. By combining the advantages of work in instance-based (or "memory-based") learning and work with robust statistical tests for separating noise from task structure, the method learns quickly, creates only task-relevant state distinctions, and handles noise well.U-Tree uses a tree-structured representation, and is related to work on Prediction Suffix Trees [Ron et al., 1994], Parti-game [Moore, 1993], G-algorithm [Chapman and Kaelbling, 1991], and Variable Resolution Dynamic Programming [Moore, 1991]. It builds on Utile Suffix Memory [McCallum, 1995c], which only used short-term memory, not selective perception. The algorithm is demonstrated solving a highway driving task in which the agent weaves around slower and faster traffic. The agent uses active perception with simulated eye movements. The environment has hidden state, time pressure, stochasticity, over 21,000 world states and over 2,500 percepts. From this environment and sensory system, the agent uses a utile distinction test to build a tree that represents depth-three memory where necessary, and has just 143 internal states--far fewer than the 25003 states that would have resulted from a fixed-sized history-window approach.

  • 1D Models: Deformable Curves

    This chapter contains sections titled: 4.1 Statistical Model, 4.2 Computation: Dynamic Programming, 4.3 Global Optimization on a Tree-Structured Prior, 4.4 Bibliographical Notes and Discussion



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