Conferences related to Decision Making

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2016 IEEE 55th IEEE Conference on Decision and Control (CDC)

The CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.

  • 2014 IEEE 53rd Annual Conference on Decision and Control (CDC)

    Largest annual conference in control theory and its applications. Areas covered all applied math, communication, control, aerospace, biology, etc.

  • 2013 IEEE 52nd Annual Conference on Decision and Control (CDC)

    The 52nd IEEE Conference on Decision and Control will be held Tuesday through Friday, December 10-13, 2013 at the Congress Centre in Firenze, Italy. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss the latest advancements of the discipline, shape its future directions, and promote its diffusion among the scientific community at large. The 52nd CDC will feature the presentation of contributed and invited papers, as well as tutorial sessions and workshops. The CDC is hosted by the IEEE Control Systems Society (CSS), and is organized in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).

  • 2012 IEEE 51st Annual Conference on Decision and Control (CDC)

    The conference discusses advances in theory, design and application of control systems. Papers will highlight the latest knowledge, exploratory developments, and practical applications in all aspects of the control systems from analysis and design through simulation and hardware. Its scope 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, organiz

  • 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011)

    This conference is dedicated to the advancement of the theory and practice of systems and control, bringing together an international community of researchers and practitioners to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.

  • 2010 49th IEEE Conference on Decision and Control (CDC)

    Theory and applications of control theory and control systems technology

  • 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC)

    This conference is dedicated to the advancement of the theory and practice of systems and control, bringing together an international community of researchers and practitioners to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.

  • 2008 47th IEEE Conference on Decision and Control (CDC)

    The CDC is the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control, bringing together an international community of researchers and practitioners to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

IEEE International Conference on Fuzzy Systems is the largest technical event in the field of fuzzy systems. In 2014, International Joint Conference on Neural Networks will be part of the 2104 IEEE World Congress on Computational Intelligence.

  • 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

    The conference will provide a platform for researchers and practitioners to deliberate / exchange ideas on a wide range topics in fuzzy systems and related areas including fuzzy measures, fuzzy control, fuzzy pattern recognition, data/text/web mining, information/text/image retrieval, knowledge discovery, reasoning, and applications of fuzzy theories in all areas

  • 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

    The annual FUZZ-IEEE is one of the leading events in the field of fuzzy systems.

  • 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

    The FUZZ-IEEE 2011 will be held in Taipei, Taiwan. The conference will cover the whole range of research and applications in fuzzy systems and soft computing. In addition to regular oral and poster presentations, the conference will include a full program of tutorials, workshops, panel sessions, and keynote talks.


2014 IEEE International Conference on Systems, Man and Cybernetics - SMC

SMC2014 targets advances in Systems Science and Engineering, Human-Machine Systems, and Cybernetics involving state-of-art technologies interacting with humans to provide an enriching experience and thereby improving the quality of lives including theories, methodologies, and emerging applications.


2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2013)

Humanoids in the Real World: All related areas of humanoid robotics including locomotion, architectures, mechatronics, control, perception, planning, learning, neuroscience and interaction.

  • 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)

    The conference theme is 'Humanoids and Humans: Towards A New Frontier.' More than a decade has passed since the first Humanoids conference. Over that period, science and technology have advanced significantly. It is time to revisit the original conception of humanoids -- human-like robots -- and engage the next stage of humanoid research. What can we do with the current and emerging research across broad areas of science and technology to explore the next generation of humanoids and their new relationship to humans? Papers contributing to answering this question from any aspects are solicited.

  • 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011)

    The creation of general-purpose service and companion humanoid robots is one of the greatest challenges in today s robotics research with a potentially huge impact. Papers are solicited in all related areas of humanoid robotics including mechatronics, control, perception, planning, learning, neuroscience, and human-robot interaction.

  • 2010 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2010)

    Humanoid Robotics is an increasing research topic stimulated both by the perspective of highly challenging applications in servicing robotics and by renewing fundamental research topics in Robotics at large such as Mechatronics, Control, Decision Making and Human-Robot Interaction. More than that Humanoid Robotics opens synergetic researches towards Life and Human Science. Such openness will constitute the special theme of Humanoids2010.


2013 9th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM)

All areas related to wireless communications, network technologies, and mobile computing systems.


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Periodicals related to Decision Making

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Industrial Informatics, IEEE Transactions on

IEEE Transactions on Industrial Informatics focuses on knowledge-based factory automation as a means to enhance industrial fabrication and manufacturing processes. This embraces a collection of techniques that use information analysis, manipulation, and distribution to achieve higher efficiency, effectiveness, reliability, and/or security within the industrial environment. The scope of the Transaction includes reporting, defining, providing a forum for discourse, and informing ...


Systems, Man and Cybernetics, Part A, IEEE Transactions on

Systems engineering, including efforts that involve issue formnaulations, issue analysis and modeling, and decision making and issue interpretation at any of the life-cycle phases associated with the definition, development, and implementation of large systems. It will also include efforts that relate to systems management, systems engineering processes and a variety of systems engineering methods such as optimization, modeling and simulation. ...


Systems, Man, and Cybernetics, Part B, IEEE Transactions on

The scope of the IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or between machines, humans, and organizations. The scope of Part B includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, ...


Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on

Applications, review, and tutorial papers within the scope of the Systems, Man and Cybernetics Society. Currently, this covers: (1) Integration of the theories of communication, control cybernetics, stochastics, optimization and system structure towards the formulation of a general theory of systems; (2) Development of systems engineering technology including problem definition methods, modeling, and stimulation, methods of systems experimentation, human factors ...



Most published Xplore authors for Decision Making

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Xplore Articles related to Decision Making

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Agents for distributed decision-making

S. Talukdar 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491), 2003

Agents are modules from which problem solving can be built. Structurally, an agent is a bundle of sensors, decision-makers and actuators; behaviorally, an agent is a mapping from an in-space (all the things the agent can sense) to an out-space (all things the agent can affect). Agents can be simple or compound. More specifically, lesser agents can be organized into ...


From interest to decision in cooperative education programs

Trina L. Fletcher; Joyce B. Main; Nichole M. Ramirez; Matthew W. Ohland 2014 IEEE Frontiers in Education Conference (FIE) Proceedings, 2014

Cooperative education programs (co-op), also referred to as Work-Integrated Learning (WIL), provide students with relevant professional experiences in industry. This industry-focused environment presents an opportunity for students to clarify academic and career objectives prior to finishing their studies. This first stage of a four-phase research project explores undergraduate engineering students' interest and decision-making process related to participation in cooperative education ...


Machine learning approaches to power-system security assessment

L. Wehenkel IEEE Expert, 1997

The paper discusses a framework that uses machine learning and other automatic-learning methods to assess power-system security. The framework exploits simulation models in parallel to screen diverse simulation scenarios of a system, yielding a large database. Using data mining techniques, the framework extracts synthetic information about the simulated system's main features from this database


Information-Oriented Models and Methods for Construction Project Supply Chain Coordination

Xiaolong Xue; Chengshuang Sun 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology, 2009

This work focuses on establishing coordination models and method with different information in the operation process of established construction project supply chains (CPSCs). A two-level programming model for collaborative decision making is established to find optimal solutions for all stakeholders in CPSCs. An agent-based negotiation framework for CPSCs coordination in dynamic decision environment is designed based on the intelligent agent ...


Automated ASPECTS scoring system as a clinical support system for acute stroke care

Yao Shieh; Chien Hung Chang Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, 2012

The recombinant tissue plasminogen activator (tPA) has shown effective in improving the outcome of early acute ischemic if less than one third of the territory of the middle cerebral artery (MCA) was involved. The Alberta Stroke Program Early CT Score (ASPECTS) method has been adopted to assess the MCA involvement by many institutions. However, ASPECTS scoring is still a challenge ...


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Educational Resources on Decision Making

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eLearning

Agents for distributed decision-making

S. Talukdar 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491), 2003

Agents are modules from which problem solving can be built. Structurally, an agent is a bundle of sensors, decision-makers and actuators; behaviorally, an agent is a mapping from an in-space (all the things the agent can sense) to an out-space (all things the agent can affect). Agents can be simple or compound. More specifically, lesser agents can be organized into ...


From interest to decision in cooperative education programs

Trina L. Fletcher; Joyce B. Main; Nichole M. Ramirez; Matthew W. Ohland 2014 IEEE Frontiers in Education Conference (FIE) Proceedings, 2014

Cooperative education programs (co-op), also referred to as Work-Integrated Learning (WIL), provide students with relevant professional experiences in industry. This industry-focused environment presents an opportunity for students to clarify academic and career objectives prior to finishing their studies. This first stage of a four-phase research project explores undergraduate engineering students' interest and decision-making process related to participation in cooperative education ...


Machine learning approaches to power-system security assessment

L. Wehenkel IEEE Expert, 1997

The paper discusses a framework that uses machine learning and other automatic-learning methods to assess power-system security. The framework exploits simulation models in parallel to screen diverse simulation scenarios of a system, yielding a large database. Using data mining techniques, the framework extracts synthetic information about the simulated system's main features from this database


Information-Oriented Models and Methods for Construction Project Supply Chain Coordination

Xiaolong Xue; Chengshuang Sun 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology, 2009

This work focuses on establishing coordination models and method with different information in the operation process of established construction project supply chains (CPSCs). A two-level programming model for collaborative decision making is established to find optimal solutions for all stakeholders in CPSCs. An agent-based negotiation framework for CPSCs coordination in dynamic decision environment is designed based on the intelligent agent ...


Automated ASPECTS scoring system as a clinical support system for acute stroke care

Yao Shieh; Chien Hung Chang Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, 2012

The recombinant tissue plasminogen activator (tPA) has shown effective in improving the outcome of early acute ischemic if less than one third of the territory of the middle cerebral artery (MCA) was involved. The Alberta Stroke Program Early CT Score (ASPECTS) method has been adopted to assess the MCA involvement by many institutions. However, ASPECTS scoring is still a challenge ...


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IEEE-USA E-Books

  • Sequential Decision Making

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • Online Stochastic Scheduling

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • References

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • The Economics of Information

    This chapter contains sections titled: Information and Individual Decision Making: Information as Value and as Cost, Organization as an Economy in the Acquisition of Information, The Economic System as an Organization, The Problem of Incentives, Editors' Postscript

  • No title

    Engineers work in an increasingly complex entanglement of ideas, people, cultures, technology, systems and environments. Today, decisions made by engineers often have serious implications for not only their clients but for society as a whole and the natural world. Such decisions may potentially influence cultures, ways of living, as well as alter ecosystems which are in delicate balance. In order to make appropriate decisions and to co-create ideas and innovations within and among the complex networks of communities which currently exist and are shaped by our decisions, we need to regain our place as professionals, to realise the significance of our work and to take responsibility in a much deeper sense. Engineers must develop the 'ability to respond' to emerging needs of all people, across all cultures. To do this requires insights and knowledge which are at present largely within the domain of the social and political sciences but which need to be shared with our students in ways hich are meaningful and relevant to engineering. This book attempts to do just that. In Part 1 Baillie introduces ideas associated with the ways in which engineers relate to the communities in which they work. Drawing on scholarship from science and technology studies, globalisation and development studies, as well as work in science communication and dialogue, this introductory text sets the scene for an engineering community which engages with the public. In Part 2 Catalano frames the thinking processes necessary to create ethical and just decisions in engineering, to understand the implications of our current decision making processes and think about ways in which we might adapt these to become more socially just in the future. In Part 3 Baillie and Catalano have provided case studies of everyday issues such as water, garbage and alarm clocks, to help us consider how we might see through the lenses of our new knowledge from Parts 1 and 2 and apply this to our everyday existence as ngineers.

  • Online Stochastic Routing

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • Multi-Rule-Set Decision-Making Schemes for A Genetic Algorithm Learning Environment for Classification Tasks

    Over the last three years, we developed an inductive learning environment called DELVAUX for classification tasks that learns PROSPECTOR-style, Bayesian rules from sets of examples, using a genetic algorithm to evolve a population consists of rule-sets. Several problems complicate the search for the best rule-set. First, the search space that is explored by DELVAUX is enormously large, which makes it difficult to predict if a particular solution is a good solution. The second problem is the problem of convergence with outliers that perform well in training but not in testing. This paper describes efforts to alleviate these two problems centering on multi-ruleset learning techniques that learn multiple rule-sets and proposes several decision-making schemes that are employed by the multi-rule-set learning environment to derive a decision. Empirical results are presented that compare the single rule-set learning environment of DELVAUX with several multi-rule-set learning environments that use different decision-making schemes. Moreover, a more sophisticated fitness function for the multi-ruleset learning approach is introduced, and a genetic algorithm approach that finds the "best" multi-rule- set for a given set of rule-sets is discussed.

  • Online Stochastic Scheduling

    This chapter contains section titled: 2.1 The Generic Offline Problem, 2.2 The Online Problem, 2.3 The Generic Online Algorithm, 2.4 Properties of Online Stochastic Scheduling, 2.5 Oblivious Algorithms, 2.6 The Expectation Algorithm, 2.7 The Consensus Algorithm, 2.8 The Regret Algorithm, 2.9 Immediate Decision Making, 2.10 The Suboptimality Approximation Problem, 2.11 Notes and Further Reading

  • Index

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • The Ideas: Policy-makers and Scholarship

    As the global information infrastructure evolves, the field of communication has the opportunity to renew itself while addressing the urgent policy need for new ways of thinking and new data to think about. Communication Researchers and Policy-making examines diverse relationships between the communication research and policy communities over more than a century and the issues that arise out of those interactions. The book provides primary material in the form of reports on such relationships spanning time periods, subject matter, policy issues, decision-making venues, and governments.The essays range from historical pieces on the importance of communication research since the beginning of systematic policy analysis and on the various roles that researchers can play to contemporary analyses of contributions of research to policy debates over network design and access, media violence, and advertising fraud. Substantial interstitial essays by the editor explore the impact of the policy context on communication theories and research practices, relationships between researchers and their institutional homes, the role of communication researchers as public intellectuals, and ways to maximize the impact of communication research on policy-making during this period of infrastructural transformation. The book includes an extensive bibliography.



Standards related to Decision Making

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No standards are currently tagged "Decision Making"