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16.412J Cognitive Robotics (MIT) 16.412J Cognitive Robotics (MIT)

Description

Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.Technical RequirementsAny text editor can be used to view the .ascii, .binary, .map, and .pddl files found on this course site. Any number of development tools can be used to compile and run the .c and .h files found on this course site. Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.Technical RequirementsAny text editor can be used to view the .ascii, .binary, .map, and .pddl files found on this course site. Any number of development tools can be used to compile and run the .c and .h files found on this course site.

Subjects

cognitive robotics | cognitive robotics | robotic systems | robotic systems | intelligence algorithms | intelligence algorithms | robustness algorithms | robustness algorithms | intelligence paradigms | intelligence paradigms | robustness paradigms | robustness paradigms | autonomous robots | autonomous robots | mars explorers | mars explorers | cooperative air vehicles | cooperative air vehicles | embedded devices | embedded devices | real-time deduction | real-time deduction | real-time search | real-time search | temporal planning | temporal planning | decision-theoretic planning | decision-theoretic planning | contingency planning | contingency planning | dynamic execution | dynamic execution | dynamics re-planning | dynamics re-planning | reasoning | reasoning | path planning | path planning | reasoning under uncertainty | reasoning under uncertainty | mapping | mapping | localization | localization | cooperative robotics | cooperative robotics | distributed robotics | distributed robotics | mars exploration rover | mars exploration rover | nursebot | nursebot | museum tourguide | museum tourguide | human-interaction systems | human-interaction systems | navigation | navigation | state-aware robots | state-aware robots | fast planning | fast planning | cooperative planning | cooperative planning | vision-based exploration | vision-based exploration | preplanning | preplanning | 16.412 | 16.412 | 6.834 | 6.834

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16.412J Cognitive Robotics (MIT) 16.412J Cognitive Robotics (MIT)

Description

Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover. Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.

Subjects

cognitive robotics | cognitive robotics | robotic systems | robotic systems | intelligence algorithms | intelligence algorithms | robustness algorithms | robustness algorithms | intelligence paradigms | intelligence paradigms | robustness paradigms | robustness paradigms | autonomous robots | autonomous robots | mars explorers | mars explorers | cooperative air vehicles | cooperative air vehicles | embedded devices | embedded devices | real-time deduction | real-time deduction | real-time search | real-time search | temporal planning | temporal planning | decision-theoretic planning | decision-theoretic planning | contingency planning | contingency planning | dynamic execution | dynamic execution | dynamics re-planning | dynamics re-planning | reasoning | reasoning | path planning | path planning | reasoning under uncertainty | reasoning under uncertainty | mapping | mapping | localization | localization | cooperative robotics | cooperative robotics | distributed robotics | distributed robotics | mars exploration rover | mars exploration rover | nursebot | nursebot | museum tourguide | museum tourguide | human-interaction systems | human-interaction systems

License

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ESD.83 Doctoral Seminar in Engineering Systems (MIT) ESD.83 Doctoral Seminar in Engineering Systems (MIT)

Description

In establishing the Engineering Systems Division, MIT has embarked on a bold experiment – bringing together diverse areas of expertise into what is designed to be a new field of study. In many respects, the full scale and scope of Engineering Systems as a field is still emerging. This seminar is simultaneously designed to codify what we presently know and to give direction for future development. In establishing the Engineering Systems Division, MIT has embarked on a bold experiment – bringing together diverse areas of expertise into what is designed to be a new field of study. In many respects, the full scale and scope of Engineering Systems as a field is still emerging. This seminar is simultaneously designed to codify what we presently know and to give direction for future development.

Subjects

engineering systems | engineering systems | complexity | complexity | uncertainty | uncertainty | fragility | fragility | robustness | robustness | systems engineering | systems engineering | systems dynamics | systems dynamics | agent modeling | agent modeling | systems simulations | systems simulations | large-scale systems change | large-scale systems change | modeling paradigms | modeling paradigms | cumulative knowledge | cumulative knowledge | empirical data generation | empirical data generation | boundary setting | boundary setting | network models | network models | policy evaluation | policy evaluation

License

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16.31 Feedback Control Systems (MIT) 16.31 Feedback Control Systems (MIT)

Description

This course covers the fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, the student should be able to design controllers using state-space methods and evaluate whether these controllers are robust. This course covers the fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, the student should be able to design controllers using state-space methods and evaluate whether these controllers are robust.

Subjects

linear system response | linear system response | aircraft control | aircraft control | frequency response methods | frequency response methods | Nyquist stability theorem | Nyquist stability theorem | bode plots | bode plots | state-space systems | state-space systems | full-state feedback control | full-state feedback control | open-loop estimators | open-loop estimators | closed-loop estimators | closed-loop estimators | robustness analysis | robustness analysis | small gain theorem | small gain theorem

License

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ESD.84 Engineering Systems Doctoral Seminar (MIT) ESD.84 Engineering Systems Doctoral Seminar (MIT)

Description

Examines core theory and contextual applications of the emerging field of Engineering Systems. The focus is on doctoral-level analysis of scholarship on key concepts such as complexity, uncertainty, fragility, and robustness, as well as a critical look at the historical roots of the field and related areas such as systems engineering, systems dynamics, agent modeling, and systems simulations. Contextual applications range from aerospace to technology implementation to regulatory systems to large-scale systems change. Special attention is given to the interdependence of social and technical dimensions of engineering systems. Examines core theory and contextual applications of the emerging field of Engineering Systems. The focus is on doctoral-level analysis of scholarship on key concepts such as complexity, uncertainty, fragility, and robustness, as well as a critical look at the historical roots of the field and related areas such as systems engineering, systems dynamics, agent modeling, and systems simulations. Contextual applications range from aerospace to technology implementation to regulatory systems to large-scale systems change. Special attention is given to the interdependence of social and technical dimensions of engineering systems.

Subjects

engineering systems | engineering systems | complexity | complexity | fragility | fragility | robustness | robustness | systems engineering | systems engineering | systems dynamics | systems dynamics | agent modeling | agent modeling | systems simulations | systems simulations | large-scale systems change | large-scale systems change | uncertainty | uncertainty

License

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2.007 Design and Manufacturing I (MIT) 2.007 Design and Manufacturing I (MIT)

Description

This course develops students' competence and self-confidence as design engineers. Emphasis is on the creative design process bolstered by application of physical laws, and learning to complete projects on schedule and within budget. Synthesis, analysis, design robustness and manufacturability are emphasized. The subject relies on active learning via a major design-and-build project. Lecture topics include idea generation, estimation, concept selection, visual thinking and communication, kinematics of mechanisms, machine elements, design for manufacturing, basic electronics, and professional responsibilities and ethics. A required on-line evaluation is given at the beginning and the end of the course so students can assess their design knowledge. This course develops students' competence and self-confidence as design engineers. Emphasis is on the creative design process bolstered by application of physical laws, and learning to complete projects on schedule and within budget. Synthesis, analysis, design robustness and manufacturability are emphasized. The subject relies on active learning via a major design-and-build project. Lecture topics include idea generation, estimation, concept selection, visual thinking and communication, kinematics of mechanisms, machine elements, design for manufacturing, basic electronics, and professional responsibilities and ethics. A required on-line evaluation is given at the beginning and the end of the course so students can assess their design knowledge.

Subjects

creative design process | creative design process | Synthesis | Synthesis | analysis | analysis | design robustness | design robustness | manufacturability | manufacturability | active learning | active learning | idea generation | idea generation | estimation | estimation | concept selection | concept selection | visual thinking | visual thinking | kinematics | kinematics | machine elements | machine elements

License

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2.007 Design and Manufacturing I (MIT) 2.007 Design and Manufacturing I (MIT)

Description

Develops students' competence and self-confidence as design engineers. Emphasis on the creative design process bolstered by application of physical laws, and learning to complete projects on schedule and within budget. Synthesis, analysis, design robustness and manufacturability are emphasized. Subject relies on active learning via a major design-and-build project. Lecture topics include idea generation, estimation, concept selection, visual thinking and communication, kinematics of mechanisms, machine elements, design for manufacturing, basic electronics, and professional responsibilities and ethics. A required on-line evaluation is given at the beginning and the end of the course so students can assess their design knowledge. Develops students' competence and self-confidence as design engineers. Emphasis on the creative design process bolstered by application of physical laws, and learning to complete projects on schedule and within budget. Synthesis, analysis, design robustness and manufacturability are emphasized. Subject relies on active learning via a major design-and-build project. Lecture topics include idea generation, estimation, concept selection, visual thinking and communication, kinematics of mechanisms, machine elements, design for manufacturing, basic electronics, and professional responsibilities and ethics. A required on-line evaluation is given at the beginning and the end of the course so students can assess their design knowledge.

Subjects

creative design process | creative design process | Synthesis | Synthesis | analysis | analysis | design robustness | design robustness | manufacturability | manufacturability | active learning | active learning | idea generation | idea generation | estimation | estimation | concept selection | concept selection | visual thinking | visual thinking | kinematics | kinematics | machine elements | machine elements

License

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2.007 Design and Manufacturing I (MIT) 2.007 Design and Manufacturing I (MIT)

Description

Includes audio/video content: AV special element video. Welcome to 2.007! This course is a first subject in engineering design. With your help, this course will be a great learning experience exposing you to interesting material, challenging you to think deeply, and providing skills useful in professional practice. A major element of the course is design of a robot to participate in a challenge that changes from year to year. This year, the theme is cleaning up the planet as inspired by the movie Wall-E.From its beginnings in 1970, the 2.007 final project competition has grown into an Olympics of engineering.  See this MIT News story for more background, a photo gallery, and videos about this course. Includes audio/video content: AV special element video. Welcome to 2.007! This course is a first subject in engineering design. With your help, this course will be a great learning experience exposing you to interesting material, challenging you to think deeply, and providing skills useful in professional practice. A major element of the course is design of a robot to participate in a challenge that changes from year to year. This year, the theme is cleaning up the planet as inspired by the movie Wall-E.From its beginnings in 1970, the 2.007 final project competition has grown into an Olympics of engineering.  See this MIT News story for more background, a photo gallery, and videos about this course.

Subjects

engineering design | engineering design | synthesis | synthesis | analysis | analysis | robustness | robustness | manufacturability | manufacturability | active learning | active learning | idea generation | idea generation | estimation | estimation | materials selection | materials selection | visual thinking | visual thinking | kinematics | kinematics | machine elements | machine elements | robotics | robotics | mechanical engineering | mechanical engineering | student work | student work | contest | contest

License

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2.830J Control of Manufacturing Processes (SMA 6303) (MIT) 2.830J Control of Manufacturing Processes (SMA 6303) (MIT)

Description

Includes audio/video content: AV special element video, AV lectures. This course explores statistical modeling and control in manufacturing processes. Topics include the use of experimental design and response surface modeling to understand manufacturing process physics, as well as defect and parametric yield modeling and optimization. Various forms of process control, including statistical process control, run by run and adaptive control, and real-time feedback control, are covered. Application contexts include semiconductor manufacturing, conventional metal and polymer processing, and emerging micro-nano manufacturing processes. Includes audio/video content: AV special element video, AV lectures. This course explores statistical modeling and control in manufacturing processes. Topics include the use of experimental design and response surface modeling to understand manufacturing process physics, as well as defect and parametric yield modeling and optimization. Various forms of process control, including statistical process control, run by run and adaptive control, and real-time feedback control, are covered. Application contexts include semiconductor manufacturing, conventional metal and polymer processing, and emerging micro-nano manufacturing processes.

Subjects

2.830 | 2.830 | 6.780 | 6.780 | ESD.63 | ESD.63 | Process control | Process control | manufacturing process | manufacturing process | discrete system feedback control theory | discrete system feedback control theory | empirical and adaptive modeling | empirical and adaptive modeling | off-line optimization | off-line optimization | statistical process control | statistical process control | real-time control. | real-time control. | real-time control | real-time control | one-factor-at-a-time | one-factor-at-a-time | robustness | robustness | Shewhart Hypothesis | Shewhart Hypothesis | semiconductor manufacturing | semiconductor manufacturing

License

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1.206J Airline Schedule Planning (MIT) 1.206J Airline Schedule Planning (MIT)

Description

Explores a variety of models and optimization techniques for the solution of airline schedule planning and operations problems. Schedule design, fleet assignment, aircraft maintenance routing, crew scheduling, passenger mix, and other topics are covered. Recent models and algorithms addressing issues of model integration, robustness, and operations recovery are introduced. Modeling and solution techniques designed specifically for large-scale problems, and state-of-the-art applications of these techniques to airline problems are detailed. Explores a variety of models and optimization techniques for the solution of airline schedule planning and operations problems. Schedule design, fleet assignment, aircraft maintenance routing, crew scheduling, passenger mix, and other topics are covered. Recent models and algorithms addressing issues of model integration, robustness, and operations recovery are introduced. Modeling and solution techniques designed specifically for large-scale problems, and state-of-the-art applications of these techniques to airline problems are detailed.

Subjects

Airline Schedule Planning | Airline Schedule Planning | Optimization | Optimization | Operations | Operations | Fleet Assignment | Fleet Assignment | Aircraft Maintenance Routing | Aircraft Maintenance Routing | Crew Scheduling | Crew Scheduling | Passenger Mix | Passenger Mix | Model Integration | Model Integration | Robustness | Robustness | Operations Recovery | Operations Recovery | models | models | optimization techniques | optimization techniques | airline schedule planning problems | airline schedule planning problems | schedule design | schedule design | fleet assignment | fleet assignment | aircraft maintenance routing | aircraft maintenance routing | crew scheduling | crew scheduling | robust planning | robust planning | passenger mix | passenger mix | integrated schedule planning | integrated schedule planning | solution techniques | solution techniques | decomposition | decomposition | Lagrangian relaxation | Lagrangian relaxation | column generation | column generation | partitioning | partitioning | applications | applications | algorithms | algorithms | model integration | model integration | robustness | robustness | operations recovery | operations recovery | airline schedule planning | airline schedule planning | 16.77 | 16.77 | ESD.215 | ESD.215

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6.435 System Identification (MIT) 6.435 System Identification (MIT)

Description

This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues. This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues.

Subjects

mathematical models | mathematical models | time series | time series | state-space | state-space | input-output models | input-output models | model structures | model structures | parametrization | parametrization | identifiability | identifiability | non-parametric methods | non-parametric methods | prediction error | prediction error | parameter estimation | parameter estimation | convergence | convergence | consistency | consistency | andasymptotic distribution | andasymptotic distribution | maximum likelihood estimation | maximum likelihood estimation | recursive estimation | recursive estimation | Kalman filters | Kalman filters | structure determination | structure determination | order estimation | order estimation | Akaike criterion | Akaike criterion | bounded noise models | bounded noise models | robustness | robustness

License

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7.342 Systems Biology: Stochastic Processes and Biological Robustness (MIT) 7.342 Systems Biology: Stochastic Processes and Biological Robustness (MIT)

Description

In this seminar, we will discuss some of the main themes that have arisen in the field of systems biology, including the concepts of robustness, stochastic cell-to-cell variability, and the evolution of molecular interactions within complex networks. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching. In this seminar, we will discuss some of the main themes that have arisen in the field of systems biology, including the concepts of robustness, stochastic cell-to-cell variability, and the evolution of molecular interactions within complex networks. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

Subjects

systems biology | systems biology | synthetic networks | synthetic networks | noise | noise | gene expression | gene expression | oscillators | oscillators | PCR | PCR | stochastic | stochastic | robustness | robustness | biological networks | biological networks | chemotaxis | chemotaxis | circadian | circadian

License

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14.147 Topics in Game Theory (MIT) 14.147 Topics in Game Theory (MIT)

Description

This course is an advanced topics course on market and mechanism design. We will study existing or new market institutions, understand their properties, and think about whether they can be re-engineered or improved. Topics discussed include mechanism design, auction theory, one-sided matching in house allocation, two-sided matching, stochastic matching mechanisms, student assignment, and school choice. This course is an advanced topics course on market and mechanism design. We will study existing or new market institutions, understand their properties, and think about whether they can be re-engineered or improved. Topics discussed include mechanism design, auction theory, one-sided matching in house allocation, two-sided matching, stochastic matching mechanisms, student assignment, and school choice.

Subjects

game theory | game theory | mechanism design | mechanism design | auction theory | auction theory | one-sided matching | one-sided matching | house allocation | house allocation | market problems | market problems | two-sided matching | two-sided matching | stability | stability | many-to-one | many-to-one | one-to-one | one-to-one | small cores | small cores | large markets | large markets | stochastic matching mechanisms | stochastic matching mechanisms | student assignment | student assignment | school choice | school choice | resale markets | resale markets | dynamics | dynamics | simplicity | simplicity | robustness | robustness | limited rationality | limited rationality | message spaces | message spaces | sharing risk | sharing risk | decentralized exchanges | decentralized exchanges | over-the-counter exchanges | over-the-counter exchanges

License

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16.30 Feedback Control Systems (MIT) 16.30 Feedback Control Systems (MIT)

Description

This course will teach fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, you should be able to design controllers using state-space methods and evaluate whether these controllers are robust to some types of modeling errors and nonlinearities. You will learn to: Design controllers using state-space methods and analyze using classical tools. Understand impact of implementation issues (nonlinearity, delay). Indicate the robustness of your control design. Linearize a nonlinear system, and analyze stability. This course will teach fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, you should be able to design controllers using state-space methods and evaluate whether these controllers are robust to some types of modeling errors and nonlinearities. You will learn to: Design controllers using state-space methods and analyze using classical tools. Understand impact of implementation issues (nonlinearity, delay). Indicate the robustness of your control design. Linearize a nonlinear system, and analyze stability.

Subjects

control design | control design | control analysis | control analysis | state-space methods | state-space methods | linear systems | linear systems | estimation filters | estimation filters | dynamic output feedback | dynamic output feedback | full state feedback | full state feedback | state estimation | state estimation | output feedback | output feedback | nonlinear analysis | nonlinear analysis | model uncertainty | model uncertainty | robustness | robustness

License

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16.323 Principles of Optimal Control (MIT) 16.323 Principles of Optimal Control (MIT)

Description

This course studies basic optimization and the principles of optimal control. It considers deterministic and stochastic problems for both discrete and continuous systems. The course covers solution methods including numerical search algorithms, model predictive control, dynamic programming, variational calculus, and approaches based on Pontryagin's maximum principle, and it includes many examples and applications of the theory. This course studies basic optimization and the principles of optimal control. It considers deterministic and stochastic problems for both discrete and continuous systems. The course covers solution methods including numerical search algorithms, model predictive control, dynamic programming, variational calculus, and approaches based on Pontryagin's maximum principle, and it includes many examples and applications of the theory.

Subjects

nonlinear optimization | nonlinear optimization | dynamic programming | dynamic programming | HJB Equation | HJB Equation | calculus of variations | calculus of variations | constrained optimal control | constrained optimal control | singular arcs | singular arcs | stochastic optimal control | stochastic optimal control | LQG robustness | LQG robustness | feedback control systems | feedback control systems | model predictive control | model predictive control | line search methods | line search methods | Lagrange multipliers | Lagrange multipliers | discrete LQR | discrete LQR

License

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16.892J Space System Architecture and Design (MIT) 16.892J Space System Architecture and Design (MIT)

Description

Space System Architecture and Design incorporates lectures, readings and discussion on topics in the architecting of space systems. The class reviews existing space system architectures and the classical methods of designing them. Sessions focus on multi-attribute utility theory as a new design paradigm for space systems, when combined with integrated concurrent engineering and efficient searches of large architectural tradespaces. Designing for flexibility and uncertainty is considered, as are policy and product development issues. Space System Architecture and Design incorporates lectures, readings and discussion on topics in the architecting of space systems. The class reviews existing space system architectures and the classical methods of designing them. Sessions focus on multi-attribute utility theory as a new design paradigm for space systems, when combined with integrated concurrent engineering and efficient searches of large architectural tradespaces. Designing for flexibility and uncertainty is considered, as are policy and product development issues.

Subjects

space system | space system | space system architecture | space system architecture | space architecting | space architecting | uncertainties | uncertainties | space policy | space policy | robustness | robustness | flexibility | flexibility | optimality | optimality | tradespace analysis | tradespace analysis | quality function deployment | quality function deployment | multi-attribute utility theory | multi-attribute utility theory | n-squared | n-squared | design structure matrix | design structure matrix | multi-attribution tradespace exploration | multi-attribution tradespace exploration | MATE | MATE | MATE-CON | MATE-CON | satellite | satellite | classes of space system | classes of space system | XTOS | XTOS | spacetug | spacetug | GINA | GINA | pareto fronts | pareto fronts | engineering design process | engineering design process | optimization methods | optimization methods | genetic algorithms | genetic algorithms | simulated annealing | simulated annealing | MMDOSA | MMDOSA | distributed space systems design optimization | distributed space systems design optimization | clarity test | clarity test | taxonomy of uncertainty | taxonomy of uncertainty | treatment of uncertainty | treatment of uncertainty | irreducible uncertainty | irreducible uncertainty | portfolio theory | portfolio theory | portfolio applications | portfolio applications | taxonomy of flexibility | taxonomy of flexibility | on-orbit servicing | on-orbit servicing | US national space policy | US national space policy | space policy heuristics | space policy heuristics | policy architectures | policy architectures | 16.892 | 16.892 | ESD.353 | ESD.353

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18.465 Topics in Statistics: Nonparametrics and Robustness (MIT) 18.465 Topics in Statistics: Nonparametrics and Robustness (MIT)

Description

This graduate-level course focuses on one-dimensional nonparametric statistics developed mainly from around 1945 and deals with order statistics and ranks, allowing very general distributions. For multidimensional nonparametric statistics, an early approach was to choose a fixed coordinate system and work with order statistics and ranks in each coordinate. A more modern method, to be followed in this course, is to look for rotationally or affine invariant procedures. These can be based on empirical processes as in computer learning theory. Robustness, which developed mainly from around 1964, provides methods that are resistant to errors or outliers in the data, which can be arbitrarily large. Nonparametric methods tend to be robust. This graduate-level course focuses on one-dimensional nonparametric statistics developed mainly from around 1945 and deals with order statistics and ranks, allowing very general distributions. For multidimensional nonparametric statistics, an early approach was to choose a fixed coordinate system and work with order statistics and ranks in each coordinate. A more modern method, to be followed in this course, is to look for rotationally or affine invariant procedures. These can be based on empirical processes as in computer learning theory. Robustness, which developed mainly from around 1964, provides methods that are resistant to errors or outliers in the data, which can be arbitrarily large. Nonparametric methods tend to be robust.

Subjects

Rank Tests | Rank Tests | Robustness | Robustness | M-estimation | M-estimation | Multivariate robustness | Multivariate robustness | VC combinatorics | VC combinatorics | Nonparametric classification | Nonparametric classification

License

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ESD.83 Doctoral Seminar in Engineering Systems (MIT) ESD.83 Doctoral Seminar in Engineering Systems (MIT)

Description

ESD.83 Doctoral Seminar in Engineering Systems examines the core theory and contextual applications of the emerging field of Engineering Systems. There is a focus on doctoral–level analysis of scholarship on key concepts such as complexity, uncertainty, fragility, and robustness, as well as a critical look at the historical roots of the field and related areas such as systems engineering, systems dynamics, agent modeling, and systems simulations. Contextual applications of the course range from aerospace to technology implementation to regulatory systems to large–scale systems change. Special attention is given to the interdependence of social and technical dimensions of engineering systems. ESD.83 Doctoral Seminar in Engineering Systems examines the core theory and contextual applications of the emerging field of Engineering Systems. There is a focus on doctoral–level analysis of scholarship on key concepts such as complexity, uncertainty, fragility, and robustness, as well as a critical look at the historical roots of the field and related areas such as systems engineering, systems dynamics, agent modeling, and systems simulations. Contextual applications of the course range from aerospace to technology implementation to regulatory systems to large–scale systems change. Special attention is given to the interdependence of social and technical dimensions of engineering systems.

Subjects

engineering systems | engineering systems | complexity | complexity | uncertainty | uncertainty | fragility | fragility | robustness | robustness | systems engineering | systems engineering | systems dynamics | systems dynamics | agent modeling | agent modeling | systems simulations | systems simulations | large-scale systems change | large-scale systems change | modeling paradigms | modeling paradigms | cumulative knowledge | cumulative knowledge | empirical data generation | empirical data generation | boundary setting | boundary setting | network models | network models | policy evaluation | policy evaluation

License

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ESD.342 Advanced System Architecture (MIT) ESD.342 Advanced System Architecture (MIT)

Description

This course provides a deep understanding of engineering systems at a level intended for research on complex engineering systems. It provides a review and extension of what is known about system architecture and complexity from a theoretical point of view while examining the origins of and recent developments in the field. The class considers how and where the theory has been applied, and uses key analytical methods proposed. Students examine the level of observational (qualitative and quantitative) understanding necessary for successful use of the theoretical framework for a specific engineering system. Case studies apply the theory and principles to engineering systems. This course provides a deep understanding of engineering systems at a level intended for research on complex engineering systems. It provides a review and extension of what is known about system architecture and complexity from a theoretical point of view while examining the origins of and recent developments in the field. The class considers how and where the theory has been applied, and uses key analytical methods proposed. Students examine the level of observational (qualitative and quantitative) understanding necessary for successful use of the theoretical framework for a specific engineering system. Case studies apply the theory and principles to engineering systems.

Subjects

DSM | DSM | SDM | SDM | structured design methodology | structured design methodology | graph | graph | network | network | hierarchy | hierarchy | structure | structure | social network | social network | abstraction | abstraction | motif | motif | modularity | modularity | coarse-graining | coarse-graining | Milgram | Milgram | scaling | scaling | scalability | scalability | organization | organization | organizational theory | organizational theory | internet | internet | air transport | air transport | taxonomy | taxonomy | computational biology | computational biology | complexity | complexity | power law | power law | Pareto | Pareto | Zipf | Zipf | epidemic | epidemic | navigation | navigation | fractal | fractal | size | size | robustness | robustness

License

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16.412J Cognitive Robotics (MIT)

Description

Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.

Subjects

cognitive robotics | robotic systems | intelligence algorithms | robustness algorithms | intelligence paradigms | robustness paradigms | autonomous robots | mars explorers | cooperative air vehicles | embedded devices | real-time deduction | real-time search | temporal planning | decision-theoretic planning | contingency planning | dynamic execution | dynamics re-planning | reasoning | path planning | reasoning under uncertainty | mapping | localization | cooperative robotics | distributed robotics | mars exploration rover | nursebot | museum tourguide | human-interaction systems

License

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Robust pro-poorest poverty reduction with counting measures: the anonymous case

Description

The talk explores conditions under which a poverty reduction experience is robustly more “pro-poor” than another one, in the context of counting measures of multidimensional poverty

Subjects

Gaston Yalonetzky | Jos ́e Gallegos | pro-poor | peru | poverty reduction | multidimensional poverty measure | robustness | Gaston Yalonetzky | Jos ́e Gallegos | pro-poor | peru | poverty reduction | multidimensional poverty measure | robustness | 2014-12-01

License

http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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Robust pro-poorest poverty reduction with counting measures: the anonymous case

Description

The talk explores conditions under which a poverty reduction experience is robustly more ?pro-poor? than another one, in the context of counting measures of multidimensional poverty Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

Gaston Yalonetzky | ?e Gallegos | pro-poor | peru | poverty reduction | multidimensional poverty measure | robustness | ?e Gallegos | pro-poor | peru | poverty reduction | multidimensional poverty measure | robustness | 2014-12-01

License

http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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2.18 Biomolecular Feedback Systems (MIT) 2.18 Biomolecular Feedback Systems (MIT)

Description

This course focuses on feedback control mechanisms that living organisms implement at the molecular level to execute their functions, with emphasis on techniques to design novel systems with prescribed behaviors. Students will learn how biological functions can be understood and designed using notions from feedback control. This course focuses on feedback control mechanisms that living organisms implement at the molecular level to execute their functions, with emphasis on techniques to design novel systems with prescribed behaviors. Students will learn how biological functions can be understood and designed using notions from feedback control.

Subjects

biomolecular feedback systems | biomolecular feedback systems | systems biology | systems biology | modeling | modeling | feedback | feedback | cell | cell | system | system | control | control | dynamical | dynamical | input/output | input/output | synthetic biology | synthetic biology | techniques | techniques | transcription | transcription | translation | translation | transcriptional regulation | transcriptional regulation | post-transcriptional regulation | post-transcriptional regulation | cellular subsystems | cellular subsystems | dynamic behavior | dynamic behavior | analysis | analysis | equilibrium | equilibrium | robustness | robustness | oscillatory behavior | oscillatory behavior | bifurcations | bifurcations | model reduction | model reduction | stochastic | stochastic | biochemical | biochemical | simulation | simulation | linear | linear | circuit | circuit | design | design | biological circuit design | biological circuit design | negative autoregulation | negative autoregulation | toggle switch | toggle switch | repressilator | repressilator | activator-repressor clock | activator-repressor clock | IFFL | IFFL | incoherent feedforward loop | incoherent feedforward loop | bacterial chemotaxis | bacterial chemotaxis | interconnecting components | interconnecting components | modularity | modularity | retroactivity | retroactivity | gene circuit | gene circuit

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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16.412J Cognitive Robotics (MIT)

Description

Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.Technical RequirementsAny text editor can be used to view the .ascii, .binary, .map, and .pddl files found on this course site. Any number of development tools can be used to compile and run the .c and .h files found on this course site.

Subjects

cognitive robotics | robotic systems | intelligence algorithms | robustness algorithms | intelligence paradigms | robustness paradigms | autonomous robots | mars explorers | cooperative air vehicles | embedded devices | real-time deduction | real-time search | temporal planning | decision-theoretic planning | contingency planning | dynamic execution | dynamics re-planning | reasoning | path planning | reasoning under uncertainty | mapping | localization | cooperative robotics | distributed robotics | mars exploration rover | nursebot | museum tourguide | human-interaction systems | navigation | state-aware robots | fast planning | cooperative planning | vision-based exploration | preplanning | 16.412 | 6.834

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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16.412J Cognitive Robotics (MIT)

Description

Cognitive robotics addresses the emerging field of autonomous systems possessing artificial reasoning skills. Successfully-applied algorithms and autonomy models form the basis for study, and provide students an opportunity to design such a system as part of their class project. Theory and application are linked through discussion of real systems such as the Mars Exploration Rover.

Subjects

cognitive robotics | robotic systems | intelligence algorithms | robustness algorithms | intelligence paradigms | robustness paradigms | autonomous robots | mars explorers | cooperative air vehicles | embedded devices | real-time deduction | real-time search | temporal planning | decision-theoretic planning | contingency planning | dynamic execution | dynamics re-planning | reasoning | path planning | reasoning under uncertainty | mapping | localization | cooperative robotics | distributed robotics | mars exploration rover | nursebot | museum tourguide | human-interaction systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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