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Course Criteria
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3.00 Credits
Survey of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. Focus on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.
Prerequisite:
Prereq: 1.00 or 6.01
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3.00 Credits
Algorithms and paradigms for creating a wide range of robotic systems that act intelligently and robustly, by reasoning extensively from models of themselves and their world. Examples range from autonomous Mars explorers and cooperative air vehicles, to everyday embedded devices. Topics include deduction and search in real-time; temporal, decision-theoretic and contingency planning; dynamic execution and re-planning; reasoning about hidden state and failures; reasoning under uncertainty, path planning, mapping and localization, and cooperative and distributed robotics. 8 Engineering Design Points.
Prerequisite:
Prereq: 6.041, 6.042, or 16.09; 16.410, 16.413, 6.034, or 6.825
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3.00 Credits
Graduate-level version of 16.410; see description under 16.410. Additional material on reasoning under uncertainty and machine learning, including hidden Markov models, graphical models and Bayesian networks, computational learning theory, reinforcement learning, decision tree learning and support vector machines. Assignments include the application of autonomy algorithms to practical aerospace systems, as well as more advanced programming assignments.
Prerequisite:
Prereq: 1.00 or 6.01
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3.00 Credits
Concepts, principles, and methods for planning with imperfect knowledge. Topics include state estimation, planning in information space, partially observable Markov decision processes, reinforcement learning and planning with uncertain models. Students will develop an understanding of how different planning algorithms and solutions techniques are useful in different problem domains. Previous coursework in artificial intelligence and state estimation strongly recommended.
Prerequisite:
Prereq: 16.413
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3.00 Credits
Principles of supervisory control and telerobotics. Different levels of automation are discussed, as well as the allocation of roles and authority between humans and machines. Human-vehicle interface design in highly automated systems. Decision aiding. Trade-offs between human control and human monitoring. Automated alerting systems and human intervention in automatic operation. Enhanced human interface technologies such as virtual presence. Performance, optimization, and social implications of the human-automation system. Examples from aerospace, ground, and undersea vehicles, robotics, and industrial systems.
Prerequisite:
Prereq: Permission of instructor
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3.00 Credits
Fundamentals of human performance, physiology, and life support impacting engineering design and aerospace systems. Topics include effects of gravity on the muscle, skeletal, cardiovascular, and neurovestibular systems; human/pilot modeling and human/machine design; flight experiment design; and life support engineering for extravehicular activity (EVA). Case studies of current research are presented. Assignments include a design project, quantitative homework sets, and quizzes emphasizing engineering and systems aspects.
Prerequisite:
Prereq: 16.400, 16.06, 16.060, or permission of instructor
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3.00 Credits
Introduces sensory systems,and multi-sensory fusion using the vestibular and spatial orientation systems as a model. Topics range from end organ dynamics to neural responses, to sensory integration, to behavior, and adaptation, with particular application to balance, posture and locomotion under normal gravity and space conditions. Depending upon the background and interests of the students, advanced term project topics might include motion sickness, astronaut adaptation, artificial gravity, lunar surface locomotion, vestibulo-cardiovascular responses, vestibular neural prostheses, or other topics of interest.
Prerequisite:
Prereq: Neuroscience or systems engineering or permission of instructor
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3.00 Credits
Provides a fundamental understanding of the human factors that must be considered in the design and engineering of complex aviation and space systems. Focuses on the derivation of human engineering design criteria from sensory, motor and cognitive sources. Students, individually and in teams, apply design principles from topic areas including displays, controls and ergonomics, manual control, the nature of human error, basic experimental design, and human-computer interaction. Includes aviation accident case presentations and interactive projects. Graduate students also complete an additional research-oriented project with a final written report and oral presentation.
Prerequisite:
Prereq: 6.041 or permission of instructor
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3.00 Credits
Fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. Basic principles and algorithms for data acquisition, imaging, filtering, and feature extraction. Laboratory projects provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. 6 Engineering Design Points.
Prerequisite:
Prereq: 6.003, 2.004, 16.004, or 18.085
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0.00 Credits
Each term, the class selects a new set of professional journal articles on bioengineering topics of current research interest. Some papers are chosen because of particular content, others are selected because they illustrate important points of methodology. Each week, one student leads the discussion, evaluating the strengths, weaknesses, and importance of each paper. Subject may be repeated for credit a maximum of four terms. Letter grade given in the last term applies to all accumulated units of 16.459.
Prerequisite:
Prereq: None
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