The [course_title] course illustrates different types of reasoning, optimization and decision-making strategies for helping you to create highly autonomous systems and decision support aids.
Reasoning part includes logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning while in the Optimization paradigm, you will learn about linear programming, integer programming, and dynamic programming.
Finally, decision theoretic planning and Markov decision processes will be discussed in decision-making section.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: MIT
|Foundations I state space search||00:40:00|
|Foundations II complexity of state space search||00:20:00|
|Foundations III soundness and completeness of search||00:30:00|
|Constraints I constraint programming||00:10:00|
|Constraints II constraint satisfaction||00:20:00|
|Constraints III conflict-directed back jumping||00:30:00|
|Planning I operator-based planning and plan graphs||00:20:00|
|Planning II plan extraction and analysis||00:20:00|
|Planning III robust execution of temporal plans||00:40:00|
|Model-based reasoning I propositional logic and satisfiability||00:40:00|
|Model-based programming of robotic space explorers||00:15:00|
|Encoding planning problems as propositional logic satisfiability||00:05:00|
|Model-based reasoning II diagnosis and mode estimation@ss||00:40:00|
|Model-based reasoning III OpSat and conflict-directed||00:40:00|
|Global path planning I informed search||00:50:00|
|Global path planning II sampling-based algorithms for motion planning||00:50:00|
|Mathematical programming I||00:25:00|
|Mathematical programming II the simplex method||00:40:00|
|Mathematical programming III (mixed-integer) linear programming for vehicle routing and motion planning||00:30:00|
|Reasoning in an uncertain world||00:30:00|
|Inferring state in an uncertain world I introduction to hidden Markov models||00:30:00|
|Inferring state in an uncertain world II hidden Markov models, the Baum-Welch algorithm||00:20:00|
|Dynamic programming and machine learning I Markov decision processes||00:15:00|
|Dynamic programming and machine learning II Markov decision processes, policy iteration||00:20:00|
|Game theory I sequential games||00:30:00|
|Game theory II differential games||00:30:00|
|Submit Your Assignment||00:00:00|
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