The Autonomy and Decision Making Diploma course illustrates different types of reasoning, optimization and decision-making strategies for helping you to create highly autonomous systems and decision support aids.
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Autonomy and Decision Making
Diploma
( 9 REVIEWS ) 478 STUDENTS
The course illustrates different types of reasoning, optimization and decision-making strategies for
helping you to create highly autonomous systems and …
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FREE
1 YEAR
LEVEL 3 - DIPLOMA
COURSE BADGE
COURSE CERTIFICATE
29NUMBER OF UNITS
0NUMBER OF QUIZZES
12 HOURS, 50 MINUTES
HOME CURRICULUM REVIEWS
The Autonomy and Decision Making Diploma 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.
Assessment
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.
Certification
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assignment of the course as part of the assessment. Upon successful completion of a course, you can
choose to make your achievement formal by obtaining your Certi cate at a cost of £49.
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Course Credit: MIT
Introduction 00:30:00
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 con ict-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 satis ability 00:40:00
Model-based programming of robotic space explorers 00:15:00
Encoding planning problems as propositional logic satis ability 00:05:00
Model-based reasoning II diagnosis and mode estimation@ss 00:40:00
Model-based reasoning III OpSat and con ict-directed 00:40:00
COURSE CURRICULUM
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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
Assessment
Submit Your Assignment 00:00:00
Certi cation 00:00:00
COURSE REVIEWS
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