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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

Course Curriculum

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 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
Certification 00:00:00

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