MIT OpenCourseWare

Browse materialsMIT 16.410 · Fall 2010

Principles of Autonomy and Decision Making

Subjects: Artificial Intelligence · Game Theory · Science

Art: Ottomar Anschütz, Lilienthal im unruhigen Gleitflug, 1894 · Public domain

Instructors: Prof. Brian Charles Williams, Prof. Emilio Frazzoli
Term: Fall 2010
Level: Undergraduate
What's inside: 1 lecture note · 1 reading · 83 other files · 7 web pages
Materials open: MIT OCW

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About this course

Source: MIT OpenCourseWare 16.410 Principles of Autonomy and Decision Making, Fall 2010, Prof. Brian Charles Williams, Prof. Emilio Frazzoli — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

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Has MIT solutions 11 files in this course are named by MIT as solutions.

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Prerequisites are not listed here. Check MIT's own syllabus for this course.

How this course was organized

What's here, and what isn't

A lecture sequence of 25 items is confirmed. Assignment and exam materials exist too, but MIT's own order for them could not be verified, so they're shown as materials below rather than a sequence.

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Grouped by section, as MIT organized them. Not sequenced or ordered.

Readings

Recurrences 6.046 Lecture Notes: Asymptotic analysis (Courtesy of Erik Demaine. Used with permission.)PDF Download

Lecture Notes

31 files

Recitations

20 files

Assignments

27 files

Exams

MIT OpenCourseWare, Prof. Brian Charles Williams, Prof. Emilio Frazzoli, 16.410 Principles of Autonomy and Decision Making, Fall 2010.

License: CC BY-NC-SA 4.0 · Edition: Fall 2010 · Open the original course on OCW ↗

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Principles of Autonomy and Decision Making

Before you start

What's here, and what isn't

Not included here

Materials by section

Readings

Lecture Notes

Recitations

Assignments

Exams

Lecture 1: Introduction [BW]

Lecture 2: Foundations I: state space search [BW]

Lecture 3: Foundations II: complexity of state space search [BW]

Lecture 4: Foundations III: soundness and completeness of search [SK]

Lecture 5: Constraints I: constraint programming [BW]

Lecture 6: Constraints II: constraint satisfaction [BW]

Lecture 7: Constraints III: conflict-directed back jumping Planning I: activity planning [BW]

Lecture 8: Planning II: graph plan [BW]

Lecture 9: Planning III: plan termination and plan execution [BW]

Lecture 10: Model-based reasoning I: propositional logic and satisfiability [BW]

Lecture 11: Encoding planning problems as propositional logic satisfiability [SK]

Lecture 12: Model-based reasoning II: diagnosis and mode estimation [BW]

Lecture 13: Model-based reasoning III: OpSat and conflict-directed A* [BW]

Lecture 14: Global path planning I: informed search [EF]

Lecture 15: Global path planning II: sampling-based algorithms for motion planning [EF]

Lecture 16: Mathematical programming I [EF]

Lecture 17: Mathematical programming II: the simplex method [EF]

Lecture 18: Mathematical programming III: (mixed-integer) linear programming for vehicle routing and motion planning [EF]

Lecture 19: Reasoning in an uncertain world [BW]

Lecture 20: Inferring state in an uncertain world I: introduction to hidden Markov models [EF]

Lecture 21: Inferring state in an uncertain world II: hidden Markov models, the Baum-Welch algorithm [EF]

Lecture 22: Dynamic programming and machine learning I: Markov decision processes [EF]

Lecture 23: Dynamic programming and machine learning II: Markov decision processes, policy iteration [EF]

Lecture 24: Game theory I: sequential games [EF]

Lecture 25: Game theory II: differential games [SK]