Session Overview
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We've been talking about how to analyze and design systems, but we haven't talked about how to make systems robust under uncertainty. In fact, we haven't even talked about how to model uncertainty. In this unit, we'll address the problem that systems we design may have to operate under uncertainty, and that we may want those systems to be able to search the world for possible solutions to problems. We'll introduce the basics of probability and search in this session, and apply those concepts to our design challenges. The overview handout provides a more detailed introduction, including the big ideas of the session, key vocabulary, what you should understand (theory) and be able to do (practice) after completing this session, and additional resources. |
Session Content
Readings
Read sections 7.1-7.4 of the course notes.
Lecture Video
Watch the lecture video. The handout and slides present the same material, but the slides include answers to the in-class questions.
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Lecture 10: Discrete Probability and State Estimation (01:17:48)
Lecture 10: Discrete Probability and State Estimation
> Download from iTunes U (MP4 - 191MB)
> Download from Internet Archive (MP4 - 191MB)
About this Video
Introduction to probability theory, with the goals of making precise statements about uncertain situations and drawing reliable inferences from unreliable observations. A hidden Markov model is then applied to robot navigation.
Recitation Video
These videos have been developed for OCW Scholar, and are designed to supplement the lecture videos.
Session Activities
The problems in the tables below are taken from the 6.01 Online Tutor, an interactive environment that is not available on OCW. Do not try to answer these questions in the PDF files; answers will not be checked, and cannot be submitted.
Software Lab
- Software Lab 10: Distributions (PDF)
- Code for Software Lab 10 (ZIP) (This ZIP file contains: 1 .py file.)
| PROBLEM # | QUESTIONS |
|---|---|
| 10.1.1 | Probability distributions: DDist (PDF) |
| 10.1.2 | Conditional distributions (PDF) |
| 10.1.3 | Joint distributions (PDF) |
| 10.1.4 | Operations on conditional distributions (PDF) |
| 10.1.5 | Where are you? (PDF) |
| 10.1.6 | Implementing joint distributions (PDF) |
| 10.1.7 | Implementing operations on conditional distributions (PDF) |
Design Lab
- Design Lab 10: Robot Pets (PDF)
- Code for Design Lab 10 (ZIP) (This resource contains: 1 .py file and 1 .pyc file.)
Additional Exercises
| PROBLEM # | QUESTIONS |
|---|---|
| 10.3.1 | A distribution (PDF) |
| 10.3.2 | Summing to 1 (PDF) |
| 10.3.3 | Truth (PDF) |
| 10.3.4 | Equivalence (PDF) |
| 10.3.5 | Buying a car (PDF) |