Lecture Notes

All lecture notes, if available, are provided below.

Lecture notes files.

SES #	TOPICS
Module 1: Programs that Monitor State
1	Robustness Through Model-based Programming (PDF - 3.5MB)
2	Programs That Monitor Hidden State (PDF - 1.3MB)
Module 2: Program with Time
3	Programs with Flexible Time
4	Programs with Flexible Time and Choice (PDF - 1.4MB)
Module 3: Programs with Goal States
5	Programs on State and Planning as Heuristic Forward Search (PDF - 3.2MB)
6	Planning with Temporal Land Marks (PDF - 1.2MB)
7	Planning with Casual Graphs (PDF - 1.7MB)
8	Time-line Planning Using Casual Graphs (PDF - 2.4MB)
Module 4: Programs with Continuous State
9	Programs that Monitor Continuous State
10	Programs with Continuous Goal States
Module 5: Programs that Collaborate
11	Multi-agent Planning (PDF - 1.3MB)
12	Programs that Relax
13	Programs that Execute with Humans (PDF - 3.3MB)
Module 6: Advanced Lectures
14	Advanced Lecture 1: Incremental Path Planning (PDF - 3.0MB)
15	Advanced Lecture 2: Semantic Localization (PDF)
16	Advanced Lecture 3: Image Classification via Deep Learning (PDF - 4.2MB)
17	Advanced Lecture 4: Monte Carlo Tree Search (PDF - 2.0MB)
18	Advanced Lecture 5: Reachability (PDF - 5.5MB)
19	Advanced Lecture 6: Planning with Temporal Logic (PDF - 1.4MB)
20	Advanced Lecture 7: Probabilistic and Infinite Horizon Planning (PDF - 4.1MB)
Module 7: Risk-bounded Programming
21	Risk-bounded Programs with Flexible Time
22	Risk-bounded Programming on Continuous State I (PDF - 3.6MB)
23	Risk-bounded Programming on Continuous State II
24	Risk-bounded Programs with Choice
Module 8: Grand Challenge
25	Grand Challenge: Practice
26	Grand Challenge