Assignments

Course exercises follow the exercises/chNN/ sources from the aima book repository, rendered as pages in this site (see Textbook chapter exercises). Assignments are distributed via GitHub Classroom starter repositories.

Schedule: the list below is 24 class sessions in 8 weeks of 3 meetings each (see Syllabus and course/ains5001-8week-schedule.json); each session links the AIMA slide deck (lecture-NN) and matching chapter exercises.

Assignment Files: Each of the 24 sessions has a dedicated assignment page with exercises from the corresponding chapter: Assignments by Lecture

Exercises by lecture¶

The lecture column links to slide pages. For each textbook chapter covered in that session, this course site has a single page with the full exercise prompts: use the chapter index or the links below. Exercise numbers (C.N) follow Artificial Intelligence: A Modern Approach (4e), matching the exercises/chNN sources. GitHub Classroom usually assigns a subset; follow the instructor’s rubric.

Lecture 0: GitHub Classroom Assignments¶

• Textbook chapter exercises: none for this session (classroom setup). Use your Classroom invite and the starter template when assigned.

Lecture 1: Introduction¶

• Ch. 1 (Introduction) — all exercises in this book: 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 1.10 · 1.11, 1.12, 1.13, 1.14, 1.15, 1.16, 1.17, 1.18, 1.19, 1.20

Lecture 2: Intelligent Agents¶

• Ch. 2 (Intelligent Agents) — all exercises in this book: 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 2.10 · 2.11, 2.12, 2.13, 2.14, 2.15, 2.16

Lecture 3: Solving Problems by Searching¶

• Ch. 3 (Solving Problems by Searching) — all exercises in this book: 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10 · 3.12, 3.13, 3.14, 3.15, 3.16, 3.17, 3.18, 3.19, 3.20, 3.21 · 3.22, 3.23, 3.24, 3.25, 3.26, 3.27, 3.28, 3.29, 3.30, 3.31 · 3.32, 3.33, 3.34, 3.35, 3.36, 3.37, 3.38, 3.39, 3.40

Lecture 4: Search in Complex Environments¶

• Ch. 4 (Search in Complex Environments) — all exercises in this book: 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 4.10 · 4.11, 4.12, 4.13, 4.14, 4.15, 4.16, 4.17

Lecture 5: Adversarial Search and Games¶

• Ch. 5 (Adversarial Search and Games) — all exercises in this book: 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 5.10 · 5.11, 5.12, 5.13, 5.14, 5.15, 5.16, 5.17, 5.18, 5.19, 5.20 · 5.21, 5.22, 5.23, 5.24, 5.25

Lecture 6: Constraint Satisfaction Problems¶

• Ch. 6 (Constraint Satisfaction Problems) — all exercises in this book: 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 6.11 · 6.12, 6.13, 6.14, 6.15, 6.16, 6.17, 6.18, 6.19, 6.20, 6.100

Lecture 7: Logical Agents¶

• Ch. 7 (Logical Agents) — all exercises in this book: 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9, 7.10 · 7.11, 7.12, 7.13, 7.14, 7.15, 7.16, 7.17, 7.18, 7.19, 7.20 · 7.21, 7.22, 7.23, 7.24, 7.25, 7.26, 7.27, 7.28, 7.29, 7.30 · 7.31, 7.32, 7.33, 7.34, 7.35

Lecture 8: First-Order Logic¶

• Ch. 8 (First-Order Logic) — all exercises in this book: 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8, 8.9, 8.10 · 8.11, 8.12, 8.13, 8.14, 8.15, 8.16, 8.17, 8.18, 8.19, 8.20 · 8.21, 8.22, 8.23, 8.24, 8.25, 8.26, 8.27, 8.28, 8.29, 8.30 · 8.31, 8.32, 8.33, 8.34, 8.35, 8.36

Lecture 9: Inference in First-Order Logic¶

• Ch. 9 (Inference in First-Order Logic) — all exercises in this book: 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9, 9.10 · 9.11, 9.12, 9.13, 9.14, 9.15, 9.16, 9.17, 9.18, 9.19, 9.20 · 9.21, 9.22, 9.23, 9.24, 9.25, 9.26, 9.27, 9.28, 9.29, 9.30 · 9.31

Lecture 10: Knowledge Representation¶

• Ch. 10 (Knowledge Representation) — all exercises in this book: 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 10.10 · 10.11, 10.12, 10.13, 10.14, 10.15, 10.16, 10.17, 10.18

Lecture 11: Automated Planning¶

• Ch. 11 (Automated Planning) — all exercises in this book: 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 11.10 · 11.11, 11.12, 11.13, 11.14, 11.15

Lecture 12: Quantifying Uncertainty¶

• Ch. 12 (Quantifying Uncertainty) — all exercises in this book: 12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 12.10 · 12.11, 12.12, 12.13, 12.14, 12.15, 12.16, 12.17, 12.18, 12.19, 12.20 · 12.21, 12.22, 12.23, 12.24, 12.25, 12.26, 12.27, 12.28, 12.29, 12.30

Lecture 13: Probabilistic Reasoning¶

• Ch. 13 (Probabilistic Reasoning) — all exercises in this book: 13.1, 13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 13.10 · 13.11, 13.12, 13.13, 13.14, 13.15, 13.16, 13.17, 13.18, 13.19, 13.20 · 13.21, 13.22, 13.23, 13.24, 13.25, 13.26, 13.27, 13.28, 13.29, 13.30 · 13.31

Lecture 14: Probabilistic Reasoning over Time¶

• Ch. 14 (Probabilistic Reasoning over Time) — all exercises in this book: 14.1, 14.2, 14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 14.10 · 14.11, 14.12, 14.13, 14.14, 14.15, 14.16, 14.17, 14.18, 14.19, 14.20 · 14.21, 14.22, 14.23, 14.24

Lecture 15: Probabilistic Programming¶

• Ch. 15 (Probabilistic Programming) — all exercises in this book: 15.1, 15.2, 15.3, 15.4, 15.5, 15.6, 15.7, 15.8, 15.9, 15.10 · 15.11, 15.12, 15.13, 15.14, 15.15, 15.16, 15.17, 15.18, 15.19, 15.20

Lecture 16: Making Simple Decisions¶

• Ch. 16 (Making Simple Decisions) — all exercises in this book: 16.1, 16.2, 16.3, 16.4, 16.5, 16.6, 16.7, 16.8, 16.9, 16.10 · 16.11, 16.12, 16.13, 16.14, 16.15, 16.16, 16.17, 16.18, 16.19, 16.20 · 16.21, 16.22, 16.23

Lecture 17: Making Complex Decisions¶

• Ch. 17 (Making Complex Decisions) — all exercises in this book: 17.1, 17.2, 17.3, 17.4, 17.5, 17.6, 17.7, 17.8, 17.9, 17.10 · 17.11, 17.12, 17.13, 17.14, 17.16, 17.17, 17.18, 17.19, 17.20, 17.21 · 17.22, 17.23, 17.24, 17.25

Lecture 18: Multiagent Decision Making¶

• Ch. 18 (Multiagent Decision Making) — all exercises in this book: 18.1, 18.2, 18.3, 18.4, 18.5, 18.6, 18.7, 18.8, 18.9, 18.10 · 18.11, 18.12, 18.13, 18.14, 18.15, 18.16, 18.17, 18.18, 18.19, 18.20 · 18.21, 18.22, 18.23, 18.24, 18.25, 18.26, 18.27, 18.28, 18.29, 18.30 · 18.31, 18.32, 18.33

Lecture 19: Learning from Examples¶

• Ch. 19 (Learning from Examples) — all exercises in this book: 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7, 19.8

Lecture 20: Learning Probabilistic Models¶

• Ch. 20 (Learning Probabilistic Models) — all exercises in this book: 20.1, 20.2, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8, 20.9, 20.10 · 20.11

Lecture 21: Deep Learning¶

• Ch. 21 (Deep Learning) — all exercises in this book: 21.1, 21.2, 21.3, 21.4, 21.5, 21.6, 21.7, 21.8, 21.9, 21.10 · 21.11, 21.12, 21.13

Lecture 22: Reinforcement Learning¶

• Ch. 22 (Reinforcement Learning) — all exercises in this book: 22.1, 22.2, 22.3, 22.4, 22.5, 22.6, 22.7, 22.8, 22.9, 22.10 · 22.11

Lecture 23: Natural Language Processing¶

• Ch. 23 (Natural Language Processing) — all exercises in this book: 23.1, 23.2, 23.3, 23.4, 23.5, 23.6, 23.7, 23.8, 23.9, 23.10 · 23.11, 23.12, 23.13, 23.14, 23.15, 23.16, 23.17, 23.18, 23.19, 23.20 · 23.21, 23.22

Lecture 24: Deep Learning for Natural Language Processing¶

• Ch. 24 (Deep Learning for Natural Language Processing) — all exercises in this book: 24.1, 24.2, 24.3, 24.4, 24.5, 24.6, 24.7, 24.8

Lecture 25: Computer Vision¶

• Ch. 25 (Computer Vision) — all exercises in this book: 25.1, 25.2, 25.3, 25.4, 25.5, 25.6, 25.7, 25.8, 25.9, 25.10 · 25.11, 25.12

Lecture 26: Robotics¶

• Ch. 26 (Robotics) — all exercises in this book: 26.1, 26.2, 26.3, 26.4, 26.5, 26.6, 26.7, 26.8, 26.9, 26.10 · 26.11, 26.12

Lecture 27: Philosophy, Ethics, and Safety of AI¶

• Ch. 27 (Philosophy, Ethics, and Safety of AI) — exercises in this book (no exercise list in the build tree yet).

Lecture 28: The Future of AI¶

• Ch. 28 (The Future of AI) — exercises in this book (no exercise list in the build tree yet).

Accept each lecture’s Classroom invitation, open the repo in Codespaces, complete exercises, run tests, and push.

Same GitHub Classroom + Codespaces flow as other tracks. For Dialogic, the course AI should receive your code cell source and Jupyter outputs (stdout, stderr, tracebacks)—not just the assignment text—so questions like “why did this fail?” are grounded in your actual run. See Dialogic AI and exercise code cells. Optional template: templates/DIALOGIC_TUTOR.form.md in the exercise starter.