Artificial Intelligence, LP4, VT 2014

This is an old website! For the current version, visit: http://www.cse.chalmers.se/edu/course/TIN172/

Here are suggested exercises for each chapter in the course book. There are both exercises at the end of each chapter, as well as a list of practice exercises.

Note: Christos has put lecture notes and exercises on Piazza.

The practice exercises makes use of some Java programs. The programs are a bit old and they use “Java web start”. If you have problems getting them to work, you can download offline versions from here.

Note that these are just suggestions for exercises.

Example written examination

There is a list of example examination questions.

Update, August 2014: Now you can look at the solutions to the exam (PDF) and the re-exam (PDF).

Chapters 1–2: Introduction

These are introductory chapters with no “hard” content, but book exercises 1.3 and 2.1 should be good for initiating some interesting discussions.

Practice exercise 2.A should also be good to get you started.

Chapter 3: States and Searching

Recommended practice exercises:

• 3.A, 3.B, 3.C, 3.D

Recommended course book exercises:

• 3.2
• 3.3, but not (e)
• 3.4

Supplementary exercises

From the course book:

• 3.9, 3.11, 3.12

Here are some classical AI search problems, for which there are several different heuristics. For each (or any) of the problems, define a state space and one or several reasonable heuristic functions for solving the puzzle.

• 8/15 puzzle
• N-queens
• Tic-tac-toe
• Travelling salesman

Chapter 4: Features and Constraints

Recommended practice exercises:

• 4.A, 4.B, 4.C

Recommended course book exercises:

• 4.1, 4.2
• 4.3, but not (c) or (d)
• 4.4, 4.5
• 4.6, but skip (ii) and (iv)
• 4.12

Supplementary exercises

From the course book:

• 4.7, 4.9

A classical CSP exercise is the 4-colour theorem, not to prove it but to colour a given map (such as this) so that no neighbors share the same colour:

• Formulate the problem as a CSP, i.e., define the variables and the constraints. Try also to solve it by hand using arc consistency and domain splitting (perhaps starting with a simpler map such as this).

Chapter 8: Planning with Certainty

Recommended practice exercises:

• 8.A, 8.B, 8.C

Recommended course book exercises:

• 8.1, 8.2, 8.4
• 8.6, without implementation
• 8.7, 8.9, 8.10

Supplementary exercises

From the course book:

• 8.3, 8.5
• 8.6, with implementation
• 8.8, 8.11

If you are specially interested in the CSP or partial-order planner:

• 8.12 (CSP)
• 8.13, 8.14, 8.15 (POP)

Chapter 5: Propositions and Inference

Recommended practice exercise:

• 5.A

Recommended course book exercises:

• 5.3, 5.4
• 5.2, but you don’t have to test it in AILog if you haven’t installed it

Supplementary exercises

From the course book:

• 5.7, 5.8, 5.9 are exercises about section 5.4 (i.e., not really part of the course contents)

Chapters 6 + 9: Reasoning + Planning Under Uncertainty

Recommended course book exercises:

• 6.1
• 6.4
• 6.6 or 6.8

And from chapter 9:

• 9.1
• 9.7

Supplementary exercises, for the more ambitious

Note: These exercises are from a course on decision theory, so some of them are too complex for a general course in AI.

Probability theory refresher [PDF]:

• exercise 1
• exercise 2.1 and 2.2, (and 2.3 if you’re ambitious)
• exercise 4

Basic mathematical exercises for subjective probability and utility [PDF]:

• exercises 1–4

Advanced decision problems [PDF]:

• exercise 1
• exercise 2

Finally, a longer decision problem about insurance PDF].