This is intended to give you a sense of what I think is important from
the course so far, and what I will be thinking of when creating the
exam.

I hate disclaimers, but here are some anyway. This is not a
contract. I may have inadvertently left something off this list that
ends up in an exam question. I make no guarantees that the exam will
be 100% limited to items listed below. Moreover, I will not be able to
test all of this material given the time limitations of the exam. I
will have to pick and choose some subset of it.

You are permitted one 8.5 x 11 sheet of paper with notes (both sides)
for use as a reference during the exam.

Here are the specifics: Students should be able to...

Indicate whether a particular environment is single agent
vs. multiagent, fully observable vs. partially observable,
deterministic vs. stochastic, episodic vs. sequential, static
vs. dynamic, and/or discrete vs. continuous.

Provide analysis of time complexity, space complexity, completeness,
and optimality for each of the search algorithms listed below. Explain
what situations make sense for each, what reasons there are for
choosing one over another, and should be able to demonstrate in detail
how any of them work on a specific example. Demonstrate understanding
of the difference between tree-search and graph-search, and why the
distinction matters. Pseudocode these algorithms, produce variations,
or interpret their application to given problems:

 - depth-first search
 - breadth-first search
 - uniform cost search
 - iterative deepening search
 - best-first search
 - greedy best-first search
 - A* search
 - IDA* search

Define a heuristic, construct an appropriate heuristic for a problem
under consideration, and demonstrate how it fits in with greedy
best-first search and A* search. Should be able to define what
admissible and consistent heuristics are, why they matter, and be able
to prove related statements about admissibility and/or
consistency. Should be able to identify when one heuristic dominates
another, and justify why a dominating heuristic leads to a faster
solution.

Answer detailed questions about how minimax works, from conceptual,
algorithmic, and programming perspectives.

Explain memory and time complexity of minimax, as well as appropriate
approximation mechanisms.

Explain what minimax what guarantees minimax makes about optimality,
what assumptions are required for it, and what the ramifications are
when the assumptions are not satisfied.

Write pseudocode for minimax, answer questions about it, and modify it
to handle variations.

Handle any of the relevant above items with multiplayer-games as well
as two-player games.

Answer detailed questions about how alpha-beta pruning works, from
conceptual, algorithmic, and programming perspectives.

Explain impact that alpha-beta pruning has in terms of time, space,
and correctness of answer.