CSci 4511
Sample Midterm 1
Closed book, notes, laptop, cell phone
The following are questions i considered asking on Midterm 1. i eventually chose 16 other
problems and so am publishing these as practice questions. The material here and on the exam
are roughly the same  minimax, local search and lots of A*.
Easier Questions
1. The distance from a node to the goal is 10. A heuristic has returned the following estimated
distances. For each one, mark whether they are from a possibly admissible heuristic (T/F).
a.
15
F
b.
10
T
c.
5
T
d.
0
T
e.
10
T
2. State whether the following admissible heuristics are Useful, Not Useful or Dominant.
a.
h1(n) = straight line distance
U
b.
h2(n) = 0
NU
c.
h3(n) = max(h1, h2)
D
d.
h4(n) = min(h1, h2)
U
3. In A*, if h(n)
≤
c(n,a,goal) then f(n) < C*. This is independent of g(n) even though ,
f(n)=g(n)+h(n). Why?
g is the past cost which we already know and thus isn't an estimate but a perfect number. It is
never too low or too high, so whether f(n) is accurate depends entirely on h(n)  we know g(n)'s
contribution is perfectly accurate.
4. In what sense is A* preferable to IDA*?
A* searches fewer nodes than IDA* and thus has better run time performance.
5. Which algorithm we use depends on the specific needs (i.e., fast runtime performance) and
properties (i.e., nondeterminism) of the problem and algorithm. Name two (not counting run
time performance and nondeterministic environments).
These are listed in lecture 2. They include resources memory, run time performance,
development time, tuning time and power and factors deterministic, dynamic, perfect
information, information accuracy, importance of optimality, discrete state, discrete actions, state
space size, presence of loops, solution density, dead ends, solution type (path or goal), degree of
sensitivity to starting location, whether the solution depth is known, variable action costs, test
cost, relative error cost, interruptibility, contextsensitive action costs and a whole bunch of other
things
6. Name three differences between breadthfirst and depthfirst search (other than that one
searches breadth first and the other searches depth first).
BFS
DFS
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optimal
not optimal
memory hog (exponential)
memory friendly (linear)
loops OK
can't handle loops
Normal Questions
7. What is the "no path" runtime problem? How does hierarchical path finding help?
If a solution doesn't exist, A* searches the entire search space. This is slow and probably a big
variance from when it finds a solution, meaning it creates performance spikes. Hierarchical path
finding groups nodes into clusters and performs path finding between clusters to make sure that
one cluster can reach another. If it cannot, it doesn't engage in a detailed search, allowing it to
fail fast.
8. A common problem in local search is local maxima. Name two ways to combat this problem.
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 Spring '10
 ABDELSALAM
 Depthfirst search, Search algorithms, Search algorithm, search space, A*

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