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CPSC625-600 Midterm Exam (10/18/2002, Fri)
Last name: , First name: , ID (last 5 digit):
Time: 12:40pm–1:30pm (50 minutes +
) , Total Points: 100
Subject Score
AI General /
Search /
Game Playing /
Propositional Logic /
Total /
You may use the back of the sheet, but please prominently mark on the front in
such a case.
Be as succinct as possible.
Read the questions carefully to see what kind of answer is expected ( explain blah
in terms of ... blah ).
Solve all problems.
Total of 12 pages, including this cover. Before starting, count the pages and see
if you have all twelve.
This is a closed-book, closed-note exam.
1 AI in General (Total: 10 points)
- Do you think AI is possible? If so, what do you think would be the key conceptual breakthrough? If
not, what do you think will be the fundamental obstacle? Answer in one short paragraph, i.e. don’t
spend too much time (i.e. anything goes). (5 points)
- AI programs usually get the representational design for a given problem from the programmer. This
is one of the causes of the rigidity in many AI approaches. What capability and/or property would
be required of an autonomous agent to build its own representations as it goes on? Don’t spend too
much time (i.e. anything goes). (5 points).
- What is your project topic and who’s your partner? (0 points)
2.2 and Iterative Deepening ( ) search (20 points)
Answer all 6 questions. Note that the 6th question is 5 points worth and the rest are 3 points each.
- What is the utility function used in search? (3 points)
- What is the major disadvantage of in terms of the evaluation measures (completeness, optimality,
space, and time complexity)? (3 points)
- How does and BFS differ in terms of node-list update? (3 points)
- What is the major advantage of ! compared to " in terms of the evaluation measures and why?
(3 points)
- Why are larger heuristic functions better than smaller heuristic functions for ? (3 points)
- In increasing the f-limit in the amount of a fixed value # can sometimes lead to a more ef-
ficient search. However, this approach has a drawback. What is this drawback and what nature of
contributes to it? (5 points).
3 Game Playing (Total: 25 points)
Solve all four problems.
- Consider a MIN-MAX game tree (the two below are the same).
Fill in the utility function values at each node (the blank squares) in the MIN-MAX tree below, and
mark the path from the root node (initial state) to the goal node with a thick line ( 5 points ).
MAX
MIN
MAX
5 30 35 50 20 15 25 3 10 7 33 18 5 15 0 100 27 43
- Cross out the branches that are pruned by K -L pruning. Show the final best estimate values at each
node. ( 10 points ).
MAX
MIN
MAX
5 30 35 50 20 15 25 3 10 7 33 18 5 15 0 100 27 43
- In MIN-MAX search, a depth-first strategy is used. What feature of the algorithm makes it a depth-
first exploration? Briefly explain in terms of the functions MIN-VALUE and MAX-VALUE. (
points)
- Why is -
pruning a good strategy compared to the regular Min-Max tree approach? Briefly ex-
plain. (5 points)
- Convert the following formula into Disjunctive Normal Form (disjunction of conjunctions). Show
all your work. (3 points)
(a) q
q
9rts u q v wx
- Consider a robot that is able to lift a block if the block is light enough that it is liftable (LIFTABLE),
and if the battery power is adequate (BATTERYOK). If both of these conditions are met, then the
robot will always lift the block and its arm will move (MOVESARM).
y ]HzH]$av|{}_~wtZ |H(
y Z$
u }a$axUv
We can think of a situation when we know that the battery is okay but the arm did not move. From
this we can infer that the block it is holding now is not liftable.
In other words, we want to show q |H(
y Z$ is a logical consequence of the facts below:
(1)
y ]HzH]$av|{_}a~
(2) q
}a$ax"v
(3) q
y ]HzH]$av|{_}a~
r q |H(
y Z$
r }a_$axUv
Show that q !H(
y $ is a logical consequence of the above using resolution. Show all your
work. (10 points)
