Question#1
What is the main purpose of the generate-and-test search algorithm in AI?
a) Explore large problem spaces simply ✅
b) Always find the optimal solution efficiently
c) Guarantee the best move in adversarial games
d) Replace the need for heuristics in search

Question#2
Which of the following is a limitation of generate-and-test algorithms?
a) They may not be efficient for large spaces ✅
b) They require deep domain knowledge
c) They never use heuristics
d) They cannot handle colored blocks problems

Question#3
In the colored blocks problem, what is a suggested heuristic?
a) Minimize outside red faces ✅
b) Place largest block first
c) Alternate colors with every block
d) Use only one color per row

Question#4
What does the A* search algorithm use to find optimal paths?
a) Actual and estimated costs ✅
b) Only the lowest edge value
c) Random walk through graph
d) Breadth First Search only

Question#5
The A* algorithm's heuristic function is commonly represented as _____?
a) h(n) ✅
b) g(n)
c) f(n)
d) c(n)

Question#6
How does A* choose the next node to explore?
a) Node with the lowest f-score ✅
b) Node with highest h(n) value
c) Randomly selects a node
d) Node with the maximum children

Question#7
What is the formula for A*'s evaluation function?
a) f(n) = g(n) + h(n) ✅
b) f(n) = h(n) - g(n)
c) f(n) = g(n) x h(n)
d) f(n) = g(n) / h(n)

Question#8
Which property must the heuristic function satisfy for A* to be optimal?
a) Admissibility ✅
b) Randomness
c) Symmetry
d) Periodicity

Question#9
When does the A* algorithm declare no path exists?
a) Open set is empty before reaching goal ✅
b) All h(n) values are equal
c) Graph contains cycles
d) Every node is in the open set

Question#10
How do you reconstruct the solution path in A* after reaching the goal?
a) Backtrack from goal following lowest f-scores ✅
b) Use BFS from the start
c) Repeat the search from current node
d) Pick nodes randomly

Question#11
What is adversarial search mainly used for in AI?
a) Game-playing in competitive environments ✅
b) Optimizing algorithmic complexity
c) Solving linear equations
d) Improving memory allocation

Question#12
What is pruning in adversarial search algorithms?
a) Ignoring parts of the search tree with no impact ✅
b) Exploring every possible move
c) Randomly skipping nodes
d) Adding extra branches to the tree

Question#13
In minimax, the MAX player tries to _____?
a) Maximize the utility value ✅
b) Minimize the utility value
c) Block all moves
d) Always lose intentionally

Question#14
The MIN player’s strategy in minimax is to _____?
a) Decrease MAX's chances of winning ✅
b) Increase own utility unconditionally
c) Switch to MAX’s moves
d) End the game early

Question#15
Which search strategy does minimax primarily use for exploring the game tree?
a) Depth-First Search ✅
b) Breadth-First Search
c) Randomized Search
d) Best-First Search

Question#16
Why are rule-based systems less favored than machine learning systems in modern game AI?
a) They cannot learn from experience or improve ✅
b) They are always slower in execution
c) They use too many random moves
d) They require no knowledge of rules

Question#17
What notable chess AI defeated Garry Kasparov in 1997?
a) Deep Blue ✅
b) AlphaZero
c) Stockfish
d) DeepMind

Question#18
Which function in minimax generates all possible moves?
a) movegen ✅
b) staticevaluation
c) alphabeta
d) pruning

Question#19
What is a key disadvantage of game playing AI techniques?
a) High computational cost in complex games ✅
b) They are always inaccurate
c) They never use search algorithms
d) Cannot be used for education

Question#20
What does the minimax algorithm use to determine the best choice?
a) Backtracking with a heuristic function ✅
b) Forward prediction only
c) Random guessing moves
d) No evaluation function needed