1. A game can be formally defined as a kind of search problem with the following components:
a) Initial State
b) Successor Function
c) Terminal Test
d) Utility Function

a) Initial State
b) Successor Function
c) Terminal Test
d) Utility Function
Explanation: The initial state includes the board position and identifies the player to move. A successor function returns a list of (move, state) pairs, each indicating a legal move and the resulting state. A terminal test determines when the game is over. States where the game has ended are called terminal states. A utility function (also called an objective function or payoff function), which gives a numeric value for the terminal states. In chess, the outcome is a win, loss, or draw, with values +1, -1, or 0.

2. General algorithm applied on game tree for making decision of win/lose is ____________
a) DFS/BFS Search Algorithms
b) Heuristic Search Algorithms
c) Greedy Search Algorithms
d) MIN/MAX Algorithms

d) MIN/MAX Algorithms
Explanation: Given a game tree, the optimal strategy can be determined by examining the min/max value of each node, which we write as MINIMAX- VALUE(n). The min/max value of a node is the utility (for MAX) of being in the corresponding state, assuming that both players play optimally from there to the end of the game. Obviously, the min/max value of a terminal state is just its utility. Furthermore, given a choice, MAX will prefer to move to a state of maximum value, whereas MIN prefers a state of minimum value.

3. Which search is equal to minimax search but eliminates the branches that can't influence the final decision?
a) Depth-first search
c) Alpha-beta pruning
d) None of the mentioned

c) Alpha-beta pruning
Explanation: The alpha-beta search computes the same optimal moves as minimax, but eliminates the branches that can't influence the final decision.

4. Which values are independent in minimax search algorithm?
a) Pruned leaves x and y
b) Every state is dependent
c) Root is independent
d) None of the mentioned

a) Pruned leaves x and y
Explanation: The minimax decision are independent of the values of the pruned values x and y because of the root values.

5. Which value is assigned to alpha and beta in the alpha-beta pruning?
a) Alpha = max
b) Beta = min
c) Beta = max
d) Both a & b

d) Both a & b
Explanation: Alpha and beta are the values of the best choice we have found so far at any choice point along the path for MAX and MIN.

6. To which depth does the alpha-beta pruning can be applied?
a) 10 states
b) 8 States
c) 6 States
d) Any depth

d) Any depth
Explanation: Alpha-beta pruning can be applied to trees of any depth and it is possible to prune entire sub-tree rather than leaves.

7. Which search is similar to minimax search?
a) Hill-climbing search
b) Depth-first search
d) All of the mentioned

b) Depth-first search
Explanation: The minimax search is depth-first search, So at one time we just have to consider the nodes along a single path in the tree.

8. Where does the values of alpha-beta search get updated?
a) Along the path of search
b) Initial state itself
c) At the end
d) None of the mentioned

a) Along the path of search
Explanation: Alpha-beta search updates the value of alpha and beta as it gets along and prunes the remaining branches at node.

9. What is called as transposition table?
a) Hash table of next seen positions
b) Hash table of previously seen positions
c) Next value in the search
d) None of the mentioned

b) Hash table of previously seen positions
Explanation: Transposition is the occurrence of repeated states frequently in the search.

10. Which function is used to calculate the feasibility of whole game tree?
a) Evaluation function
b) Transposition
c) Alpha-beta pruning
d) All of the mentioned

a) Evaluation function
Explanation: Because we need to cut the search off at some point and apply an evaluation function that gives an estimate of the utility of the state.