Engineering Questions with Answers - Multiple Choice Questions
Artificial Intelligence MCQ – Game Theory
General games involves ____________
c) Neither Single-agent nor Multi-agent
d) Only Single-agent and Multi-agent
Explanation: Depending upon games it could be single agent (Sudoku) or multi-agent (Chess).
Adversarial search problems uses ____________
a) Competitive Environment
b) Cooperative Environment
c) Neither Competitive nor Cooperative Environment
d) Only Competitive and Cooperative Environment
Explanation: Since in cooperative environment agents’ goals are I conflicts. They compete for goal.
Mathematical game theory, a branch of economics, views any multi-agent environment as a game provided that the impact of each agent on the others is “significant,” regardless of whether the agents are cooperative or competitive.
Zero sum games are the one in which there are two agents whose actions must alternate and in which the utility values at the end of the game are always the same.
Explanation: Utility values are always same and opposite.
Zero sum game has to be a ______ game.
a) Single player
b) Two player
d) Three player
Explanation: Zero sum games could be multiplayer games as long as the condition for zero sum game is satisfied.
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) All of the mentioned
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, lose, or draw, with values +1, -1, or 0.
The initial state and the legal moves for each side define the __________ for the game.
a) Search Tree
b) Game Tree
c) State Space Search
Explanation: An example of game tree for Tic-Tac-Toe game.
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
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.
The minimax algorithm computes the minimax decision from the current state. It uses a simple recursive computation of the minimax values of each successor state, directly implementing the defining equations. The recursion proceeds all the way down to the leaves of the tree, and then the minimax values are backed up through the tree as the recursion unwinds.
Explanation: Refer definition of minimax algorithm.
What is the complexity of minimax algorithm?
a) Same as of DFS
b) Space – bm and time – bm
c) Time – bm and space – bm
d) Same as BFS
Explanation: Same as DFS.