AI Games

  Home  Artificial intelligence (AI)  AI Games

“Artificial Intelligence Games frequently Asked Questions in various AI Games job Interviews by interviewer. Get preparation of Artificial Intelligence Games job interview”

25 AI Games Questions And Answers

1⟩ 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.


2⟩ 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.


14⟩ The minimax algorithm (Figure 6.3) 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. a) True b) False

a) True

Explanation: Refer definition of minimax algorithm.


20⟩ Consider this after a while Tesuaros temporal difference program will likely stop learning, so does this means that it lost its intelligence?

Some game playing programs are getting quite good and I expect that in the long run all the best "players" will be programs. While that is wonderful and while those programs that learn to play their games get a rating of minimal intelligence from me remember that what's impressive about people is that not only can they do games, they do heuristic search, theorem proving, use natural language and cope with the real world. The real challenge is to get programs to do that. If you simply pursue techniques for game playing will you ever end up with all these human capabilities in one program?