package aima.core.search.adversarial;
import java.util.List;
/**
* Artificial Intelligence A Modern Approach (3rd Edition): page 165.<br>
* <br>
* A game can be formally defined as a kind of search problem with the following
* elements: <br>
* <ul>
* <li>S0: The initial state, which specifies how the game is set up at the
* start.</li>
* <li>PLAYER(s): Defines which player has the move in a state.</li>
* <li>ACTIONS(s): Returns the set of legal moves in a state.</li>
* <li>RESULT(s, a): The transition model, which defines the result of a move.</li>
* <li>TERMINAL-TEST(s): A terminal test, which is true when the game is over
* and false TERMINAL STATES otherwise. States where the game has ended are
* called terminal states.</li>
* <li>UTILITY(s, p): A utility function (also called an objective function or
* payoff function), defines the final numeric value for a game that ends in
* terminal state s for a player p. In chess, the outcome is a win, loss, or
* draw, with values +1, 0, or 1/2 . Some games have a wider variety of possible
* outcomes; the payoffs in backgammon range from 0 to +192. A zero-sum game is
* (confusingly) defined as one where the total payoff to all players is the
* same for every instance of the game. Chess is zero-sum because every game has
* payoff of either 0 + 1, 1 + 0 or 1/2 + 1/2 . "Constant-sum" would have been a
* better term, but zero-sum is traditional and makes sense if you imagine each
* player is charged an entry fee of 1/2.</li>
* </ul>
*
* @author Ruediger Lunde
*
* @param <STATE>
* Type which is used for states in the game.
* @param <ACTION>
* Type which is used for actions in the game.
* @param <PLAYER>
* Type which is used for players in the game.
*/
public interface Game<STATE, ACTION, PLAYER> {
STATE getInitialState();
PLAYER[] getPlayers();
PLAYER getPlayer(STATE state);
List<ACTION> getActions(STATE state);
STATE getResult(STATE state, ACTION action);
boolean isTerminal(STATE state);
double getUtility(STATE state, PLAYER player);
}