Select an action using a given method (min_max, alpha_beta). More...
#include <game_ai.hpp>
Public Types | |
typedef Method::state | state |
typedef Method::action | action |
typedef Method::score | score |
Public Member Functions | |
void | operator() (int depth, const state ¤t_state, action &new_action, bool computer_turn) const |
Select an action using the given method. |
Select an action using a given method (min_max, alpha_beta).
Template parameters:
Definition at line 188 of file game_ai.hpp.
typedef Method::action claw::ai::game::select_action< Method >::action |
Definition at line 192 of file game_ai.hpp.
typedef Method::score claw::ai::game::select_action< Method >::score |
Definition at line 193 of file game_ai.hpp.
typedef Method::state claw::ai::game::select_action< Method >::state |
Definition at line 191 of file game_ai.hpp.
void claw::ai::game::select_action< Method >::operator() | ( | int | depth, |
const state & | current_state, | ||
action & | new_action, | ||
bool | computer_turn | ||
) | const |
Select an action using the given method.
depth | Maximum depth of the search tree. |
current_state | The state of the game. |
new_action | (in/out) Best known action. |
computer_turn | Tell if the action is done by the computer. |
Definition at line 328 of file game_ai.tpp.
{ std::list<action> l; typename std::list<action>::iterator it; score best_eval; Method method; // get all reachable states current_state.next_actions( l ); best_eval = current_state.min_score(); for (it=l.begin(); it!=l.end(); ++it) { state* new_state; score eval; // try and evaluate each action new_state = static_cast<state*>(current_state.do_action(*it)); eval = method(depth-1, *new_state, !computer_turn); delete new_state; // we keep one of the best actions if (eval > best_eval) { best_eval = eval; new_action = *it; } } } // select_action::operator()