eliot/game/ai_player.h
Olivier Teulière 3c7a84d543 Support saving/loading games (any game type) in XML format.
Status:
It works well, but there are still a few details to improve/fix

More details about the changes:
 - New dependency on Arabica and Libxml2 to parse the XML
 - Loading the old format is still supported for this release, but won't be supported anymore in the next one
 - Games are now only saved in the new format
 - In training mode, the player is now created externally, like in the other modes
 - Avoid using GameIO (the one from game/) whenever possible
 - Do not use a FILE* argument anymore when loading a game
 - Throw and catch exceptions correctly when a game cannot be loaded or saved
 - The non-regression tests now use a new method to print the game history
2009-11-29 16:01:31 +00:00

84 lines
3.3 KiB
C++

/*****************************************************************************
* Eliot
* Copyright (C) 2005-2007 Olivier Teulière
* Authors: Olivier Teulière <ipkiss @@ gmail.com>
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
*****************************************************************************/
#ifndef AI_PLAYER_H_
#define AI_PLAYER_H_
#include "player.h"
class Dictionary;
class Round;
class Board;
class Tile;
/**
* This class is a pure interface, that must be implemented by all the AI
* (Artificial Intelligence) players
*
* Note: we could implement various strategies (some of which are already
* implemented):
* - best: play the word with the best score (current default implementation)
* - second: play the word with the second best score (strictly lower than
* the best one)
* - random: randomly choose one of the possible words
* - handicap(p): in the array of the n possible words (sorted by
* decreasing scores), play the word number i, where i/n is nearest
* from a predefined percentage p.
* So 'handicap(0)' should be equivalent to 'best'.
* This strategy should make an interesting opponent, because you can
* adapt it to your level, with a careful choice of the p value.
*
* In fact, instead of working on the score of the words, these strategies
* could work on any other value. In particular, some heuristics could
* modulate the score with a value indicating the openings offered by the
* word (if a word makes accessible a "word counts triple" square, it is
* less interesting than another word with the same score or even with a
* slightly lower score, but which does not offer such a square).
*
* More evolved heuristics could even take into account the remaining
* letters in the bag to guess the 'statistical rack' of the opponent, and
* play a word both maximizing the score and minimizing the opponent's
* score...
* Hmmm... i don't think this one will be implemented in a near future :)
**************************/
class AIPlayer: public Player
{
public:
virtual ~AIPlayer() {}
/// No human here. Trespassers will be shot!
virtual bool isHuman() const { return false; }
/**
* This method does the actual computation. It will be called before any
* of the following methods, so it must prepare everything for them.
*/
virtual void compute(const Dictionary &iDic, const Board &iBoard, bool iFirstWord) = 0;
/// Return the move played by the AI
virtual Move getMove() const = 0;
protected:
/// This class is a pure interface, forbid any direct instanciation
AIPlayer() {}
};
#endif