mirror of
git://git.savannah.nongnu.org/eliot.git
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3c7a84d543
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
84 lines
3.3 KiB
C++
84 lines
3.3 KiB
C++
/*****************************************************************************
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* Eliot
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* Copyright (C) 2005-2007 Olivier Teulière
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* Authors: Olivier Teulière <ipkiss @@ gmail.com>
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*
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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation; either version 2 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
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*****************************************************************************/
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#ifndef AI_PLAYER_H_
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#define AI_PLAYER_H_
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#include "player.h"
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class Dictionary;
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class Round;
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class Board;
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class Tile;
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/**
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* This class is a pure interface, that must be implemented by all the AI
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* (Artificial Intelligence) players
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*
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* Note: we could implement various strategies (some of which are already
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* implemented):
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* - best: play the word with the best score (current default implementation)
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* - second: play the word with the second best score (strictly lower than
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* the best one)
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* - random: randomly choose one of the possible words
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* - handicap(p): in the array of the n possible words (sorted by
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* decreasing scores), play the word number i, where i/n is nearest
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* from a predefined percentage p.
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* So 'handicap(0)' should be equivalent to 'best'.
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* This strategy should make an interesting opponent, because you can
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* adapt it to your level, with a careful choice of the p value.
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*
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* In fact, instead of working on the score of the words, these strategies
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* could work on any other value. In particular, some heuristics could
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* modulate the score with a value indicating the openings offered by the
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* word (if a word makes accessible a "word counts triple" square, it is
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* less interesting than another word with the same score or even with a
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* slightly lower score, but which does not offer such a square).
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*
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* More evolved heuristics could even take into account the remaining
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* letters in the bag to guess the 'statistical rack' of the opponent, and
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* play a word both maximizing the score and minimizing the opponent's
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* score...
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* Hmmm... i don't think this one will be implemented in a near future :)
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**************************/
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class AIPlayer: public Player
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{
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public:
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virtual ~AIPlayer() {}
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/// No human here. Trespassers will be shot!
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virtual bool isHuman() const { return false; }
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/**
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* This method does the actual computation. It will be called before any
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* of the following methods, so it must prepare everything for them.
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*/
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virtual void compute(const Dictionary &iDic, const Board &iBoard, bool iFirstWord) = 0;
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/// Return the move played by the AI
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virtual Move getMove() const = 0;
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protected:
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/// This class is a pure interface, forbid any direct instanciation
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AIPlayer() {}
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};
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#endif
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