Introduce a new, uniform way to create and solve games. Games can now be created directly from specification using creat_game, uniformly solved using solve_game and transformed into a strategy using create_strategy. Strategy are mealy machines, which can be minimized. * bin/ltlsynt.cc: Minor adaption * spot/twaalgos/game.cc: solve_game, setters and getters for named properties * spot/twaalgos/game.hh: Here too * spot/twaalgos/mealy_machine.cc: Minor adaption * spot/twaalgos/synthesis.cc: create_game, create_strategy and minimize_strategy * spot/twaalgos/synthesis.hh: Here too * tests/core/ltlsynt.test: Adapting * tests/python/aiger.py , tests/python/games.ipynb , tests/python/mealy.py , tests/python/parity.py , tests/python/split.py: Adapting
136 lines
No EOL
5.4 KiB
C++
136 lines
No EOL
5.4 KiB
C++
// -*- coding: utf-8 -*-
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// Copyright (C) 2020-2021 Laboratoire de Recherche et
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// Développement de l'Epita (LRDE).
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//
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// This file is part of Spot, a model checking library.
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//
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// Spot is free software; you can redistribute it and/or modify it
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// under the terms of the GNU General Public License as published by
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// the Free Software Foundation; either version 3 of the License, or
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// (at your option) any later version.
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//
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// Spot is distributed in the hope that it will be useful, but WITHOUT
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// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
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// or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
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// 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, see <http://www.gnu.org/licenses/>.
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#pragma once
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#include <spot/twa/twagraph.hh>
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#include <spot/twaalgos/game.hh>
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#include <bddx.h>
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namespace spot
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{
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/// \brief make each transition (conditionally, see do__simpify)
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/// a 2-step transition
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///
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/// Given a set of atomic propositions I, split each transition
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/// p -- cond --> q cond in 2^2^AP
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/// into a set of transitions of the form
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/// p -- {a} --> (p,a) -- o --> q
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/// for each a in cond \cap 2^2^I
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/// and where o = (cond & a) \cap 2^2^(O)
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///
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/// By definition, the states p are deterministic,
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/// only the states of the form
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/// (p,a) may be non-deterministic.
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/// This function is used to transform an automaton into a turn-based game in
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/// the context of LTL reactive synthesis.
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/// \param aut automaton to be transformed
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/// \param output_bdd conjunction of all output AP, all APs not present
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/// are treated as inputs
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/// \param complete_env Whether the automaton should be complete for the
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/// environment, i.e. the player of I
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/// \param do_simplify If a state has a single outgoing transition
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/// we do not necessarily have to split it
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/// to solve the game
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/// \note: This function also computes the state players
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/// \note: If the automaton is to be completed for both env and player
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/// then egdes between the sinks will be added
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/// (assuming that the environnement/player of I) plays first
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SPOT_API twa_graph_ptr
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split_2step(const const_twa_graph_ptr& aut,
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const bdd& output_bdd, bool complete_env, bool do_simplify);
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/// \brief the reverse of split_2step
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///
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/// \note: This function relies on the named property "state-player"
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SPOT_API twa_graph_ptr
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unsplit_2step(const const_twa_graph_ptr& aut);
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/// \brief Creates a game from a specification and a set of
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/// output propositions
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///
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/// \param f The specification given as LTL/PSL formula
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/// \param all_outs The names of all output propositions
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/// \param gi game_info structure
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/// \note All propositions in the formula that do not appear in all_outs
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/// arer treated as input variables.
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SPOT_API twa_graph_ptr
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create_game(const formula& f,
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const std::vector<std::string>& all_outs,
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game_info& gi);
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/// \brief create_game called with default options
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SPOT_API twa_graph_ptr
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create_game(const formula& f,
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const std::vector<std::string>& all_outs);
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/// \brief Like create_game but formula given as string
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SPOT_API twa_graph_ptr
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create_game(const std::string& f,
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const std::vector<std::string>& all_outs,
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game_info& gi);
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/// \brief create_game called with default options
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SPOT_API twa_graph_ptr
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create_game(const std::string& f,
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const std::vector<std::string>& all_outs);
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/// \brief Takes a solved game and restricts the automaton to the
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/// winning strategy of the player
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///
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/// \param arena twa_graph with named properties "state-player",
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/// "strategy" and "state-winner"
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/// \param unsplit Whether or not to unsplit the automaton
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/// \param keep_acc Whether or not keep the acceptance condition
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/// \return the resulting twa_graph
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SPOT_API spot::twa_graph_ptr
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apply_strategy(const spot::twa_graph_ptr& arena,
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bool unsplit, bool keep_acc);
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/// \brief Minimizes a strategy. Strategies are infact
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/// Mealy machines. So we can use techniques designed for them
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///
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/// \param strat The machine to minimize
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/// \param min_lvl How to minimize the machine:
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/// 0: No minimization, 1: Minimizing it like an DFA,
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/// means the language is preserved, 2: Inclusion with
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/// output assignement, 3: Exact minimization using
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/// SAT solving, 4: SAT solving with DFA minimization as
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/// preprocessing, 5: SAT solving using inclusion based
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/// minimization with output assignment as preprocessing
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/// \note min_lvl 1 and 2 work more efficiently on UNSPLIT strategies,
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/// whereas min_lvl 3, 4 and 5 mandate a SPLIT strategy
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SPOT_API void
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minimize_strategy_here(twa_graph_ptr& strat, int min_lvl);
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///\brief Like minimize_strategy_here
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SPOT_API twa_graph_ptr
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minimize_strategy(const_twa_graph_ptr& strat, int min_lvl);
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/// \brief creates a strategy from a solved game taking into account the
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/// options given in gi
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SPOT_API twa_graph_ptr
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create_strategy(twa_graph_ptr arena, game_info& gi);
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SPOT_API twa_graph_ptr
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create_strategy(twa_graph_ptr arena);
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} |