ltlsynt: rework synthesis algorithms
ltlsynt now offers two algorithms: one where splitting occurs before determinization (the historical one) and one where determinization occurs before splitting. * bin/ltlsynt.cc: here * tests/core/ltlsynt.test: test it and refactor test file * NEWS: document it * spot/misc/game.hh, spot/misc/game.cc: remove Calude's algorithm
This commit is contained in:
Maximilien Colange
parent
516e9536df
commit
bd75ab5b39
5 changed files with 101 additions and 423 deletions
10
NEWS
10
NEWS
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@ -34,6 +34,16 @@ New in spot 2.6.0.dev (not yet released)
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- "ltlfilt --suspendable" is now a synonym for
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"ltlfilt --universal --eventual".
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- ltlsynt now has two algorithms for synthesis:
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--algo=sd is the historical one. The automaton of the formula
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is split to separate inputs and outputs, then
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determinized.
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--algo=ds the automaton of the formula is determinized, then
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split to separate inputs and outputs.
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In both cases, the obtained parity game is solved using
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Zielonka algorithm. Calude's quasi-polynomial time algorithm has
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been dropped as it was not used.
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Bugs fixed:
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- scc_info::split_on_sets() did not correctly register the
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135
bin/ltlsynt.cc
135
bin/ltlsynt.cc
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@ -70,10 +70,10 @@ static const argp_option options[] =
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" propositions", 0},
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/**************************************************/
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{ nullptr, 0, nullptr, 0, "Fine tuning:", 10 },
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{ "algo", OPT_ALGO, "qp|rec", 0,
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"choose the parity game algorithm, valid ones are rec (Zielonka's"
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" recursive algorithm, default) and qp (Calude et al.'s quasi-polynomial"
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" time algorithm)", 0 },
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{ "algo", OPT_ALGO, "ds|sd", 0,
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"choose the algorithm for synthesis:\n"
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" - sd: split then determinize (default)\n"
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" - ds: determinize then split", 0 },
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/**************************************************/
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{ nullptr, 0, nullptr, 0, "Output options:", 20 },
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{ "print-pg", OPT_PRINT, nullptr, 0,
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@ -112,27 +112,26 @@ bool opt_print_pg(false);
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bool opt_real(false);
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bool opt_print_aiger(false);
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// FIXME rename options to choose the algorithm
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enum solver
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{
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QP,
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REC
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DET_SPLIT,
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SPLIT_DET
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};
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static char const *const solver_args[] =
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{
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"qp", "quasi-polynomial",
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"recursive",
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"detsplit", "ds",
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"splitdet", "sd",
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nullptr
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};
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static solver const solver_types[] =
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{
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QP, QP,
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REC,
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DET_SPLIT, DET_SPLIT,
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SPLIT_DET, SPLIT_DET
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};
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ARGMATCH_VERIFY(solver_args, solver_types);
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static solver opt_solver = REC;
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static solver opt_solver = SPLIT_DET;
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static bool verbose = false;
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namespace
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@ -187,7 +186,8 @@ namespace
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{
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// if the input automaton is deterministic, degeneralize it to be sure to
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// end up with a parity automaton
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auto dpa = spot::tgba_determinize(spot::degeneralize_tba(split));
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auto dpa = spot::tgba_determinize(spot::degeneralize_tba(split),
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false, true, true, false);
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dpa->merge_edges();
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if (opt_print_pg)
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dpa = spot::sbacc(dpa);
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@ -293,12 +293,40 @@ namespace
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all_outputs &= bdd_ithvar(v);
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}
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auto split = split_2step(aut, all_inputs);
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if (verbose)
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std::cerr << "split inputs and outputs done" << std::endl;
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auto dpa = to_dpa(split);
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if (verbose)
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std::cerr << "determinization done" << std::endl;
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spot::twa_graph_ptr dpa = nullptr;
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if (opt_solver == DET_SPLIT)
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{
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auto tmp = to_dpa(aut);
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if (verbose)
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std::cerr << "determinization done\n"
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<< "dpa has " << tmp->num_states() << " states" << std::endl;
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tmp->merge_states();
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if (verbose)
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std::cerr << "simulation done\n"
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<< "dpa has " << tmp->num_states() << " states" << std::endl;
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dpa = split_2step(tmp, all_inputs);
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if (verbose)
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std::cerr << "split inputs and outputs done\n"
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<< "automaton has " << dpa->num_states() << " states"
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<< std::endl;
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spot::colorize_parity_here(dpa, true);
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}
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else
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{
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auto split = split_2step(aut, all_inputs);
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if (verbose)
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std::cerr << "split inputs and outputs done\n"
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<< "automaton has " << split->num_states() << " states"
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<< std::endl;
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dpa = to_dpa(split);
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if (verbose)
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std::cerr << "determinization done\n"
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<< "dpa has " << dpa->num_states() << " states" << std::endl;
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dpa->merge_states();
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if (verbose)
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std::cerr << "simulation done\n"
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<< "dpa has " << dpa->num_states() << " states" << std::endl;
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}
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auto owner = complete_env(dpa);
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auto pg = spot::parity_game(dpa, owner);
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if (verbose)
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@ -310,58 +338,33 @@ namespace
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pg.print(std::cout);
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return 0;
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}
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switch (opt_solver)
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{
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case REC:
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{
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spot::parity_game::strategy_t strategy[2];
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spot::parity_game::region_t winning_region[2];
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pg.solve(winning_region, strategy);
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if (winning_region[1].count(pg.get_init_state_number()))
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{
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std::cout << "REALIZABLE\n";
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if (!opt_real)
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{
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auto strat_aut =
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strat_to_aut(pg, strategy[1], dpa, all_outputs);
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// output the winning strategy
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if (opt_print_aiger)
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spot::print_aiger(std::cout, strat_aut);
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else
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{
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automaton_printer printer;
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printer.print(strat_aut, timer);
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}
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}
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return 0;
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}
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spot::parity_game::strategy_t strategy[2];
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spot::parity_game::region_t winning_region[2];
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pg.solve(winning_region, strategy);
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if (winning_region[1].count(pg.get_init_state_number()))
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{
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std::cout << "REALIZABLE\n";
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if (!opt_real)
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{
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auto strat_aut =
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strat_to_aut(pg, strategy[1], dpa, all_outputs);
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// output the winning strategy
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if (opt_print_aiger)
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spot::print_aiger(std::cout, strat_aut);
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else
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{
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std::cout << "UNREALIZABLE\n";
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return 1;
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automaton_printer printer;
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printer.print(strat_aut, timer);
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}
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}
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case QP:
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if (!opt_real)
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{
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std::cout << "The quasi-polynomial time algorithm does not"
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" implement synthesis yet, use --realizability\n";
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return 2;
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}
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else if (pg.solve_qp())
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{
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std::cout << "REALIZABLE\n";
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return 0;
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}
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else
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{
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std::cout << "UNREALIZABLE\n";
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return 1;
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}
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default:
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SPOT_UNREACHABLE();
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return 2;
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return 0;
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}
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else
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{
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std::cout << "UNREALIZABLE\n";
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return 1;
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}
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}
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};
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@ -82,11 +82,6 @@ void parity_game::solve(region_t (&w)[2], strategy_t (&s)[2]) const
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solve_rec(states_, m, w, s);
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}
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bool parity_game::solve_qp() const
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{
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return reachability_game(*this).is_reachable();
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}
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parity_game::strategy_t
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parity_game::attractor(const region_t& subgame, region_t& set,
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unsigned max_parity, int p, bool attr_max) const
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@ -223,166 +218,4 @@ void parity_game::solve_rec(region_t& subgame, unsigned max_parity,
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subgame.insert(w0[!p].begin(), w0[!p].end());
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}
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int reachability_state::compare(const state* other) const
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{
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auto o = down_cast<const reachability_state*>(other);
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assert(o);
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if (num_ != o->num())
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return num_ - o->num();
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if (b_ < o->b())
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return -1;
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if (b_ > o->b())
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return 1;
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return 0;
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}
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bool reachability_state::operator<(const reachability_state& o) const
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{
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// Heuristic to process nodes with a higher chance of leading to a target
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// node first.
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assert(b_.size() == o.b().size());
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for (unsigned i = b_.size(); i > 0; --i)
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if (b_[i - 1] != o.b()[i - 1])
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return b_[i - 1] > o.b()[i - 1];
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return num_ < o.num();
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}
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const reachability_state* reachability_game_succ_iterator::dst() const
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{
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// NB: colors are indexed at 1 in Calude et al.'s paper and at 0 in spot
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// All acceptance sets are therefore incremented (which is already done by
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// max_set), so that 0 can be kept as a special value indicating that no
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// i-sequence is tracked at this index. Hence the parity switch in the
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// following implementation, compared to the paper.
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std::vector<unsigned> b = state_.b();
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unsigned a = it_->acc.max_set();
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assert(a);
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unsigned i = -1U;
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bool all_even = a % 2 == 0;
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for (unsigned j = 0; j < b.size(); ++j)
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{
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if ((b[j] % 2 == 1 || b[j] == 0) && all_even)
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i = j;
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else if (b[j] > 0 && a > b[j])
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i = j;
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all_even = all_even && b[j] > 0 && b[j] % 2 == 0;
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}
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if (i != -1U)
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{
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b[i] = a;
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for (unsigned j = 0; j < i; ++j)
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b[j] = 0;
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}
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return new reachability_state(it_->dst, b, !state_.anke());
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}
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const reachability_state* reachability_game::get_init_state() const
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{
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// b[ceil(log(n + 1))] != 0 implies there is an i-sequence of length
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// 2^(ceil(log(n + 1))) >= 2^log(n + 1) = n + 1, so it has to contain a
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// cycle.
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unsigned i = std::ceil(std::log2(pg_.num_states() + 1));
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return new reachability_state(pg_.get_init_state_number(),
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std::vector<unsigned>(i + 1),
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false);
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}
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reachability_game_succ_iterator*
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reachability_game::succ_iter(const state* s) const
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{
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auto state = down_cast<const reachability_state*>(s);
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return new reachability_game_succ_iterator(pg_, *state);
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}
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std::string reachability_game::format_state(const state* s) const
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{
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auto state = down_cast<const reachability_state*>(s);
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std::ostringstream fmt;
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bool first = true;
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fmt << state->num() << ", ";
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fmt << '[';
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for (unsigned b : state->b())
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{
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if (!first)
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fmt << ',';
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else
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first = false;
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fmt << b;
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}
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fmt << ']';
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return fmt.str();
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}
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bool reachability_game::is_reachable()
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{
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std::set<spot::reachability_state> todo{*init_state_};
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while (!todo.empty())
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{
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spot::reachability_state v = *todo.begin();
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todo.erase(todo.begin());
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std::vector<spot::const_reachability_state_ptr> succs;
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spot::reachability_game_succ_iterator* it = succ_iter(&v);
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for (it->first(); !it->done(); it->next())
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succs.push_back(spot::const_reachability_state_ptr(it->dst()));
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if (is_target(v))
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{
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c_[v] = 1;
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if (mark(v))
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return true;
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continue;
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}
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else if (v.anke())
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c_[v] = 1;
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else
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c_[v] = succs.size();
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for (auto succ: succs)
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{
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if (parents_[*succ].empty())
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{
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if (*succ != *init_state_)
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{
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todo.insert(*succ);
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parents_[*succ] = { v };
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c_[*succ] = -1U;
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}
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}
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else
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{
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parents_[*succ].push_back(v);
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if (c_[*succ] == 0 && mark(v))
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return true;
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}
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}
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}
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return false;
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}
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bool reachability_game::mark(const spot::reachability_state& s)
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{
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if (c_[s] > 0)
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{
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--c_[s];
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if (c_[s] == 0)
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{
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if (s == *init_state_)
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return true;
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for (auto& u: parents_[s])
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if (mark(u))
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return true;
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}
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}
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return false;
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}
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bool reachability_game::is_target(const reachability_state& v)
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{
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return v.b().back();
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}
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}
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@ -108,31 +108,6 @@ public:
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\endverbatim */
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void solve(region_t (&w)[2], strategy_t (&s)[2]) const;
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/// Whether player 1 has a winning strategy from the initial state.
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/// Implements Calude et al.'s quasipolynomial time algorithm.
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/** \verbatim
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@inproceedings{calude.17.stoc,
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author = {Calude, Cristian S. and Jain, Sanjay and Khoussainov,
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Bakhadyr and Li, Wei and Stephan, Frank},
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title = {Deciding Parity Games in Quasipolynomial Time},
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booktitle = {Proceedings of the 49th Annual ACM SIGACT Symposium on
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Theory of Computing},
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series = {STOC 2017},
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year = {2017},
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isbn = {978-1-4503-4528-6},
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location = {Montreal, Canada},
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pages = {252--263},
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numpages = {12},
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url = {http://doi.acm.org/10.1145/3055399.3055409},
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doi = {10.1145/3055399.3055409},
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acmid = {3055409},
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publisher = {ACM},
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address = {New York, NY, USA},
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keywords = {Muller Games, Parity Games, Quasipolynomial Time Algorithm},
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}
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\endverbatim */
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bool solve_qp() const;
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private:
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typedef twa_graph::graph_t::edge_storage_t edge_t;
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@ -149,159 +124,4 @@ private:
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region_t (&w)[2], strategy_t (&s)[2]) const;
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};
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class reachability_state: public state
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{
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private:
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unsigned num_;
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std::vector<unsigned> b_;
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bool anke_;
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public:
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reachability_state(unsigned state, const std::vector<unsigned>& b,
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bool anke)
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: num_(state), b_(b), anke_(anke)
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{
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}
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int compare(const state* other) const override;
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bool operator==(const reachability_state& o) const
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{
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return compare(&o) == 0;
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}
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bool operator!=(const reachability_state& o) const
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{
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return compare(&o) != 0;
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}
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bool operator<(const reachability_state& o) const;
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size_t hash() const override
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{
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size_t hash = wang32_hash(num_);
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for (unsigned i = 0; i < b_.size(); ++i)
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hash ^= wang32_hash(b_[i]) ^ wang32_hash(i);
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return hash;
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}
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reachability_state* clone() const override
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{
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return new reachability_state(*this);
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}
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std::vector<unsigned> b() const
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{
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return b_;
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}
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unsigned num() const
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{
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return num_;
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}
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bool anke() const
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{
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return anke_;
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}
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};
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typedef std::shared_ptr<const reachability_state>
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const_reachability_state_ptr;
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struct reachability_state_hash
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{
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size_t operator()(const reachability_state& state) const
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{
|
||||
return state.hash();
|
||||
}
|
||||
};
|
||||
|
||||
class reachability_game_succ_iterator final: public twa_succ_iterator
|
||||
{
|
||||
private:
|
||||
const parity_game& pg_;
|
||||
const reachability_state& state_;
|
||||
internal::edge_iterator<const twa_graph::graph_t> it_;
|
||||
|
||||
public:
|
||||
reachability_game_succ_iterator(const parity_game& pg,
|
||||
const reachability_state& s)
|
||||
: pg_(pg), state_(s)
|
||||
{
|
||||
}
|
||||
|
||||
bool first() override
|
||||
{
|
||||
it_ = pg_.out(state_.num()).begin();
|
||||
return it_ != pg_.out(state_.num()).end();
|
||||
}
|
||||
|
||||
bool next() override
|
||||
{
|
||||
++it_;
|
||||
return it_ != pg_.out(state_.num()).end();
|
||||
}
|
||||
|
||||
bool done() const override
|
||||
{
|
||||
return it_ == pg_.out(state_.num()).end();
|
||||
}
|
||||
|
||||
const reachability_state* dst() const override;
|
||||
|
||||
bdd cond() const override
|
||||
{
|
||||
return bddtrue;
|
||||
}
|
||||
|
||||
acc_cond::mark_t acc() const override
|
||||
{
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
// On-the-fly reachability game interface for a max even parity game such
|
||||
// that a target is reachable iff there is a memoryless winning strategy
|
||||
// in the parity game for player 1.
|
||||
class reachability_game final: public twa
|
||||
{
|
||||
private:
|
||||
typedef std::unordered_map<spot::reachability_state, unsigned,
|
||||
spot::reachability_state_hash> wincount_t;
|
||||
typedef std::unordered_map<spot::reachability_state,
|
||||
std::vector<spot::reachability_state>,
|
||||
spot::reachability_state_hash> parents_t;
|
||||
|
||||
const parity_game& pg_;
|
||||
// number of successors that need to have a winning strategy in order for
|
||||
// a given node to have a winning strategy.
|
||||
wincount_t c_;
|
||||
parents_t parents_;
|
||||
const_reachability_state_ptr init_state_; // cache
|
||||
|
||||
public:
|
||||
|
||||
reachability_game(const parity_game& pg)
|
||||
: twa(std::make_shared<bdd_dict>()), pg_(pg)
|
||||
{
|
||||
init_state_ = std::shared_ptr<const reachability_state>(get_init_state());
|
||||
}
|
||||
|
||||
const reachability_state* get_init_state() const override;
|
||||
|
||||
reachability_game_succ_iterator* succ_iter(const state* s) const override;
|
||||
|
||||
std::string format_state(const state* s) const override;
|
||||
|
||||
bool is_reachable();
|
||||
|
||||
private:
|
||||
bool mark(const spot::reachability_state& s);
|
||||
|
||||
bool is_target(const reachability_state& s);
|
||||
|
||||
};
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -104,7 +104,11 @@ diff out exp
|
|||
cat >exp <<EOF
|
||||
translating formula done
|
||||
split inputs and outputs done
|
||||
automaton has 9 states
|
||||
determinization done
|
||||
dpa has 14 states
|
||||
simulation done
|
||||
dpa has 14 states
|
||||
parity game built
|
||||
EOF
|
||||
ltlsynt --ins='a' --outs='b' -f 'GFa <-> GFb' --verbose --realizability 2> out
|
||||
|
|
@ -179,10 +183,11 @@ for i in 0 1 7 8 9; do
|
|||
IN=$(eval echo \$IN$i)
|
||||
OUT=$(eval echo \$OUT$i)
|
||||
EXP=$(eval echo \$EXP$i)
|
||||
test $EXP = $(ltlsynt -f "$F" --ins="$IN" --outs="$OUT" --realizability \
|
||||
--algo=rec)
|
||||
test $EXP = $(ltlsynt -f "$F" --ins="$IN" --outs="$OUT" --realizability \
|
||||
--algo=qp)
|
||||
|
||||
for algo in sd ds; do
|
||||
test $EXP = $(ltlsynt -f "$F" --ins="$IN" --outs="$OUT" --realizability \
|
||||
--algo=$algo)
|
||||
done
|
||||
done
|
||||
|
||||
for i in 2 3 4 5 6 10; do
|
||||
|
|
@ -190,11 +195,18 @@ for i in 2 3 4 5 6 10; do
|
|||
IN=$(eval echo \$IN$i)
|
||||
OUT=$(eval echo \$OUT$i)
|
||||
EXP=$(eval echo \$EXP$i)
|
||||
ltlsynt -f "$F" --ins="$IN" --outs="$OUT" > out$i
|
||||
REAL=`head -1 out$i`
|
||||
test $REAL = $EXP
|
||||
tail -n +2 out$i > res$i
|
||||
|
||||
ltl2tgba -f "!($F)" > negf_aut$i
|
||||
# check that the L(strategy) is included in L(F)
|
||||
autfilt -q -v --intersect=negf_aut$i res$i
|
||||
|
||||
# test ltlsynt
|
||||
for algo in sd ds; do
|
||||
ltlsynt -f "$F" --ins="$IN" --outs="$OUT" --algo=$algo > out$i || true
|
||||
REAL=`head -1 out$i`
|
||||
test $REAL = $EXP
|
||||
tail -n +2 out$i > res$i
|
||||
# check that the L(strategy) is included in L(F)
|
||||
autfilt -q -v --intersect=negf_aut$i res$i
|
||||
# check that all environment actions are possible
|
||||
autfilt --remove-ap="$OUT" res$i | autfilt --dualize | autfilt --is-empty -q
|
||||
done
|
||||
done
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue