Add satisficing FDSS 2023 portfolio.

driver/portfolios/seq_opt_fdss_2023.py

@@ -0,0 +1,80 @@
+"""
+This is the "Fast Downward Stone Soup 2023" sequential portfolio that
+participated in the IPC 2023 optimal track.
+
+Clemens Büchner, Remo Christen, Augusto Blaas Corrêa, Salomé Eriksson, Patrick Ferber, Jendrik Seipp and Silvan Sievers.
+Fast Downward Stone Soup 2023.
+In Tenth International Planning Competition (IPC 2023), Deterministic Part. 2023.
+"""
+
+SAS_FILE = "output.sas"
+OPTIMAL = True
+
+CONFIGS_STRIPS = [
+    # ipdb-60s-por
+    (542, ['--search', 'astar(ipdb(max_time=60), pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))']),
+    # can-cegar-10s-por
+    (93, ['--search', 'astar(cpdbs(multiple_cegar(max_pdb_size=1000000,max_collection_size=10000000,pattern_generation_max_time=infinity,total_max_time=10,stagnation_limit=2.0,blacklist_trigger_percentage=0.75,enable_blacklist_on_stagnation=true,use_wildcard_plans=true)), pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))']),
+    # mas-ssc-sbmiasm-300s-por
+    (213, ['--search', 'astar(merge_and_shrink(shrink_strategy=shrink_bisimulation(greedy=false), merge_strategy=merge_sccs(order_of_sccs=topological, merge_selector=score_based_filtering(scoring_functions=[sf_miasm(shrink_strategy=shrink_bisimulation(greedy=false),max_states=50000,threshold_before_merge=1),total_order(atomic_ts_order=reverse_level,product_ts_order=new_to_old,atomic_before_product=false)])), label_reduction=exact(before_shrinking=true, before_merging=false), max_states=50k, threshold_before_merge=1, main_loop_max_time=300), pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))']),
+    # bjolp
+    (206, ['--search', 'let(lmc, landmark_cost_partitioning(lm_merged([lm_rhw(), lm_hm(m=1)])), astar(lmc,lazy_evaluator=lmc))']),
+    # seq-lmcut-por
+    (105, ['--search', 'astar(operatorcounting([state_equation_constraints(), lmcut_constraints()], lpsolver=cplex), pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))']),
+    # potential-initial-state
+    (83, ['--search', 'astar(initial_state_potential())']),
+    # cartesian-cegar-landmarks-goals-60s-por
+    (96, ['--search', 'astar(cegar(subtasks=[landmarks(order=random), goals(order=random)], max_states=infinity, max_transitions=infinity, max_time=60), pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))']),
+    # can-sys3
+    (218, ['--search', 'astar(cpdbs(patterns=systematic(3)))']),
+    # seq-lmcut-hplus-relaxed
+    (91, ['--search', 'astar(operatorcounting([state_equation_constraints(), lmcut_constraints(), delete_relaxation_constraints(use_time_vars=false, use_integer_vars=false)], lpsolver=cplex))']),
+]
+
+CONFIGS_COND_EFFS = [
+    # mas-ssc-dfp-60s
+    (1137, ['--search', 'astar(merge_and_shrink(shrink_strategy=shrink_bisimulation(greedy=false), merge_strategy=merge_sccs(order_of_sccs=topological, merge_selector=score_based_filtering(scoring_functions=[goal_relevance(), dfp(), total_order(atomic_ts_order=reverse_level,product_ts_order=new_to_old,atomic_before_product=false)])), label_reduction=exact(before_shrinking=true, before_merging=false), max_states=50k, threshold_before_merge=1, main_loop_max_time=60))']),
+    # mas-ssc-sbmiasm-300s
+    (346, ['--search', 'astar(merge_and_shrink(shrink_strategy=shrink_bisimulation(greedy=false), merge_strategy=merge_sccs(order_of_sccs=topological, merge_selector=score_based_filtering(scoring_functions=[sf_miasm(shrink_strategy=shrink_bisimulation(greedy=false),max_states=50000,threshold_before_merge=1),total_order(atomic_ts_order=reverse_level,product_ts_order=new_to_old,atomic_before_product=false)])), label_reduction=exact(before_shrinking=true, before_merging=false), max_states=50k, threshold_before_merge=1, main_loop_max_time=300))']),
+    # hmax
+    (229, ['--search', 'astar(hmax())']),
+]
+
+CONFIGS_AXIOMS = [
+    (1800, ['--search', 'astar(blind())']),
+]
+
+
+def get_pddl_features(task):
+    has_axioms = False
+    has_conditional_effects = False
+    with open(task) as f:
+        in_op = False
+        for line in f:
+            line = line.strip()
+            if line == "begin_rule":
+                has_axioms = True
+
+            if line == "begin_operator":
+                in_op = True
+                next(f)  # Skip line with operator name.
+            elif line == "end_operator":
+                in_op = False
+            elif in_op:
+                parts = line.split()
+                if len(parts) >= 6 and all(p.lstrip('-').isdigit() for p in parts):
+                    has_conditional_effects = True
+    return has_axioms, has_conditional_effects
+
+
+HAS_AXIOMS, HAS_CONDITIONAL_EFFECTS = get_pddl_features(SAS_FILE)
+
+print(f"Task has axioms: {HAS_AXIOMS}")
+print(f"Task has conditional effects: {HAS_CONDITIONAL_EFFECTS}")
+
+if HAS_AXIOMS:
+    CONFIGS = CONFIGS_AXIOMS
+elif HAS_CONDITIONAL_EFFECTS:
+    CONFIGS = CONFIGS_COND_EFFS
+else:
+    CONFIGS = CONFIGS_STRIPS

driver/portfolios/seq_sat_fdss_2023.py

@@ -0,0 +1,65 @@
+"""
+This is the "Fast Downward Stone Soup 2023" sequential portfolio that
+participated in the IPC 2023 agile and satisficing tracks.
+
+Clemens Büchner, Remo Christen, Augusto Blaas Corrêa, Salomé Eriksson, Patrick Ferber, Jendrik Seipp and Silvan Sievers.
+Fast Downward Stone Soup 2023.
+In Tenth International Planning Competition (IPC 2023), Deterministic Part. 2023.
+"""
+
+OPTIMAL = False
+
+CONFIGS = [
+    # fdss-2018-01
+    (383, ['--search', 'let(hlm, landmark_sum(lm_reasonable_orders_hps(lm_rhw()),transform=adapt_costs(one)),let(hff, ff(transform=adapt_costs(one)),lazy(alt([single(hff),single(hff,pref_only=true),single(hlm),single(hlm,pref_only=true),type_based([hff,g()])],boost=1000),preferred=[hff,hlm],cost_type=one,reopen_closed=false,randomize_successors=true,preferred_successors_first=false,bound=BOUND, verbosity=silent)))']),
+    # fdss-2018-03
+    (57, ['--search', 'let(hlm, landmark_sum(lm_reasonable_orders_hps(lm_rhw()),transform=adapt_costs(one)),let(hff, ff(transform=adapt_costs(one)),lazy(alt([single(hff),single(hff,pref_only=true),single(hlm),single(hlm,pref_only=true)],boost=1000),preferred=[hff,hlm],cost_type=one,reopen_closed=false,randomize_successors=false,preferred_successors_first=true,bound=BOUND, verbosity=silent)))']),
+    # lazy_hff_hlm-epsilon-greedy_pref_ops-no-reasonable-orders
+    (60, ['--search', 'let(hlm, landmark_sum(lm_rhw(), pref=true, transform=adapt_costs(one)),let(hff, ff(transform=adapt_costs(one)),lazy(alt([single(hff),single(hff,pref_only=true),epsilon_greedy(hlm),single(hlm,pref_only=true)],boost=1000),preferred=[hff,hlm],cost_type=one,reopen_closed=false,randomize_successors=true, bound=BOUND, verbosity=silent)))']),
+    # fdss-2014-01
+    (22, ['--search', 'let(hadd, add(transform=adapt_costs(one)),let(hlm, landmark_sum(lm_reasonable_orders_hps(lm_rhw()),transform=adapt_costs(plusone)),lazy_greedy([hadd,hlm],preferred=[hadd,hlm],cost_type=one,bound=BOUND, verbosity=silent)))']),
+    # fdss-2018-04
+    (30, ['--search', 'let(hff, ff(transform=adapt_costs(one)),let(hlm, landmark_sum(lm_reasonable_orders_hps(lm_rhw()),transform=adapt_costs(one)),eager_greedy([hff,hlm],preferred=[hff,hlm],cost_type=one,bound=BOUND, verbosity=silent)))']),
+    # fdss-2014-08
+    (206, ['--search', 'let(hff, ff(transform=adapt_costs(one)),let(hadd, add(transform=adapt_costs(one)),lazy_greedy([hadd,hff],preferred=[hadd,hff],cost_type=one,bound=BOUND, verbosity=silent)))']),
+    # fdss-2014-03
+    (30, ['--search', 'let(hadd, add(transform=adapt_costs(one)),let(hlm, landmark_sum(lm_reasonable_orders_hps(lm_rhw()),transform=adapt_costs(plusone)),eager_greedy([hadd,hlm],preferred=[hadd,hlm],cost_type=one,bound=BOUND, verbosity=silent)))']),
+    # fdss-2018-07
+    (21, ['--search', 'let(hcea, cea(transform=adapt_costs(one)),let(hlm, landmark_sum(lm_reasonable_orders_hps(lm_rhw()),transform=adapt_costs(one)),lazy_greedy([hcea,hlm],preferred=[hcea,hlm],cost_type=one,bound=BOUND, verbosity=silent)))']),
+    # fdss-2018-09
+    (59, ['--search', 'let(hff, ff(transform=adapt_costs(one)),lazy(alt([single(sum([g(),weight(hff,10)])),single(sum([g(),weight(hff,10)]),pref_only=true)],boost=2000),preferred=[hff],reopen_closed=false,cost_type=one,bound=BOUND, verbosity=silent))']),
+    # fdss-2014-11
+    (89, ['--search', 'let(hff, ff(transform=adapt_costs(one)),let(hlm, landmark_sum(lm_reasonable_orders_hps(lm_rhw()),transform=adapt_costs(plusone)),lazy_wastar([hff,hlm],w=3,preferred=[hff,hlm],cost_type=one,bound=BOUND, verbosity=silent)))']),
+    # fdss-2018-14
+    (29, ['--search', 'let(hgoalcount, goalcount(transform=adapt_costs(plusone)),let(hff, ff(),lazy(alt([single(sum([g(),weight(hff,10)])),single(sum([g(),weight(hff,10)]),pref_only=true),single(sum([g(),weight(hgoalcount,10)])),single(sum([g(),weight(hgoalcount,10)]),pref_only=true)],boost=2000),preferred=[hff,hgoalcount],reopen_closed=false,cost_type=one,bound=BOUND, verbosity=silent)))']),
+    # fdss-1-03
+    (53, ['--search', 'let(hcea, cea(transform=adapt_costs(one)),let(hcg, cg(transform=adapt_costs(one)),lazy_greedy([hcea,hcg],preferred=[hcea,hcg],cost_type=one,bound=BOUND, verbosity=silent)))']),
+    # fdss-2014-16
+    (19, ['--search', 'let(hcg, cg(transform=adapt_costs(one)),let(hff, ff(transform=adapt_costs(one)),lazy_wastar([hcg,hff],w=3,preferred=[hcg,hff],cost_type=one,bound=BOUND, verbosity=silent)))']),
+    # fdss-2018-18
+    (177, ['--search', 'let(hcg, cg(transform=adapt_costs(plusone)),lazy(alt([type_based([g()]),single(hcg),single(hcg,pref_only=true)],boost=0),preferred=[hcg],reopen_closed=true,cost_type=plusone,bound=BOUND, verbosity=silent))']),
+    # fdss-2014-13
+    (30, ['--search', 'let(hcg, cg(transform=adapt_costs(one)),let(hlm, landmark_sum(lm_reasonable_orders_hps(lm_rhw()),transform=adapt_costs(plusone)),eager_greedy([hcg,hlm],preferred=[hcg,hlm],cost_type=one,bound=BOUND, verbosity=silent)))']),
+    # fdss-2014-18
+    (30, ['--search', 'let(hadd, add(transform=adapt_costs(one)),eager(alt([single(sum([g(), weight(hadd, 3)])),single(sum([g(), weight(hadd,3)]),pref_only=true)]),preferred=[hadd],cost_type=one,bound=BOUND, verbosity=silent))']),
+    # fdss-1-11
+    (26, ['--search', 'let(h, cea(transform=adapt_costs(one)),eager_greedy([h],preferred=[h],cost_type=one,bound=BOUND, verbosity=silent))']),
+    # fdss-2014-19
+    (27, ['--search', 'let(hff, ff(transform=adapt_costs(one)),let(hcea, cea(transform=adapt_costs(one)),eager(alt([single(sum([g(),weight(hff,3)])),single(sum([g(),weight(hff,3)]),pref_only=true),single(sum([g(),weight(hcea,3)])),single(sum([g(),weight(hcea,3)]),pref_only=true)]),preferred=[hff,hcea],cost_type=one,bound=BOUND, verbosity=silent)))']),
+    # fdss-2018-02
+    (18, ['--search', 'let(lmg, lm_rhw(only_causal_landmarks=false,disjunctive_landmarks=true,use_orders=false),let(hlm, landmark_cost_partitioning(lmg,transform=adapt_costs(one)),let(hff, ff(transform=adapt_costs(one)),lazy(alt([type_based([g()]),single(hlm),single(hlm,pref_only=true),single(hff),single(hff,pref_only=true)],boost=0),preferred=[hlm],reopen_closed=false,cost_type=plusone,bound=BOUND, verbosity=silent))))']),
+    # fdss-2018-16
+    (29, ['--search', 'let(lmg, lm_rhw(only_causal_landmarks=false,disjunctive_landmarks=false,use_orders=true),let(hlm, landmark_sum(lmg,transform=adapt_costs(one)),let(hff, ff(transform=adapt_costs(one)),let(hblind, blind(),lazy(alt([type_based([g()]),single(sum([g(),weight(hblind,2)])),single(sum([g(),weight(hblind,2)]),pref_only=true),single(sum([g(),weight(hlm,2)])),single(sum([g(),weight(hlm,2)]),pref_only=true),single(sum([g(),weight(hff,2)])),single(sum([g(),weight(hff,2)]),pref_only=true)],boost=4419),preferred=[hlm],reopen_closed=true,cost_type=one,bound=BOUND, verbosity=silent)))))']),
+    # fdss-2018-29
+    (90, ['--search', 'let(hadd, add(transform=adapt_costs(plusone)),let(hff, ff(),lazy(alt([tiebreaking([sum([weight(g(),4),weight(hff,5)]),hff]),tiebreaking([sum([weight(g(),4),weight(hff,5)]),hff],pref_only=true),tiebreaking([sum([weight(g(),4),weight(hadd,5)]),hadd]),tiebreaking([sum([weight(g(),4),weight(hadd,5)]),hadd],pref_only=true)],boost=2537),preferred=[hff,hadd],reopen_closed=true,bound=BOUND, verbosity=silent)))']),
+    # fdss-2018-31
+    (28, ['--search', 'let(hff, ff(transform=adapt_costs(one)),lazy(alt([single(sum([weight(g(),2),weight(hff,3)])),single(sum([weight(g(),2),weight(hff,3)]),pref_only=true)],boost=5000),preferred=[hff],reopen_closed=true,cost_type=one,bound=BOUND, verbosity=silent))']),
+    # fdss-2018-28
+    (29, ['--search', 'let(hblind, blind(),let(hadd, add(),let(hcg, cg(transform=adapt_costs(one)),let(hhmax, hmax(),eager(alt([tiebreaking([sum([g(),weight(hblind,7)]),hblind]),tiebreaking([sum([g(),weight(hhmax,7)]),hhmax]),tiebreaking([sum([g(),weight(hadd,7)]),hadd]),tiebreaking([sum([g(),weight(hcg,7)]),hcg])],boost=2142),preferred=[],reopen_closed=true,bound=BOUND, verbosity=silent)))))']),
+    # fdss-2018-35
+    (85, ['--search', 'let(lmg, lm_hm(conjunctive_landmarks=false,use_orders=false,m=1),let(hcg, cg(transform=adapt_costs(one)),let(hlm, landmark_cost_partitioning(lmg),lazy(alt([single(hlm),single(hlm,pref_only=true),single(hcg),single(hcg,pref_only=true)],boost=0),preferred=[hcg],reopen_closed=false,cost_type=one,bound=BOUND, verbosity=silent))))']),
+    # fdss-2018-27
+    (30, ['--search', 'let(lmg, lm_reasonable_orders_hps(lm_rhw(only_causal_landmarks=true,disjunctive_landmarks=true,use_orders=true)),let(hblind, blind(),let(hadd, add(),let(hlm, landmark_sum(lmg,pref=true,transform=adapt_costs(plusone)),let(hff, ff(),lazy(alt([single(sum([weight(g(),2),weight(hblind,3)])),single(sum([weight(g(),2),weight(hblind,3)]),pref_only=true),single(sum([weight(g(),2),weight(hff,3)])),single(sum([weight(g(),2),weight(hff,3)]),pref_only=true),single(sum([weight(g(),2),weight(hlm,3)])),single(sum([weight(g(),2),weight(hlm,3)]),pref_only=true),single(sum([weight(g(),2),weight(hadd,3)])),single(sum([weight(g(),2),weight(hadd,3)]),pref_only=true)],boost=2474),preferred=[hadd],reopen_closed=false,cost_type=one,bound=BOUND, verbosity=silent))))))']),
+    # fdss-2018-39
+    (59, ['--search', 'let(lmg, lm_exhaust(only_causal_landmarks=false),let(hgoalcount, goalcount(transform=adapt_costs(plusone)),let(hlm, landmark_sum(lmg),let(hff, ff(),let(hblind, blind(),eager(alt([tiebreaking([sum([weight(g(),8),weight(hblind,9)]),hblind]),tiebreaking([sum([weight(g(),8),weight(hlm,9)]),hlm]),tiebreaking([sum([weight(g(),8),weight(hff,9)]),hff]),tiebreaking([sum([weight(g(),8),weight(hgoalcount,9)]),hgoalcount])],boost=2005),preferred=[],reopen_closed=true,bound=BOUND, verbosity=silent))))))']),
+]

driver/tests.py

@@ -27,16 +27,27 @@ def translate():
            "misc/tests/benchmarks/gripper/prob01.pddl"]
     subprocess.check_call(cmd, cwd=REPO_ROOT_DIR)
 
+# We need to translate the example task when this module is imported to have the
+# SAS+ file ready for the @pytest.parametrize function below. Translating the
+# task in setup_module() does not work here, because the @pytest.parametrize
+# decorator is executed before setup_module() is called. An alternative would be
+# to use a conftest.py file and call translate() in a pytest_sessionstart()
+# function, but that adds another file and leads to dumping the translator
+# output to the terminal.
+translate()
+
 
 def cleanup():
     subprocess.check_call([sys.executable, "fast-downward.py", "--cleanup"],
                           cwd=REPO_ROOT_DIR)
 
 
-def run_driver(parameters):
+def teardown_module(module):
     cleanup()
-    translate()
-    cmd = [sys.executable, "fast-downward.py"] + parameters
+
+
+def run_driver(parameters):
+    cmd = [sys.executable, "fast-downward.py", "--keep"] + parameters
     return subprocess.check_call(cmd, cwd=REPO_ROOT_DIR)
 
 
@@ -72,16 +83,19 @@ def _get_portfolio_configs(portfolio: Path):
         traceback.print_exc()
         raise SyntaxError(
             f"The portfolio {portfolio} could not be loaded.")
-    if "CONFIGS" not in attributes:
-        raise ValueError("portfolios must define CONFIGS")
-    return [config for _, config in attributes["CONFIGS"]]
+    for key, value in attributes.items():
+        # The optimal FDSS 2023 portfolio defines different configs for different PDDL subsets.
+        if key.startswith("CONFIGS"):
+            for _, config in value:
+                yield config
 
 
 def _convert_to_standalone_config(config):
     replacements = [
         ("H_COST_TRANSFORM", "no_transform()"),
         ("S_COST_TYPE", "normal"),
         ("BOUND", "infinity"),
+        ("lpsolver=cplex", "lpsolver=soplex"),  # CPLEX is not available in all CI runners.
     ]
     for index, part in enumerate(config):
         for before, after in replacements:
@@ -97,13 +111,17 @@ def _run_search(config):
         stdin="output.sas")
 
 
-def test_portfolio_configs():
+def _get_all_portfolio_configs():
     all_configs = set()
     for portfolio in PORTFOLIOS.values():
         configs = _get_portfolio_configs(Path(portfolio))
         all_configs |= set(tuple(_convert_to_standalone_config(config)) for config in configs)
-    for config in all_configs:
-        _run_search(config)
+    return all_configs
+
+
+@pytest.mark.parametrize("config", _get_all_portfolio_configs())
+def test_portfolio_config(config):
+    _run_search(config)
 
 
 @pytest.mark.skipif(not limits.can_set_time_limit(), reason="Cannot set time limits on this system")