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How to use hasstate method in pyatom
Best Python code snippet using pyatom_python
unknown_no_occup.py
Source:unknown_no_occup.py 
1from stripstream.pddl.objects import EasyType as Type, EasyParameter as Param2from stripstream.pddl.logic.predicates import EasyPredicate as Predicate3from stripstream.pddl.operators import Action, Axiom4from stripstream.pddl.logic.connectives import And, Not5from stripstream.algorithms.incremental.incremental_planner import incremental_planner6from stripstream.algorithms.search.fast_downward import get_fast_downward7from stripstream.pddl.utils import convert_plan, rename_easy8from stripstream.pddl.problem import STRIPStreamProblem9from stripstream.pddl.examples.belief.problems import *10from stripstream.pddl.logic.predicates import Function11from stripstream.pddl.logic.operations import Initialize, Cost12from stripstream.algorithms.plan import plan_cost13from stripstream.pddl.logic.quantifiers import Exists, ForAll14from stripstream.pddl.logic.atoms import Equal15from stripstream.pddl.examples.belief.utils import *16from toyTest import glob, makeOperators, Bd, ObjState, ObjLoc17import toyTest18OPERATOR_MAP = {19 'find': make_look,20 'inspect_loc': make_look_clear,21 'inspect_state': make_look_state,22 'transport': make_transport,23 'wash': make_wash,24 'paint': make_paint,25 'dry': make_dry,26}27COST_SCALE = 1028OBJ, LOC, STATE = Type(), Type(), Type()29At = Predicate(OBJ, LOC)30HasState = Predicate(OBJ, STATE)31Clear = Predicate(LOC)32Safe = Predicate(OBJ, LOC)33IsDryer = Predicate(LOC)34IsPainter = Predicate(LOC)35IsWasher = Predicate(LOC)36UnsureLoc = Predicate(OBJ) # NOTE - can also just make these objects37UnsureState = Predicate(OBJ)38UnsureClear = Predicate(LOC)39NotAtLoc = Predicate(OBJ, LOC)40FindCost = Function('find_cost', [OBJ, LOC])41InspectStateCost = Function('inspect_state_cost', [OBJ, STATE])42O, O2, L1, L2, S = Param(OBJ), Param(OBJ), Param(LOC), Param(LOC), Param(STATE)43rename_easy(locals())44actions = [45 Action(name='find', parameters=[O, L1], # Policy to find the object or an inspection?46 condition=And(UnsureLoc(O), Not(NotAtLoc(O, L1))),47 effect=And(At(O, L1), Not(UnsureLoc(O)), Cost(FindCost(O, L1)))), # TODO - can even adjust the cost of these to bias search towards more likely48 Action(name='inspect_state', parameters=[O, L1, S],49 condition=And(UnsureState(O), At(O, L1)), # Probably should know where it is before you worry about its state50 #condition=And(UnsureState(O), Not(UnsureLoc(O))), # Probably should know where it is before you worry about its state51 effect=And(HasState(O, S), Not(UnsureState(O)), Cost(InspectStateCost(O, S)))), # TODO - should I factor loc in the cost?52 Action(name='transport', parameters=[O, L1, L2], # NOTE - Leslie and Tomas call this Move53 #condition=And(At(O, L1)),54 condition=And(At(O, L1), Clear(L2)),55 effect=And(At(O, L2), Not(At(O, L1))),56 cost=COST_SCALE*1),57 Action(name='wash', parameters=[O, L1, S],58 condition=And(At(O, L1), HasState(O, S), IsWasher(L1)),59 effect=And(HasState(O, clean), Not(HasState(O, S))),60 cost=COST_SCALE*1),61 Action(name='paint', parameters=[O, L1],62 condition=And(At(O, L1), HasState(O, clean), IsPainter(L1)),63 effect=And(HasState(O, wet), Not(HasState(O, clean))),64 cost=COST_SCALE*1),65 Action(name='dry', parameters=[O, L1],66 condition=And(At(O, L1), HasState(O, wet), IsDryer(L1)),67 effect=And(HasState(O, dry), Not(HasState(O, wet))),68 cost=COST_SCALE*1),69 Axiom(effect=Clear(L2), condition=ForAll([O2], Safe(O2, L2))),70 Axiom(effect=Safe(O, L1), condition=Exists([L2], And(At(O, L2), Not(Equal(L1, L2))))), # NOTE - automatically includes UnsureLoc71]72# TODO - do I need lower confidence bound that the object isn't there to prevent it from doing nothing or impossible things?73LOC_CONFIDENCE = .9574STATE_CONFIDENCE = LOC_CONFIDENCE75CLEAR_CONFIDENCE = STATE_CONFIDENCE76MIN_CONFIDENCE = .001 # TODO - how does this work in continuous domains?77MIN_P = 1e-678def observable_problem(belief, goal, costs=True): # NOTE - costs is really important here79 #objects = belief.objLoc.keys()80 locations = belief.occupancies.keys()81 states = [dirty, clean, dry, wet]82 initial_atoms = []83 occupied = set()84 for obj in belief.objLoc.keys():85 dist = belief.objLocDist(obj)86 loc, p = dist.computeMPE() # TODO - concentration inequalities for Gaussian87 if p >= LOC_CONFIDENCE:88 initial_atoms.append(At(obj, loc))89 occupied.add(loc)90 else:91 initial_atoms.append(UnsureLoc(obj))92 for loc in locations:93 p = dist.prob(loc)94 cost = int(COST_SCALE*1./max(p, MIN_P)) if costs else COST_SCALE*1 # TODO - set to infinity if too large (equivalent to blocking)95 initial_atoms.append(Initialize(FindCost(OBJ(obj), LOC(loc)), cost))96 #if p < MIN_CONFIDENCE:97 # initial_atoms.append(NotAtLoc(obj, loc))98 for obj in belief.objState.keys():99 dist = belief.objState[obj]100 state, p = dist.computeMPE()101 if p >= STATE_CONFIDENCE:102 initial_atoms.append(HasState(obj, state))103 else:104 initial_atoms.append(UnsureState(obj))105 for state in states:106 p = dist.prob(state)107 cost = int(COST_SCALE*1./max(p, MIN_P)) if costs else COST_SCALE*1108 initial_atoms.append(Initialize(InspectStateCost(OBJ(obj), STATE(state)), cost))109 #if p < MIN_CONFIDENCE:110 # initial_atoms.append(NotHasState(obj, loc))111 for loc in belief.occupancies.keys():112 if loc == 'washer':113 initial_atoms.append(IsWasher(loc))114 elif loc == 'painter':115 initial_atoms.append(IsPainter(loc))116 elif loc == 'dryer':117 initial_atoms.append(IsDryer(loc))118 goal_literals = []119 for fluent in goal.fluents:120 if isinstance(fluent, Bd):121 literal, arg, prob = fluent.args122 if isinstance(literal, ObjLoc):123 goal_literals.append(At(literal.args[0], literal.value))124 elif isinstance(literal, ObjState):125 goal_literals.append(HasState(literal.args[0], arg))126 elif isinstance(literal, toyTest.Clear):127 goal_literals.append(Clear(literal.args[0]))128 else:129 raise NotImplementedError(literal)130 else:131 raise NotImplementedError(fluent)132 return STRIPStreamProblem(initial_atoms, goal_literals, actions, [], [])133##################################################134def unknown_policy(operators, goal, max_cost=10000):135 def fn(belief):136 problem = observable_problem(belief, goal)137 #print problem138 # NOTE - the cost sensitivity starts to factor into this...139 # NOTE - max cost prevents extremely large cost actions140 search = get_fast_downward('astar', verbose=False, max_cost=max_cost) # dijkstra | astar | wastar1 | wastar2 | wastar3 | eager | lazy141 plan, universe = incremental_planner(problem, search=search, frequency=1)142 cost = plan_cost(universe, plan)143 plan = convert_plan(plan)144 print 'Plan:', plan145 print 'Cost:', cost146 print 'Length:', len(plan)147 if plan is None or not plan:148 return None149 action, params = plan[0]150 print 'Action:', action, params151 print152 return OPERATOR_MAP[action.name](operators, *params)153 return fn154##################################################155#operator.__dict__.keys() = ['configurationCache', 'prim', 'conditionOnPreconds', 'delayBinding', 'results', 'cost', 'instanceCost',156# 'subPlans', 'functions', 'sideEffects', 'rebindPenalty', 'concreteAbstractionLevel', 'argsToPrint', 'parent',157# 'abstractionLevel', 'args', 'metaOperator', 'preconditions', 'closedLoopPrim', 'ignorableArgs', 'name',158# 'f', 'specialRegress', 'num']159def run_unknown():160 problem_fn = test8 # test0 | test1 | test8161 env, start, goal = problem_fn()162 global operatorDict163 operatorDict = makeOperators(glob.failProbs)164 print165 for name, operator in operatorDict.iteritems():166 print 'Name:', name167 print 'Args:', [arg for i, arg in enumerate(operator.args) if i not in operator.ignorableArgs]168 print 'Pre:', operator.preconditions169 print 'Eff:', operator.results170 print 'Side Eff:', operator.sideEffects171 print operator.__dict__172 print173 problem = observable_problem(start.details, goal)174 print problem175 search = get_fast_downward('eager') # dijkstra | astar | wastar1 | wastar2 | wastar3 | eager | lazy176 plan, _ = incremental_planner(problem, search=search, frequency=1)...
observable_2.py
Source:observable_2.py
1from stripstream.pddl.objects import EasyType as Type, EasyParameter as Param2from stripstream.pddl.logic.predicates import EasyPredicate as Predicate3from stripstream.pddl.operators import Action4from stripstream.pddl.logic.connectives import And, Not5from stripstream.algorithms.incremental.incremental_planner import incremental_planner6from stripstream.algorithms.search.fast_downward import get_fast_downward7from stripstream.pddl.utils import convert_plan, rename_easy8from stripstream.pddl.problem import STRIPStreamProblem9from stripstream.pddl.examples.belief.problems import *10from stripstream.pddl.examples.belief.utils import *11from stripstream.pddl.examples.belief.unknown import OPERATOR_MAP12from toyTest import glob, makeOperators, Bd, ObjState, ObjLoc13OBJ, LOC, STATE = Type(), Type(), Type()14At = Predicate(OBJ, LOC)15HasState = Predicate(OBJ, STATE)16Clear = Predicate(LOC)17IsDryer = Predicate(LOC)18IsPainter = Predicate(LOC)19IsWasher = Predicate(LOC)20O, L1, L2, S = Param(OBJ), Param(LOC), Param(LOC), Param(STATE)21actions = [22 Action(name='transport', parameters=[O, L1, L2],23 condition=And(At(O, L1), Clear(L2)),24 effect=And(At(O, L2), Clear(L1), Not(At(O, L1)), Not(Clear(L2)))), # NOTE - Leslie and Tomas call this Move25 Action(name='wash', parameters=[O, L1, S],26 condition=And(At(O, L1), HasState(O, S), IsWasher(L1)),27 effect=And(HasState(O, clean), Not(HasState(O, S)))),28 Action(name='paint', parameters=[O, L1],29 condition=And(At(O, L1), HasState(O, clean), IsPainter(L1)),30 effect=And(HasState(O, wet), Not(HasState(O, clean)))),31 Action(name='dry', parameters=[O, L1],32 condition=And(At(O, L1), HasState(O, wet), IsDryer(L1)),33 effect=And(HasState(O, dry), Not(HasState(O, wet)))),34]35rename_easy(locals())36##################################################37def make_goal_literals(goal):38 goal_literals = []39 for fluent in goal.fluents:40 if isinstance(fluent, Bd):41 literal, arg, prob = fluent.args42 if isinstance(literal, ObjLoc):43 goal_literals.append(At(literal.args[0], literal.value))44 elif isinstance(literal, ObjState):45 goal_literals.append(HasState(literal.args[0], arg))46 else:47 raise NotImplementedError(literal)48 else:49 raise NotImplementedError(fluent)50 return goal_literals51def make_initial_atoms(locations, states, occupancies):52 initial_atoms = []53 initial_atoms += [At(*pair) for pair in locations.iteritems()]54 initial_atoms += [HasState(*pair) for pair in states.iteritems()]55 initial_atoms += [Clear(loc) for loc, occ in occupancies.iteritems() if not occ]56 for loc in occupancies:57 if loc == 'washer':58 initial_atoms.append(IsWasher(loc))59 if loc == 'painter':60 initial_atoms.append(IsPainter(loc))61 if loc == 'dryer':62 initial_atoms.append(IsDryer(loc))63 return initial_atoms64def make_problem((l, s, o), goal):65 initial_atoms = make_initial_atoms(l, s, o)66 goal_literals = make_goal_literals(goal)67 return STRIPStreamProblem(initial_atoms, goal_literals, actions, [], []) # TODO - constants?68"""69def observable_problem(env, start, goal):70 locations = start.details.occupancies.keys()71 initial_atoms = []72 constants = []73 occupied = set()74 for obj in env.objects:75 for attr in env.objects[obj]:76 if attr in STATES:77 initial_atoms.append(HasState(obj, attr))78 elif attr in locations:79 initial_atoms.append(At(obj, attr))80 occupied.add(attr)81 else:82 raise NotImplementedError(attr)83 for loc in locations:84 if loc not in occupied:85 initial_atoms.append(Clear(loc))86 constants.append(LOC(loc))87 if loc == 'washer':88 initial_atoms.append(IsWasher(loc))89 if loc == 'painter':90 initial_atoms.append(IsPainter(loc))91 if loc == 'dryer':92 initial_atoms.append(IsDryer(loc))93 return STRIPStreamProblem(initial_atoms, make_goal(goal), actions, [], constants)94"""95##################################################96def observable_policy(operators, goal):97 def fn(belief):98 problem = make_problem(maximum_likelihood_obs(belief), goal)99 #problem = make_problem(sample_obs(belief), goal)100 #problem = make_problem(sample_consistent_obs(belief), goal)101 #print problem102 search = get_fast_downward('astar', verbose=False) # dijkstra | astar | wastar1 | wastar2 | wastar3 | eager | lazy103 plan, universe = incremental_planner(problem, search=search, frequency=1)104 plan = convert_plan(plan)105 print 'Plan:', plan106 print 'Length:', len(plan)107 if plan is None or not plan:108 return None109 action, params = plan[0]110 print 'Action:', action, params111 print112 return OPERATOR_MAP[action.name](operators, *params)113 return fn114##################################################115#operator.__dict__.keys() = ['configurationCache', 'prim', 'conditionOnPreconds', 'delayBinding', 'results', 'cost', 'instanceCost',116# 'subPlans', 'functions', 'sideEffects', 'rebindPenalty', 'concreteAbstractionLevel', 'argsToPrint', 'parent',117# 'abstractionLevel', 'args', 'metaOperator', 'preconditions', 'closedLoopPrim', 'ignorableArgs', 'name',118# 'f', 'specialRegress', 'num']119def run_observable_2():120 problem_fn = test0 # test0 | test1 | test8121 env, start, goal = problem_fn()122 global operatorDict123 operatorDict = makeOperators(glob.failProbs)124 print125 for name, operator in operatorDict.iteritems():126 print 'Name:', name127 print 'Args:', [arg for i, arg in enumerate(operator.args) if i not in operator.ignorableArgs]128 print 'Pre:', operator.preconditions129 print 'Eff:', operator.results130 print 'Side Eff:', operator.sideEffects131 print132 #problem = make_problem(convert_start(env, start), goal)133 #problem = make_problem(maximum_likelihood_obs(start.details), goal)134 #problem = make_problem(sample_obs(start.details), goal)135 problem = make_problem(sample_consistent_obs(start.details), goal)136 print problem137 search = get_fast_downward('eager') # dijkstra | astar | wastar1 | wastar2 | wastar3 | eager | lazy138 plan, _ = incremental_planner(problem, search=search, frequency=1)...
observable_no_occup_2.py
Source:observable_no_occup_2.py
1from stripstream.pddl.objects import EasyType as Type, EasyParameter as Param2from stripstream.pddl.logic.predicates import EasyPredicate as Predicate3from stripstream.pddl.operators import Action, Axiom4from stripstream.pddl.logic.connectives import And, Not5from stripstream.algorithms.incremental.incremental_planner import incremental_planner6from stripstream.algorithms.search.fast_downward import get_fast_downward7from stripstream.pddl.utils import convert_plan, rename_easy8from stripstream.pddl.problem import STRIPStreamProblem9from stripstream.pddl.examples.belief.problems import *10from stripstream.pddl.examples.belief.utils import *11from stripstream.pddl.examples.belief.unknown import OPERATOR_MAP12from stripstream.pddl.logic.quantifiers import Exists, ForAll13from stripstream.pddl.logic.atoms import Equal14from toyTest import glob, makeOperators, Bd, ObjState, ObjLoc15OBJ, LOC, STATE = Type(), Type(), Type()16At = Predicate(OBJ, LOC)17HasState = Predicate(OBJ, STATE)18Clear = Predicate(LOC)19Safe = Predicate(OBJ, LOC)20IsDryer = Predicate(LOC)21IsPainter = Predicate(LOC)22IsWasher = Predicate(LOC)23O, O2, L1, L2, S = Param(OBJ), Param(OBJ), Param(LOC), Param(LOC), Param(STATE)24actions = [25 Action(name='transport', parameters=[O, L1, L2],26 condition=And(At(O, L1), Clear(L2)),27 effect=And(At(O, L2), Not(At(O, L1)))), # NOTE - Leslie and Tomas call this Move28 Action(name='wash', parameters=[O, L1, S],29 condition=And(At(O, L1), HasState(O, S), IsWasher(L1)),30 effect=And(HasState(O, clean), Not(HasState(O, S)))),31 Action(name='paint', parameters=[O, L1],32 condition=And(At(O, L1), HasState(O, clean), IsPainter(L1)),33 effect=And(HasState(O, wet), Not(HasState(O, clean)))),34 Action(name='dry', parameters=[O, L1],35 condition=And(At(O, L1), HasState(O, wet), IsDryer(L1)),36 effect=And(HasState(O, dry), Not(HasState(O, wet)))),37 Axiom(effect=Clear(L2), condition=ForAll([O2], Safe(O2, L2))),38 Axiom(effect=Safe(O, L1), condition=Exists([L2], And(At(O, L2), Not(Equal(L1, L2))))),39]40rename_easy(locals())41##################################################42def make_goal_literals(goal):43 goal_literals = []44 for fluent in goal.fluents:45 if isinstance(fluent, Bd):46 literal, arg, prob = fluent.args47 if isinstance(literal, ObjLoc):48 goal_literals.append(At(literal.args[0], literal.value))49 elif isinstance(literal, ObjState):50 goal_literals.append(HasState(literal.args[0], arg))51 else:52 raise NotImplementedError(literal)53 else:54 raise NotImplementedError(fluent)55 return goal_literals56def make_initial_atoms(locations, states, occupancies):57 initial_atoms = []58 initial_atoms += [At(*pair) for pair in locations.iteritems()]59 initial_atoms += [HasState(*pair) for pair in states.iteritems()]60 for loc in occupancies:61 if loc == 'washer':62 initial_atoms.append(IsWasher(loc))63 if loc == 'painter':64 initial_atoms.append(IsPainter(loc))65 if loc == 'dryer':66 initial_atoms.append(IsDryer(loc))67 return initial_atoms68def make_problem((l, s, o), goal):69 initial_atoms = make_initial_atoms(l, s, o)70 goal_literals = make_goal_literals(goal)71 constants = map(LOC, o)72 return STRIPStreamProblem(initial_atoms, goal_literals, actions, [], constants)73##################################################74def observable_policy(operators, goal):75 def fn(belief):76 problem = make_problem(maximum_likelihood_obs(belief), goal)77 #problem = make_problem(sample_obs(belief), goal)78 #problem = make_problem(sample_consistent_obs(belief), goal)79 #print problem80 search = get_fast_downward('astar', verbose=False) # dijkstra | astar | wastar1 | wastar2 | wastar3 | eager | lazy81 plan, universe = incremental_planner(problem, search=search, frequency=1)82 plan = convert_plan(plan)83 print 'Plan:', plan84 print 'Length:', len(plan)85 if plan is None or not plan:86 return None87 action, params = plan[0]88 print 'Action:', action, params89 print90 return OPERATOR_MAP[action.name](operators, *params)91 return fn92##################################################93#operator.__dict__.keys() = ['configurationCache', 'prim', 'conditionOnPreconds', 'delayBinding', 'results', 'cost', 'instanceCost',94# 'subPlans', 'functions', 'sideEffects', 'rebindPenalty', 'concreteAbstractionLevel', 'argsToPrint', 'parent',95# 'abstractionLevel', 'args', 'metaOperator', 'preconditions', 'closedLoopPrim', 'ignorableArgs', 'name',96# 'f', 'specialRegress', 'num']97def run_observable_2():98 problem_fn = test8 # test0 | test1 | test899 env, start, goal = problem_fn()100 print101 print convert_start(env, start)102 print convert_goal(goal)103 #operators = makeOperators(glob.failProbs)104 #for name, operator in operators.iteritems():105 # print 'Name:', name106 # print 'Args:', [arg for i, arg in enumerate(operator.args) if i not in operator.ignorableArgs]107 # print 'Pre:', operator.preconditions108 # print 'Eff:', operator.results109 # print 'Side Eff:', operator.sideEffects110 # print111 #problem = make_problem(convert_start(env, start), goal)112 problem = make_problem(maximum_likelihood_obs(start.details), goal)113 #problem = make_problem(sample_obs(start.details), goal)114 #problem = make_problem(sample_consistent_obs(start.details), goal)115 print problem116 search = get_fast_downward('dijkstra') # dijkstra | astar | wastar1 | wastar2 | wastar3 | eager | lazy117 plan, _ = incremental_planner(problem, search=search, frequency=1)...
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