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planning.py
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"""Planning (Chapters 10-11)
"""
import copy
import itertools
from search import Node
from utils import Expr, expr, first
from logic import FolKB, conjuncts, unify
from collections import deque
from functools import reduce as _reduce
class PlanningProblem:
"""
Planning Domain Definition Language (PlanningProblem) used to define a search problem.
It stores states in a knowledge base consisting of first order logic statements.
The conjunction of these logical statements completely defines a state.
"""
def __init__(self, init, goals, actions):
self.init = self.convert(init)
self.goals = self.convert(goals)
self.actions = actions
def convert(self, clauses):
"""Converts strings into exprs"""
if not isinstance(clauses, Expr):
if len(clauses) > 0:
clauses = expr(clauses)
else:
clauses = []
try:
clauses = conjuncts(clauses)
except AttributeError:
clauses = clauses
new_clauses = []
for clause in clauses:
if clause.op == '~':
new_clauses.append(expr('Not' + str(clause.args[0])))
else:
new_clauses.append(clause)
return new_clauses
def goal_test(self):
"""Checks if the goals have been reached"""
return all(goal in self.init for goal in self.goals)
def act(self, action):
"""
Performs the action given as argument.
Note that action is an Expr like expr('Remove(Glass, Table)') or expr('Eat(Sandwich)')
"""
action_name = action.op
args = action.args
list_action = first(a for a in self.actions if a.name == action_name)
if list_action is None:
raise Exception("Action '{}' not found".format(action_name))
if not list_action.check_precond(self.init, args):
raise Exception("Action '{}' pre-conditions not satisfied".format(action))
self.init = list_action(self.init, args).clauses
class Action:
"""
Defines an action schema using preconditions and effects.
Use this to describe actions in PlanningProblem.
action is an Expr where variables are given as arguments(args).
Precondition and effect are both lists with positive and negative literals.
Negative preconditions and effects are defined by adding a 'Not' before the name of the clause
Example:
precond = [expr("Human(person)"), expr("Hungry(Person)"), expr("NotEaten(food)")]
effect = [expr("Eaten(food)"), expr("Hungry(person)")]
eat = Action(expr("Eat(person, food)"), precond, effect)
"""
def __init__(self, action, precond, effect):
if isinstance(action, str):
action = expr(action)
self.name = action.op
self.args = action.args
self.precond = self.convert(precond)
self.effect = self.convert(effect)
def __call__(self, kb, args):
return self.act(kb, args)
def __repr__(self):
return '{}({})'.format(self.__class__.__name__, Expr(self.name, *self.args))
def convert(self, clauses):
"""Converts strings into Exprs"""
if isinstance(clauses, Expr):
clauses = conjuncts(clauses)
for i in range(len(clauses)):
if clauses[i].op == '~':
clauses[i] = expr('Not' + str(clauses[i].args[0]))
elif isinstance(clauses, str):
clauses = clauses.replace('~', 'Not')
if len(clauses) > 0:
clauses = expr(clauses)
try:
clauses = conjuncts(clauses)
except AttributeError:
pass
return clauses
def substitute(self, e, args):
"""Replaces variables in expression with their respective Propositional symbol"""
new_args = list(e.args)
for num, x in enumerate(e.args):
for i, _ in enumerate(self.args):
if self.args[i] == x:
new_args[num] = args[i]
return Expr(e.op, *new_args)
def check_precond(self, kb, args):
"""Checks if the precondition is satisfied in the current state"""
if isinstance(kb, list):
kb = FolKB(kb)
for clause in self.precond:
if self.substitute(clause, args) not in kb.clauses:
return False
return True
def act(self, kb, args):
"""Executes the action on the state's knowledge base"""
if isinstance(kb, list):
kb = FolKB(kb)
if not self.check_precond(kb, args):
raise Exception('Action pre-conditions not satisfied')
for clause in self.effect:
kb.tell(self.substitute(clause, args))
if clause.op[:3] == 'Not':
new_clause = Expr(clause.op[3:], *clause.args)
if kb.ask(self.substitute(new_clause, args)) is not False:
kb.retract(self.substitute(new_clause, args))
else:
new_clause = Expr('Not' + clause.op, *clause.args)
if kb.ask(self.substitute(new_clause, args)) is not False:
kb.retract(self.substitute(new_clause, args))
return kb
def goal_test(goals, state):
"""Generic goal testing helper function"""
if isinstance(state, list):
kb = FolKB(state)
else:
kb = state
return all(kb.ask(q) is not False for q in goals)
def air_cargo():
"""
[Figure 10.1] AIR-CARGO-PROBLEM
An air-cargo shipment problem for delivering cargo to different locations,
given the starting location and airplanes.
Example:
>>> from planning import *
>>> ac = air_cargo()
>>> ac.goal_test()
False
>>> ac.act(expr('Load(C2, P2, JFK)'))
>>> ac.act(expr('Load(C1, P1, SFO)'))
>>> ac.act(expr('Fly(P1, SFO, JFK)'))
>>> ac.act(expr('Fly(P2, JFK, SFO)'))
>>> ac.act(expr('Unload(C2, P2, SFO)'))
>>> ac.goal_test()
False
>>> ac.act(expr('Unload(C1, P1, JFK)'))
>>> ac.goal_test()
True
>>>
"""
return PlanningProblem(init='At(C1, SFO) & At(C2, JFK) & At(P1, SFO) & At(P2, JFK) & Cargo(C1) & Cargo(C2) & Plane(P1) & Plane(P2) & Airport(SFO) & Airport(JFK)',
goals='At(C1, JFK) & At(C2, SFO)',
actions=[Action('Load(c, p, a)',
precond='At(c, a) & At(p, a) & Cargo(c) & Plane(p) & Airport(a)',
effect='In(c, p) & ~At(c, a)'),
Action('Unload(c, p, a)',
precond='In(c, p) & At(p, a) & Cargo(c) & Plane(p) & Airport(a)',
effect='At(c, a) & ~In(c, p)'),
Action('Fly(p, f, to)',
precond='At(p, f) & Plane(p) & Airport(f) & Airport(to)',
effect='At(p, to) & ~At(p, f)')])
def spare_tire():
"""[Figure 10.2] SPARE-TIRE-PROBLEM
A problem involving changing the flat tire of a car
with a spare tire from the trunk.
Example:
>>> from planning import *
>>> st = spare_tire()
>>> st.goal_test()
False
>>> st.act(expr('Remove(Spare, Trunk)'))
>>> st.act(expr('Remove(Flat, Axle)'))
>>> st.goal_test()
False
>>> st.act(expr('PutOn(Spare, Axle)'))
>>> st.goal_test()
True
>>>
"""
return PlanningProblem(init='Tire(Flat) & Tire(Spare) & At(Flat, Axle) & At(Spare, Trunk)',
goals='At(Spare, Axle) & At(Flat, Ground)',
actions=[Action('Remove(obj, loc)',
precond='At(obj, loc)',
effect='At(obj, Ground) & ~At(obj, loc)'),
Action('PutOn(t, Axle)',
precond='Tire(t) & At(t, Ground) & ~At(Flat, Axle)',
effect='At(t, Axle) & ~At(t, Ground)'),
Action('LeaveOvernight',
precond='',
effect='~At(Spare, Ground) & ~At(Spare, Axle) & ~At(Spare, Trunk) & \
~At(Flat, Ground) & ~At(Flat, Axle) & ~At(Flat, Trunk)')])
def three_block_tower():
"""
[Figure 10.3] THREE-BLOCK-TOWER
A blocks-world problem of stacking three blocks in a certain configuration,
also known as the Sussman Anomaly.
Example:
>>> from planning import *
>>> tbt = three_block_tower()
>>> tbt.goal_test()
False
>>> tbt.act(expr('MoveToTable(C, A)'))
>>> tbt.act(expr('Move(B, Table, C)'))
>>> tbt.goal_test()
False
>>> tbt.act(expr('Move(A, Table, B)'))
>>> tbt.goal_test()
True
>>>
"""
return PlanningProblem(init='On(A, Table) & On(B, Table) & On(C, A) & Block(A) & Block(B) & Block(C) & Clear(B) & Clear(C)',
goals='On(A, B) & On(B, C)',
actions=[Action('Move(b, x, y)',
precond='On(b, x) & Clear(b) & Clear(y) & Block(b) & Block(y)',
effect='On(b, y) & Clear(x) & ~On(b, x) & ~Clear(y)'),
Action('MoveToTable(b, x)',
precond='On(b, x) & Clear(b) & Block(b)',
effect='On(b, Table) & Clear(x) & ~On(b, x)')])
def simple_blocks_world():
"""
SIMPLE-BLOCKS-WORLD
A simplified definition of the Sussman Anomaly problem.
Example:
>>> from planning import *
>>> sbw = simple_blocks_world()
>>> sbw.goal_test()
False
>>> sbw.act(expr('ToTable(A, B)'))
>>> sbw.act(expr('FromTable(B, A)'))
>>> sbw.goal_test()
False
>>> sbw.act(expr('FromTable(C, B)'))
>>> sbw.goal_test()
True
>>>
"""
return PlanningProblem(init='On(A, B) & Clear(A) & OnTable(B) & OnTable(C) & Clear(C)',
goals='On(B, A) & On(C, B)',
actions=[Action('ToTable(x, y)',
precond='On(x, y) & Clear(x)',
effect='~On(x, y) & Clear(y) & OnTable(x)'),
Action('FromTable(y, x)',
precond='OnTable(y) & Clear(y) & Clear(x)',
effect='~OnTable(y) & ~Clear(x) & On(y, x)')])
def have_cake_and_eat_cake_too():
"""
[Figure 10.7] CAKE-PROBLEM
A problem where we begin with a cake and want to
reach the state of having a cake and having eaten a cake.
The possible actions include baking a cake and eating a cake.
Example:
>>> from planning import *
>>> cp = have_cake_and_eat_cake_too()
>>> cp.goal_test()
False
>>> cp.act(expr('Eat(Cake)'))
>>> cp.goal_test()
False
>>> cp.act(expr('Bake(Cake)'))
>>> cp.goal_test()
True
>>>
"""
return PlanningProblem(init='Have(Cake)',
goals='Have(Cake) & Eaten(Cake)',
actions=[Action('Eat(Cake)',
precond='Have(Cake)',
effect='Eaten(Cake) & ~Have(Cake)'),
Action('Bake(Cake)',
precond='~Have(Cake)',
effect='Have(Cake)')])
def shopping_problem():
"""
SHOPPING-PROBLEM
A problem of acquiring some items given their availability at certain stores.
Example:
>>> from planning import *
>>> sp = shopping_problem()
>>> sp.goal_test()
False
>>> sp.act(expr('Go(Home, HW)'))
>>> sp.act(expr('Buy(Drill, HW)'))
>>> sp.act(expr('Go(HW, SM)'))
>>> sp.act(expr('Buy(Banana, SM)'))
>>> sp.goal_test()
False
>>> sp.act(expr('Buy(Milk, SM)'))
>>> sp.goal_test()
True
>>>
"""
return PlanningProblem(init='At(Home) & Sells(SM, Milk) & Sells(SM, Banana) & Sells(HW, Drill)',
goals='Have(Milk) & Have(Banana) & Have(Drill)',
actions=[Action('Buy(x, store)',
precond='At(store) & Sells(store, x)',
effect='Have(x)'),
Action('Go(x, y)',
precond='At(x)',
effect='At(y) & ~At(x)')])
def socks_and_shoes():
"""
SOCKS-AND-SHOES-PROBLEM
A task of wearing socks and shoes on both feet
Example:
>>> from planning import *
>>> ss = socks_and_shoes()
>>> ss.goal_test()
False
>>> ss.act(expr('RightSock'))
>>> ss.act(expr('RightShoe'))
>>> ss.act(expr('LeftSock'))
>>> ss.goal_test()
False
>>> ss.act(expr('LeftShoe'))
>>> ss.goal_test()
True
>>>
"""
return PlanningProblem(init='',
goals='RightShoeOn & LeftShoeOn',
actions=[Action('RightShoe',
precond='RightSockOn',
effect='RightShoeOn'),
Action('RightSock',
precond='',
effect='RightSockOn'),
Action('LeftShoe',
precond='LeftSockOn',
effect='LeftShoeOn'),
Action('LeftSock',
precond='',
effect='LeftSockOn')])
def double_tennis_problem():
"""
[Figure 11.10] DOUBLE-TENNIS-PROBLEM
A multiagent planning problem involving two partner tennis players
trying to return an approaching ball and repositioning around in the court.
Example:
>>> from planning import *
>>> dtp = double_tennis_problem()
>>> goal_test(dtp.goals, dtp.init)
False
>>> dtp.act(expr('Go(A, RightBaseLine, LeftBaseLine)'))
>>> dtp.act(expr('Hit(A, Ball, RightBaseLine)'))
>>> goal_test(dtp.goals, dtp.init)
False
>>> dtp.act(expr('Go(A, LeftNet, RightBaseLine)'))
>>> goal_test(dtp.goals, dtp.init)
True
>>>
"""
return PlanningProblem(init='At(A, LeftBaseLine) & At(B, RightNet) & Approaching(Ball, RightBaseLine) & Partner(A, B) & Partner(B, A)',
goals='Returned(Ball) & At(a, LeftNet) & At(a, RightNet)',
actions=[Action('Hit(actor, Ball, loc)',
precond='Approaching(Ball, loc) & At(actor, loc)',
effect='Returned(Ball)'),
Action('Go(actor, to, loc)',
precond='At(actor, loc)',
effect='At(actor, to) & ~At(actor, loc)')])
class Level:
"""
Contains the state of the planning problem
and exhaustive list of actions which use the
states as pre-condition.
"""
def __init__(self, kb):
"""Initializes variables to hold state and action details of a level"""
self.kb = kb
# current state
self.current_state = kb.clauses
# current action to state link
self.current_action_links = {}
# current state to action link
self.current_state_links = {}
# current action to next state link
self.next_action_links = {}
# next state to current action link
self.next_state_links = {}
# mutually exclusive actions
self.mutex = []
def __call__(self, actions, objects):
self.build(actions, objects)
self.find_mutex()
def separate(self, e):
"""Separates an iterable of elements into positive and negative parts"""
positive = []
negative = []
for clause in e:
if clause.op[:3] == 'Not':
negative.append(clause)
else:
positive.append(clause)
return positive, negative
def find_mutex(self):
"""Finds mutually exclusive actions"""
# Inconsistent effects
pos_nsl, neg_nsl = self.separate(self.next_state_links)
for negeff in neg_nsl:
new_negeff = Expr(negeff.op[3:], *negeff.args)
for poseff in pos_nsl:
if new_negeff == poseff:
for a in self.next_state_links[poseff]:
for b in self.next_state_links[negeff]:
if {a, b} not in self.mutex:
self.mutex.append({a, b})
# Interference will be calculated with the last step
pos_csl, neg_csl = self.separate(self.current_state_links)
# Competing needs
for posprecond in pos_csl:
for negprecond in neg_csl:
new_negprecond = Expr(negprecond.op[3:], *negprecond.args)
if new_negprecond == posprecond:
for a in self.current_state_links[posprecond]:
for b in self.current_state_links[negprecond]:
if {a, b} not in self.mutex:
self.mutex.append({a, b})
# Inconsistent support
state_mutex = []
for pair in self.mutex:
next_state_0 = self.next_action_links[list(pair)[0]]
if len(pair) == 2:
next_state_1 = self.next_action_links[list(pair)[1]]
else:
next_state_1 = self.next_action_links[list(pair)[0]]
if (len(next_state_0) == 1) and (len(next_state_1) == 1):
state_mutex.append({next_state_0[0], next_state_1[0]})
self.mutex = self.mutex + state_mutex
def build(self, actions, objects):
"""Populates the lists and dictionaries containing the state action dependencies"""
for clause in self.current_state:
p_expr = Expr('P' + clause.op, *clause.args)
self.current_action_links[p_expr] = [clause]
self.next_action_links[p_expr] = [clause]
self.current_state_links[clause] = [p_expr]
self.next_state_links[clause] = [p_expr]
for a in actions:
num_args = len(a.args)
possible_args = tuple(itertools.permutations(objects, num_args))
for arg in possible_args:
if a.check_precond(self.kb, arg):
for num, symbol in enumerate(a.args):
if not symbol.op.islower():
arg = list(arg)
arg[num] = symbol
arg = tuple(arg)
new_action = a.substitute(Expr(a.name, *a.args), arg)
self.current_action_links[new_action] = []
for clause in a.precond:
new_clause = a.substitute(clause, arg)
self.current_action_links[new_action].append(new_clause)
if new_clause in self.current_state_links:
self.current_state_links[new_clause].append(new_action)
else:
self.current_state_links[new_clause] = [new_action]
self.next_action_links[new_action] = []
for clause in a.effect:
new_clause = a.substitute(clause, arg)
self.next_action_links[new_action].append(new_clause)
if new_clause in self.next_state_links:
self.next_state_links[new_clause].append(new_action)
else:
self.next_state_links[new_clause] = [new_action]
def perform_actions(self):
"""Performs the necessary actions and returns a new Level"""
new_kb = FolKB(list(set(self.next_state_links.keys())))
return Level(new_kb)
class Graph:
"""
Contains levels of state and actions
Used in graph planning algorithm to extract a solution
"""
def __init__(self, planningproblem):
self.planningproblem = planningproblem
self.kb = FolKB(planningproblem.init)
self.levels = [Level(self.kb)]
self.objects = set(arg for clause in self.kb.clauses for arg in clause.args)
def __call__(self):
self.expand_graph()
def expand_graph(self):
"""Expands the graph by a level"""
last_level = self.levels[-1]
last_level(self.planningproblem.actions, self.objects)
self.levels.append(last_level.perform_actions())
def non_mutex_goals(self, goals, index):
"""Checks whether the goals are mutually exclusive"""
goal_perm = itertools.combinations(goals, 2)
for g in goal_perm:
if set(g) in self.levels[index].mutex:
return False
return True
class GraphPlan:
"""
Class for formulation GraphPlan algorithm
Constructs a graph of state and action space
Returns solution for the planning problem
"""
def __init__(self, planningproblem):
self.graph = Graph(planningproblem)
self.nogoods = []
self.solution = []
def check_leveloff(self):
"""Checks if the graph has levelled off"""
check = (set(self.graph.levels[-1].current_state) == set(self.graph.levels[-2].current_state))
if check:
return True
def extract_solution(self, goals, index):
"""Extracts the solution"""
level = self.graph.levels[index]
if not self.graph.non_mutex_goals(goals, index):
self.nogoods.append((level, goals))
return
level = self.graph.levels[index - 1]
# Create all combinations of actions that satisfy the goal
actions = []
for goal in goals:
actions.append(level.next_state_links[goal])
all_actions = list(itertools.product(*actions))
# Filter out non-mutex actions
non_mutex_actions = []
for action_tuple in all_actions:
action_pairs = itertools.combinations(list(set(action_tuple)), 2)
non_mutex_actions.append(list(set(action_tuple)))
for pair in action_pairs:
if set(pair) in level.mutex:
non_mutex_actions.pop(-1)
break
# Recursion
for action_list in non_mutex_actions:
if [action_list, index] not in self.solution:
self.solution.append([action_list, index])
new_goals = []
for act in set(action_list):
if act in level.current_action_links:
new_goals = new_goals + level.current_action_links[act]
if abs(index) + 1 == len(self.graph.levels):
return
elif (level, new_goals) in self.nogoods:
return
else:
self.extract_solution(new_goals, index - 1)
# Level-Order multiple solutions
solution = []
for item in self.solution:
if item[1] == -1:
solution.append([])
solution[-1].append(item[0])
else:
solution[-1].append(item[0])
for num, item in enumerate(solution):
item.reverse()
solution[num] = item
return solution
def goal_test(self, kb):
return all(kb.ask(q) is not False for q in self.graph.planningproblem.goals)
def execute(self):
"""Executes the GraphPlan algorithm for the given problem"""
while True:
self.graph.expand_graph()
if (self.goal_test(self.graph.levels[-1].kb) and self.graph.non_mutex_goals(self.graph.planningproblem.goals, -1)):
solution = self.extract_solution(self.graph.planningproblem.goals, -1)
if solution:
return solution
if len(self.graph.levels) >= 2 and self.check_leveloff():
return None
class Linearize:
def __init__(self, planningproblem):
self.planningproblem = planningproblem
def filter(self, solution):
"""Filter out persistence actions from a solution"""
new_solution = []
for section in solution[0]:
new_section = []
for operation in section:
if not (operation.op[0] == 'P' and operation.op[1].isupper()):
new_section.append(operation)
new_solution.append(new_section)
return new_solution
def orderlevel(self, level, planningproblem):
"""Return valid linear order of actions for a given level"""
for permutation in itertools.permutations(level):
temp = copy.deepcopy(planningproblem)
count = 0
for action in permutation:
try:
temp.act(action)
count += 1
except:
count = 0
temp = copy.deepcopy(planningproblem)
break
if count == len(permutation):
return list(permutation), temp
return None
def execute(self):
"""Finds total-order solution for a planning graph"""
graphplan_solution = GraphPlan(self.planningproblem).execute()
filtered_solution = self.filter(graphplan_solution)
ordered_solution = []
planningproblem = self.planningproblem
for level in filtered_solution:
level_solution, planningproblem = self.orderlevel(level, planningproblem)
for element in level_solution:
ordered_solution.append(element)
return ordered_solution
def linearize(solution):
"""Converts a level-ordered solution into a linear solution"""
linear_solution = []
for section in solution[0]:
for operation in section:
if not (operation.op[0] == 'P' and operation.op[1].isupper()):
linear_solution.append(operation)
return linear_solution
'''
[Section 10.13] PARTIAL-ORDER-PLANNER
Partially ordered plans are created by a search through the space of plans
rather than a search through the state space. It views planning as a refinement of partially ordered plans.
A partially ordered plan is defined by a set of actions and a set of constraints of the form A < B,
which denotes that action A has to be performed before action B.
To summarize the working of a partial order planner,
1. An open precondition is selected (a sub-goal that we want to achieve).
2. An action that fulfils the open precondition is chosen.
3. Temporal constraints are updated.
4. Existing causal links are protected. Protection is a method that checks if the causal links conflict
and if they do, temporal constraints are added to fix the threats.
5. The set of open preconditions is updated.
6. Temporal constraints of the selected action and the next action are established.
7. A new causal link is added between the selected action and the owner of the open precondition.
8. The set of new causal links is checked for threats and if found, the threat is removed by either promotion or demotion.
If promotion or demotion is unable to solve the problem, the planning problem cannot be solved with the current sequence of actions
or it may not be solvable at all.
9. These steps are repeated until the set of open preconditions is empty.
'''
class PartialOrderPlanner:
def __init__(self, planningproblem):
self.planningproblem = planningproblem
self.initialize()
def initialize(self):
"""Initialize all variables"""
self.causal_links = []
self.start = Action('Start', [], self.planningproblem.init)
self.finish = Action('Finish', self.planningproblem.goals, [])
self.actions = set()
self.actions.add(self.start)
self.actions.add(self.finish)
self.constraints = set()
self.constraints.add((self.start, self.finish))
self.agenda = set()
for precond in self.finish.precond:
self.agenda.add((precond, self.finish))
self.expanded_actions = self.expand_actions()
def expand_actions(self, name=None):
"""Generate all possible actions with variable bindings for precondition selection heuristic"""
objects = set(arg for clause in self.planningproblem.init for arg in clause.args)
expansions = []
action_list = []
if name is not None:
for action in self.planningproblem.actions:
if str(action.name) == name:
action_list.append(action)
else:
action_list = self.planningproblem.actions
for action in action_list:
for permutation in itertools.permutations(objects, len(action.args)):
bindings = unify(Expr(action.name, *action.args), Expr(action.name, *permutation))
if bindings is not None:
new_args = []
for arg in action.args:
if arg in bindings:
new_args.append(bindings[arg])
else:
new_args.append(arg)
new_expr = Expr(str(action.name), *new_args)
new_preconds = []
for precond in action.precond:
new_precond_args = []
for arg in precond.args:
if arg in bindings:
new_precond_args.append(bindings[arg])
else:
new_precond_args.append(arg)
new_precond = Expr(str(precond.op), *new_precond_args)
new_preconds.append(new_precond)
new_effects = []
for effect in action.effect:
new_effect_args = []
for arg in effect.args:
if arg in bindings:
new_effect_args.append(bindings[arg])
else:
new_effect_args.append(arg)
new_effect = Expr(str(effect.op), *new_effect_args)
new_effects.append(new_effect)
expansions.append(Action(new_expr, new_preconds, new_effects))
return expansions
def find_open_precondition(self):
"""Find open precondition with the least number of possible actions"""
number_of_ways = dict()
actions_for_precondition = dict()
for element in self.agenda:
open_precondition = element[0]
possible_actions = list(self.actions) + self.expanded_actions
for action in possible_actions:
for effect in action.effect:
if effect == open_precondition:
if open_precondition in number_of_ways:
number_of_ways[open_precondition] += 1
actions_for_precondition[open_precondition].append(action)
else:
number_of_ways[open_precondition] = 1
actions_for_precondition[open_precondition] = [action]
number = sorted(number_of_ways, key=number_of_ways.__getitem__)
for k, v in number_of_ways.items():
if v == 0:
return None, None, None
act1 = None
for element in self.agenda:
if element[0] == number[0]:
act1 = element[1]
break
if number[0] in self.expanded_actions:
self.expanded_actions.remove(number[0])
return number[0], act1, actions_for_precondition[number[0]]
def find_action_for_precondition(self, oprec):
"""Find action for a given precondition"""
# either
# choose act0 E Actions such that act0 achieves G
for action in self.actions:
for effect in action.effect:
if effect == oprec:
return action, 0
# or
# choose act0 E Actions such that act0 achieves G
for action in self.planningproblem.actions:
for effect in action.effect:
if effect.op == oprec.op:
bindings = unify(effect, oprec)
if bindings is None:
break
return action, bindings
def generate_expr(self, clause, bindings):
"""Generate atomic expression from generic expression given variable bindings"""
new_args = []
for arg in clause.args:
if arg in bindings:
new_args.append(bindings[arg])
else:
new_args.append(arg)
try:
return Expr(str(clause.name), *new_args)
except:
return Expr(str(clause.op), *new_args)
def generate_action_object(self, action, bindings):
"""Generate action object given a generic action andvariable bindings"""
# if bindings is 0, it means the action already exists in self.actions
if bindings == 0:
return action
# bindings cannot be None
else:
new_expr = self.generate_expr(action, bindings)
new_preconds = []
for precond in action.precond:
new_precond = self.generate_expr(precond, bindings)
new_preconds.append(new_precond)
new_effects = []
for effect in action.effect:
new_effect = self.generate_expr(effect, bindings)
new_effects.append(new_effect)
return Action(new_expr, new_preconds, new_effects)
def cyclic(self, graph):
"""Check cyclicity of a directed graph"""
new_graph = dict()
for element in graph:
if element[0] in new_graph:
new_graph[element[0]].append(element[1])
else:
new_graph[element[0]] = [element[1]]
path = set()
def visit(vertex):
path.add(vertex)
for neighbor in new_graph.get(vertex, ()):
if neighbor in path or visit(neighbor):
return True
path.remove(vertex)
return False
value = any(visit(v) for v in new_graph)
return value
def add_const(self, constraint, constraints):
"""Add the constraint to constraints if the resulting graph is acyclic"""
if constraint[0] == self.finish or constraint[1] == self.start:
return constraints
new_constraints = set(constraints)
new_constraints.add(constraint)
if self.cyclic(new_constraints):
return constraints
return new_constraints
def is_a_threat(self, precondition, effect):
"""Check if effect is a threat to precondition"""
if (str(effect.op) == 'Not' + str(precondition.op)) or ('Not' + str(effect.op) == str(precondition.op)):
if effect.args == precondition.args:
return True
return False
def protect(self, causal_link, action, constraints):
"""Check and resolve threats by promotion or demotion"""
threat = False
for effect in action.effect:
if self.is_a_threat(causal_link[1], effect):
threat = True
break
if action != causal_link[0] and action != causal_link[2] and threat:
# try promotion
new_constraints = set(constraints)
new_constraints.add((action, causal_link[0]))
if not self.cyclic(new_constraints):
constraints = self.add_const((action, causal_link[0]), constraints)
else:
# try demotion
new_constraints = set(constraints)