aima-python/planning.py at sofmonk-test_utils · sofmonk/aima-python · GitHub

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"""Planning (Chapters 10-11)

"""

import itertools

from utils import Expr, expr, first

from logic import FolKB

class PDLL:

"""

PDLL 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, initial_state, actions, goal_test):

self.kb = FolKB(initial_state)

self.actions = actions

self.goal_test_func = goal_test

def goal_test(self):

return self.goal_test_func(self.kb)

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.kb, args):

raise Exception("Action '{}' pre-conditions not satisfied".format(action))

list_action(self.kb, args)

class Action:

"""

Defines an action schema using preconditions and effects.

Use this to describe actions in PDDL.

action is an Expr where variables are given as arguments(args).

Precondition and effect are both lists with positive and negated literals.

Example:

precond_pos = [expr("Human(person)"), expr("Hungry(Person)")]

precond_neg = [expr("Eaten(food)")]

effect_add = [expr("Eaten(food)")]

effect_rem = [expr("Hungry(person)")]

eat = Action(expr("Eat(person, food)"), [precond_pos, precond_neg], [effect_add, effect_rem])

"""

def __init__(self, action, precond, effect):

self.name = action.op

self.args = action.args

self.precond_pos = precond[0]

self.precond_neg = precond[1]

self.effect_add = effect[0]

self.effect_rem = effect[1]

def __call__(self, kb, args):

return self.act(kb, args)

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 range(len(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"""

# check for positive clauses

for clause in self.precond_pos:

if self.substitute(clause, args) not in kb.clauses:

return False

# check for negative clauses

for clause in self.precond_neg:

if self.substitute(clause, args) in kb.clauses:

return False

return True

def act(self, kb, args):

"""Executes the action on the state's kb"""

# check if the preconditions are satisfied

if not self.check_precond(kb, args):

raise Exception("Action pre-conditions not satisfied")

# remove negative literals

for clause in self.effect_rem:

kb.retract(self.substitute(clause, args))

# add positive literals

for clause in self.effect_add:

kb.tell(self.substitute(clause, args))

def air_cargo():

init = [expr('At(C1, SFO)'),

expr('At(C2, JFK)'),

expr('At(P1, SFO)'),

expr('At(P2, JFK)'),

expr('Cargo(C1)'),

expr('Cargo(C2)'),

expr('Plane(P1)'),

expr('Plane(P2)'),

expr('Airport(JFK)'),

expr('Airport(SFO)')]

def goal_test(kb):

required = [expr('At(C1 , JFK)'), expr('At(C2 ,SFO)')]

for q in required:

if kb.ask(q) is False:

return False

return True

# Actions

# Load

precond_pos = [expr("At(c, a)"), expr("At(p, a)"), expr("Cargo(c)"), expr("Plane(p)"),

expr("Airport(a)")]

precond_neg = []

effect_add = [expr("In(c, p)")]

effect_rem = [expr("At(c, a)")]

load = Action(expr("Load(c, p, a)"), [precond_pos, precond_neg], [effect_add, effect_rem])

# Unload

precond_pos = [expr("In(c, p)"), expr("At(p, a)"), expr("Cargo(c)"), expr("Plane(p)"),

expr("Airport(a)")]

precond_neg = []

effect_add = [expr("At(c, a)")]

effect_rem = [expr("In(c, p)")]

unload = Action(expr("Unload(c, p, a)"), [precond_pos, precond_neg], [effect_add, effect_rem])

# Fly

# Used 'f' instead of 'from' because 'from' is a python keyword and expr uses eval() function

precond_pos = [expr("At(p, f)"), expr("Plane(p)"), expr("Airport(f)"), expr("Airport(to)")]

precond_neg = []

effect_add = [expr("At(p, to)")]

effect_rem = [expr("At(p, f)")]

fly = Action(expr("Fly(p, f, to)"), [precond_pos, precond_neg], [effect_add, effect_rem])

return PDLL(init, [load, unload, fly], goal_test)

def spare_tire():

init = [expr('Tire(Flat)'),

expr('Tire(Spare)'),

expr('At(Flat, Axle)'),

expr('At(Spare, Trunk)')]

def goal_test(kb):

required = [expr('At(Spare, Axle)'), expr('At(Flat, Ground)')]

for q in required:

if kb.ask(q) is False:

return False

return True

# Actions

# Remove

precond_pos = [expr("At(obj, loc)")]

precond_neg = []

effect_add = [expr("At(obj, Ground)")]

effect_rem = [expr("At(obj, loc)")]

remove = Action(expr("Remove(obj, loc)"), [precond_pos, precond_neg], [effect_add, effect_rem])

# PutOn

precond_pos = [expr("Tire(t)"), expr("At(t, Ground)")]

precond_neg = [expr("At(Flat, Axle)")]

effect_add = [expr("At(t, Axle)")]

effect_rem = [expr("At(t, Ground)")]

put_on = Action(expr("PutOn(t, Axle)"), [precond_pos, precond_neg], [effect_add, effect_rem])

# LeaveOvernight

precond_pos = []

precond_neg = []

effect_add = []

effect_rem = [expr("At(Spare, Ground)"), expr("At(Spare, Axle)"), expr("At(Spare, Trunk)"),

expr("At(Flat, Ground)"), expr("At(Flat, Axle)"), expr("At(Flat, Trunk)")]

leave_overnight = Action(expr("LeaveOvernight"), [precond_pos, precond_neg],

[effect_add, effect_rem])

return PDLL(init, [remove, put_on, leave_overnight], goal_test)

def three_block_tower():

init = [expr('On(A, Table)'),

expr('On(B, Table)'),

expr('On(C, A)'),

expr('Block(A)'),

expr('Block(B)'),

expr('Block(C)'),

expr('Clear(B)'),

expr('Clear(C)')]

def goal_test(kb):

required = [expr('On(A, B)'), expr('On(B, C)')]

for q in required:

if kb.ask(q) is False:

return False

return True

# Actions

# Move

precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Clear(y)'), expr('Block(b)'),

expr('Block(y)')]

precond_neg = []

effect_add = [expr('On(b, y)'), expr('Clear(x)')]

effect_rem = [expr('On(b, x)'), expr('Clear(y)')]

move = Action(expr('Move(b, x, y)'), [precond_pos, precond_neg], [effect_add, effect_rem])

# MoveToTable

precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Block(b)')]

precond_neg = []

effect_add = [expr('On(b, Table)'), expr('Clear(x)')]

effect_rem = [expr('On(b, x)')]

moveToTable = Action(expr('MoveToTable(b, x)'), [precond_pos, precond_neg],

[effect_add, effect_rem])

return PDLL(init, [move, moveToTable], goal_test)

def have_cake_and_eat_cake_too():

init = [expr('Have(Cake)')]

def goal_test(kb):

required = [expr('Have(Cake)'), expr('Eaten(Cake)')]

for q in required:

if kb.ask(q) is False:

return False

return True

# Actions

# Eat cake

precond_pos = [expr('Have(Cake)')]

precond_neg = []

effect_add = [expr('Eaten(Cake)')]

effect_rem = [expr('Have(Cake)')]

eat_cake = Action(expr('Eat(Cake)'), [precond_pos, precond_neg], [effect_add, effect_rem])

# Bake Cake

precond_pos = []

precond_neg = [expr('Have(Cake)')]

effect_add = [expr('Have(Cake)')]

effect_rem = []

bake_cake = Action(expr('Bake(Cake)'), [precond_pos, precond_neg], [effect_add, effect_rem])

return PDLL(init, [eat_cake, bake_cake], goal_test)

class Level():

"""

Contains the state of the planning problem

and exhaustive list of actions which use the

states as pre-condition.

"""

def __init__(self, poskb, negkb):

self.poskb = poskb

# Current state

self.current_state_pos = poskb.clauses

self.current_state_neg = negkb.clauses

# Current action to current state link

self.current_action_links_pos = {}

self.current_action_links_neg = {}

# Current state to action link

self.current_state_links_pos = {}

self.current_state_links_neg = {}

# Current action to next state link

self.next_action_links = {}

# Next state to current action link

self.next_state_links_pos = {}

self.next_state_links_neg = {}

self.mutex = []

def __call__(self, actions, objects):

self.build(actions, objects)

self.find_mutex()

def find_mutex(self):

# Inconsistent effects

for poseff in self.next_state_links_pos:

negeff = poseff

if negeff in self.next_state_links_neg:

for a in self.next_state_links_pos[poseff]:

for b in self.next_state_links_neg[negeff]:

if set([a, b]) not in self.mutex:

self.mutex.append(set([a, b]))

# Interference

for posprecond in self.current_state_links_pos:

negeff = posprecond

if negeff in self.next_state_links_neg:

for a in self.current_state_links_pos[posprecond]:

for b in self.next_state_links_neg[negeff]:

if set([a, b]) not in self.mutex:

self.mutex.append(set([a, b]))

for negprecond in self.current_state_links_neg:

poseff = negprecond

if poseff in self.next_state_links_pos:

for a in self.next_state_links_pos[poseff]:

for b in self.current_state_links_neg[negprecond]:

if set([a, b]) not in self.mutex:

self.mutex.append(set([a, b]))

# Competing needs

for posprecond in self.current_state_links_pos:

negprecond = posprecond

if negprecond in self.current_state_links_neg:

for a in self.current_state_links_pos[posprecond]:

for b in self.current_state_links_neg[negprecond]:

if set([a, b]) not in self.mutex:

self.mutex.append(set([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(set([next_state_0[0], next_state_1[0]]))

self.mutex = self.mutex+state_mutex

def build(self, actions, objects):

# Add persistence actions for positive states

for clause in self.current_state_pos:

self.current_action_links_pos[Expr('Persistence', clause)] = [clause]

self.next_action_links[Expr('Persistence', clause)] = [clause]

self.current_state_links_pos[clause] = [Expr('Persistence', clause)]

self.next_state_links_pos[clause] = [Expr('Persistence', clause)]

# Add persistence actions for negative states

for clause in self.current_state_neg:

not_expr = Expr('not'+clause.op, clause.args)

self.current_action_links_neg[Expr('Persistence', not_expr)] = [clause]

self.next_action_links[Expr('Persistence', not_expr)] = [clause]

self.current_state_links_neg[clause] = [Expr('Persistence', not_expr)]

self.next_state_links_neg[clause] = [Expr('Persistence', not_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.poskb, 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_pos[new_action] = []

self.current_action_links_neg[new_action] = []

for clause in a.precond_pos:

new_clause = a.substitute(clause, arg)

self.current_action_links_pos[new_action].append(new_clause)

if new_clause in self.current_state_links_pos:

self.current_state_links_pos[new_clause].append(new_action)

else:

self.current_state_links_pos[new_clause] = [new_action]

for clause in a.precond_neg:

new_clause = a.substitute(clause, arg)

self.current_action_links_neg[new_action].append(new_clause)

if new_clause in self.current_state_links_neg:

self.current_state_links_neg[new_clause].append(new_action)

else:

self.current_state_links_neg[new_clause] = [new_action]

self.next_action_links[new_action] = []

for clause in a.effect_add:

new_clause = a.substitute(clause, arg)

self.next_action_links[new_action].append(new_clause)

if new_clause in self.next_state_links_pos:

self.next_state_links_pos[new_clause].append(new_action)

else:

self.next_state_links_pos[new_clause] = [new_action]

for clause in a.effect_rem:

new_clause = a.substitute(clause, arg)

self.next_action_links[new_action].append(new_clause)

if new_clause in self.next_state_links_neg:

self.next_state_links_neg[new_clause].append(new_action)

else:

self.next_state_links_neg[new_clause] = [new_action]

def perform_actions(self):

new_kb_pos = FolKB(list(set(self.next_state_links_pos.keys())))

new_kb_neg = FolKB(list(set(self.next_state_links_neg.keys())))

return Level(new_kb_pos, new_kb_neg)

class Graph:

"""

Contains levels of state and actions

Used in graph planning algorithm to extract a solution

"""

def __init__(self, pdll, negkb):

self.pdll = pdll

self.levels = [Level(pdll.kb, negkb)]

self.objects = set(arg for clause in pdll.kb.clauses + negkb.clauses for arg in clause.args)

def __call__(self):

self.expand_graph()

def expand_graph(self):

last_level = self.levels[-1]

last_level(self.pdll.actions, self.objects)

self.levels.append(last_level.perform_actions())

def non_mutex_goals(self, goals, index):

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, pdll, negkb):

self.graph = Graph(pdll, negkb)

self.nogoods = []

self.solution = []

def check_leveloff(self):

first_check = (set(self.graph.levels[-1].current_state_pos) ==

set(self.graph.levels[-2].current_state_pos))

second_check = (set(self.graph.levels[-1].current_state_neg) ==

set(self.graph.levels[-2].current_state_neg))

if first_check and second_check:

return True

def extract_solution(self, goals_pos, goals_neg, index):

level = self.graph.levels[index]

if not self.graph.non_mutex_goals(goals_pos+goals_neg, index):

self.nogoods.append((level, goals_pos, goals_neg))

return

level = self.graph.levels[index-1]

# Create all combinations of actions that satisfy the goal

actions = []

for goal in goals_pos:

actions.append(level.next_state_links_pos[goal])

for goal in goals_neg:

actions.append(level.next_state_links_neg[goal])

all_actions = list(itertools.product(*actions))

# Filter out the action combinations which contain mutexes

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_pos = []

new_goals_neg = []

for act in set(action_list):

if act in level.current_action_links_pos:

new_goals_pos = new_goals_pos + level.current_action_links_pos[act]

for act in set(action_list):

if act in level.current_action_links_neg:

new_goals_neg = new_goals_neg + level.current_action_links_neg[act]

if abs(index)+1 == len(self.graph.levels):

return

elif (level, new_goals_pos, new_goals_neg) in self.nogoods:

return

else:

self.extract_solution(new_goals_pos, new_goals_neg, 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(kb, goals):

for q in goals:

if kb.ask(q) is False:

return False

return True

def spare_tire_graphplan():

pdll = spare_tire()

negkb = FolKB([expr('At(Flat, Trunk)')])

graphplan = GraphPlan(pdll, negkb)

# Not sure

goals_pos = [expr('At(Spare, Axle)'), expr('At(Flat, Ground)')]

goals_neg = []

while True:

if (goal_test(graphplan.graph.levels[-1].poskb, goals_pos) and

graphplan.graph.non_mutex_goals(goals_pos+goals_neg, -1)):

solution = graphplan.extract_solution(goals_pos, goals_neg, -1)

if solution:

return solution

graphplan.graph.expand_graph()

if len(graphplan.graph.levels)>=2 and graphplan.check_leveloff():

return None

def double_tennis_problem():

init = [expr('At(A, LeftBaseLine)'),

expr('At(B, RightNet)'),

expr('Approaching(Ball, RightBaseLine)'),

expr('Partner(A, B)'),

expr('Partner(B, A)')]

def goal_test(kb):

required = [expr('Goal(Returned(Ball))'), expr('At(a, RightNet)'), expr('At(a, LeftNet)')]

for q in required:

if kb.ask(q) is False:

return False

return True

# Actions

# Hit

precond_pos = [expr("Approaching(Ball,loc)"), expr("At(actor,loc)")]

precond_neg = []

effect_add = [expr("Returned(Ball)")]

effect_rem = []

hit = Action(expr("Hit(actor, Ball)"), [precond_pos, precond_neg], [effect_add, effect_rem])

# Go

precond_pos = [expr("At(actor, loc)")]

precond_neg = []

effect_add = [expr("At(actor, to)")]

effect_rem = [expr("At(actor, loc)")]

go = Action(expr("Go(actor, to)"), [precond_pos, precond_neg], [effect_add, effect_rem])

return PDLL(init, [hit, go], goal_test)

aima-python/planning.py at sofmonk-test_utils · sofmonk/aima-python · GitHub

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