''' Base class for planning graph nodes.

includes instance sets common to both types of nodes used in a planning graph

parents: the set of nodes in the previous level

children: the set of nodes in the subsequent level

mutex: the set of sibling nodes that are mutually exclusive with this node

'''

def __init__(self):

self.parents = set()

self.children = set()

self.mutex = set()

def is_mutex(self, other) -> bool:

''' Boolean test for mutual exclusion

:param other: PgNode

the other node to compare with

:return: bool

True if this node and the other are marked mutually exclusive (mutex)

'''

if other in self.mutex:

return True

return False

def show(self):

''' helper print for debugging shows counts of parents, children, siblings

:return:

print only

'''

print("{} parents".format(len(self.parents)))

print("{} children".format(len(self.children)))

'''

A planning graph node representing a state (literal fluent) from a planning

problem.

Args:

----------

symbol : str

A string representing a literal expression from a planning problem

domain.

is_pos : bool

Boolean flag indicating whether the literal expression is positive or

negative.

'''

def __init__(self, symbol: str, is_pos: bool):

''' S-level Planning Graph node constructor

:param symbol: expr

:param is_pos: bool

Instance variables calculated:

literal: expr

fluent in its literal form including negative operator if applicable

Instance variables inherited from PgNode:

parents: set of nodes connected to this node in previous A level; initially empty

children: set of nodes connected to this node in next A level; initially empty

mutex: set of sibling S-nodes that this node has mutual exclusion with; initially empty

'''

PgNode.__init__(self)

self.symbol = symbol

self.is_pos = is_pos

self.literal = expr(self.symbol)

if not self.is_pos:

self.literal = expr('~{}'.format(self.symbol))

def show(self):

'''helper print for debugging shows literal plus counts of parents, children, siblings

:return:

print only

'''

print("\n*** {}".format(self.literal))

PgNode.show(self)

def __eq__(self, other):

'''equality test for nodes - compares only the literal for equality

:param other: PgNode_s

:return: bool

'''

if isinstance(other, self.__class__):

return (self.symbol == other.symbol) \

and (self.is_pos == other.is_pos)

def __hash__(self):

'''A-type (action) Planning Graph node - inherited from PgNode

'''

def __init__(self, action: Action):

'''A-level Planning Graph node constructor

:param action: Action

a ground action, i.e. this action cannot contain any variables

Instance variables calculated:

An A-level will always have an S-level as its parent and an S-level as its child.

The preconditions and effects will become the parents and children of the A-level node

However, when this node is created, it is not yet connected to the graph

prenodes: set of *possible* parent S-nodes

effnodes: set of *possible* child S-nodes

is_persistent: bool True if this is a persistence action, i.e. a no-op action

Instance variables inherited from PgNode:

parents: set of nodes connected to this node in previous S level; initially empty

children: set of nodes connected to this node in next S level; initially empty

mutex: set of sibling A-nodes that this node has mutual exclusion with; initially empty

'''

PgNode.__init__(self)

self.action = action

self.prenodes = self.precond_s_nodes()

self.effnodes = self.effect_s_nodes()

self.is_persistent = False

if self.prenodes == self.effnodes:

self.is_persistent = True

def show(self):

'''helper print for debugging shows action plus counts of parents, children, siblings

:return:

print only

'''

print("\n*** {}{}".format(self.action.name, self.action.args))

PgNode.show(self)

def precond_s_nodes(self):

'''precondition literals as S-nodes (represents possible parents for this node).

It is computationally expensive to call this function; it is only called by the

class constructor to populate the `prenodes` attribute.

:return: set of PgNode_s

'''

nodes = set()

for p in self.action.precond_pos:

n = PgNode_s(p, True)

nodes.add(n)

for p in self.action.precond_neg:

n = PgNode_s(p, False)

nodes.add(n)

return nodes

def effect_s_nodes(self):

'''effect literals as S-nodes (represents possible children for this node).

It is computationally expensive to call this function; it is only called by the

class constructor to populate the `effnodes` attribute.

:return: set of PgNode_s

'''

nodes = set()

for e in self.action.effect_add:

n = PgNode_s(e, True)

nodes.add(n)

for e in self.action.effect_rem:

n = PgNode_s(e, False)

nodes.add(n)

return nodes

def __eq__(self, other):

'''equality test for nodes - compares only the action name for equality

:param other: PgNode_a

:return: bool

'''

if isinstance(other, self.__class__):

return (self.action.name == other.action.name) \

and (self.action.args == other.action.args)

def __hash__(self):

''' adds sibling nodes to each other's mutual exclusion (mutex) set. These should be sibling nodes!

:param node1: PgNode (or inherited PgNode_a, PgNode_s types)

:param node2: PgNode (or inherited PgNode_a, PgNode_s types)

:return:

node mutex sets modified

'''

if type(node1) != type(node2):

raise TypeError('Attempted to mutex two nodes of different types')

node1.mutex.add(node2)

'''

A planning graph as described in chapter 10 of the AIMA text. The planning

graph can be used to reason about

'''

def __init__(self, problem: Problem, state: str, serial_planning=True):

'''

:param problem: PlanningProblem (or subclass such as AirCargoProblem or HaveCakeProblem)

:param state: str (will be in form TFTTFF... representing fluent states)

:param serial_planning: bool (whether or not to assume that only one action can occur at a time)

Instance variable calculated:

fs: FluentState

the state represented as positive and negative fluent literal lists

all_actions: list of the PlanningProblem valid ground actions combined with calculated no-op actions

s_levels: list of sets of PgNode_s, where each set in the list represents an S-level in the planning graph

a_levels: list of sets of PgNode_a, where each set in the list represents an A-level in the planning graph

'''

self.problem = problem

self.fs = decode_state(state, problem.state_map)

self.serial = serial_planning

self.all_actions = self.problem.actions_list + self.noop_actions(self.problem.state_map)

self.s_levels = []

self.a_levels = []

self.create_graph()

def noop_actions(self, literal_list):

'''create persistent action for each possible fluent

"No-Op" actions are virtual actions (i.e., actions that only exist in

the planning graph, not in the planning problem domain) that operate

on each fluent (literal expression) from the problem domain. No op

actions "pass through" the literal expressions from one level of the

planning graph to the next.

The no-op action list requires both a positive and a negative action

for each literal expression. Positive no-op actions require the literal

as a positive precondition and add the literal expression as an effect

in the output, and negative no-op actions require the literal as a

negative precondition and remove the literal expression as an effect in

the output.

This function should only be called by the class constructor.

:param literal_list:

:return: list of Action

'''

action_list = []

for fluent in literal_list:

act1 = Action(expr("Noop_pos({})".format(fluent)), ([fluent], []), ([fluent], []))

action_list.append(act1)

act2 = Action(expr("Noop_neg({})".format(fluent)), ([], [fluent]), ([], [fluent]))

action_list.append(act2)

return action_list

def create_graph(self):

''' build a Planning Graph as described in Russell-Norvig 3rd Ed 10.3 or 2nd Ed 11.4

The S0 initial level has been implemented for you. It has no parents and includes all of

the literal fluents that are part of the initial state passed to the constructor. At the start

of a problem planning search, this will be the same as the initial state of the problem. However,

the planning graph can be built from any state in the Planning Problem

This function should only be called by the class constructor.

:return:

builds the graph by filling s_levels[] and a_levels[] lists with node sets for each level

'''

# the graph should only be built during class construction

if (len(self.s_levels) != 0) or (len(self.a_levels) != 0):

raise Exception(

'Planning Graph already created; construct a new planning graph for each new state in the planning sequence')

# initialize S0 to literals in initial state provided.

leveled = False

level = 0

self.s_levels.append(set()) # S0 set of s_nodes - empty to start

# for each fluent in the initial state, add the correct literal PgNode_s

for literal in self.fs.pos:

self.s_levels[level].add(PgNode_s(literal, True))

for literal in self.fs.neg:

self.s_levels[level].add(PgNode_s(literal, False))

# no mutexes at the first level

# continue to build the graph alternating A, S levels until last two S levels contain the same literals,

# i.e. until it is "leveled"

while not leveled:

self.add_action_level(level)

self.update_a_mutex(self.a_levels[level])

level += 1

self.add_literal_level(level)

self.update_s_mutex(self.s_levels[level])

if self.s_levels[level] == self.s_levels[level - 1]:

leveled = True

def add_action_level(self, level):

''' add an A (action) level to the Planning Graph

:param level: int

the level number alternates S0, A0, S1, A1, S2, .... etc the level number is also used as the

index for the node set lists self.a_levels[] and self.s_levels[]

:return:

adds A nodes to the current level in self.a_levels[level]

'''

# TODO add action A level to the planning graph as described in the Russell-Norvig text

# 1. determine what actions to add and create those PgNode_a objects

# 2. connect the nodes to the previous S literal level

# for example, the A0 level will iterate through all possible actions for the problem and add a PgNode_a to a_levels[0]

# set iff all prerequisite literals for the action hold in S0. This can be accomplished by testing

# to see if a proposed PgNode_a has prenodes that are a subset of the previous S level. Once an

# action node is added, it MUST be connected to the S node instances in the appropriate s_level set.

self.a_levels.append(set())

"""Loop all actions,

create action nodes,

see if actions nodes prenodes are in previous state nodes.

If so, make the parent child connections."""

for a in self.all_actions:

for node_s in self.s_levels[level]:

node_a = PgNode_a(a)

if node_s in node_a.prenodes:

self.a_levels[level].add(node_a)

node_a.parents.add(node_s)

node_s.children.add(node_a)

def add_literal_level(self, level):

''' add an S (literal) level to the Planning Graph

:param level: int

the level number alternates S0, A0, S1, A1, S2, .... etc the level number is also used as the

index for the node set lists self.a_levels[] and self.s_levels[]

:return:

adds S nodes to the current level in self.s_levels[level]

'''

# TODO add literal S level to the planning graph as described in the Russell-Norvig text

# 1. determine what literals to add

# 2. connect the nodes

# for example, every A node in the previous level has a list of S nodes in effnodes that represent the effect

# produced by the action. These literals will all be part of the new S level. Since we are working with sets, they

# may be "added" to the set without fear of duplication. However, it is important to then correctly create and connect

# all of the new S nodes as children of all the A nodes that could produce them, and likewise add the A nodes to the

# parent sets of the S nodes

self.s_levels.append(set())

"""Loop all previous action nodes

if state nodes are result of effects of action

add state node and make connections"""

for node_a in self.a_levels[level-1]:

for node_s in node_a.effnodes:

self.s_levels[level].add(node_s)

node_s.parents.add(node_a)

node_a.children.add(node_s)

def update_a_mutex(self, nodeset):

''' Determine and update sibling mutual exclusion for A-level nodes

Mutex action tests section from 3rd Ed. 10.3 or 2nd Ed. 11.4

A mutex relation holds between two actions a given level

if the planning graph is a serial planning graph and the pair are nonpersistence actions

or if any of the three conditions hold between the pair:

Inconsistent Effects

Interference

Competing needs

:param nodeset: set of PgNode_a (siblings in the same level)

:return:

mutex set in each PgNode_a in the set is appropriately updated

'''

nodelist = list(nodeset)

for i, n1 in enumerate(nodelist[:-1]):

for n2 in nodelist[i + 1:]:

if (self.serialize_actions(n1, n2) or

self.inconsistent_effects_mutex(n1, n2) or

self.interference_mutex(n1, n2) or

self.competing_needs_mutex(n1, n2)):

mutexify(n1, n2)

def serialize_actions(self, node_a1: PgNode_a, node_a2: PgNode_a) -> bool:

'''

Test a pair of actions for mutual exclusion, returning True if the

planning graph is serial, and if either action is persistent; otherwise

return False. Two serial actions are mutually exclusive if they are

both non-persistent.

:param node_a1: PgNode_a

:param node_a2: PgNode_a

:return: bool

'''

#

if not self.serial:

return False

if node_a1.is_persistent or node_a2.is_persistent:

return False

return True

def inconsistent_effects_mutex(self, node_a1: PgNode_a, node_a2: PgNode_a) -> bool:

'''

Test a pair of actions for inconsistent effects, returning True if

one action negates an effect of the other, and False otherwise.

HINT: The Action instance associated with an action node is accessible

through the PgNode_a.action attribute. See the Action class

documentation for details on accessing the effects and preconditions of

an action.

:param node_a1: PgNode_a

:param node_a2: PgNode_a

:return: bool

'''

# TODO test for Inconsistent Effects between nodes

"""loop all node1 actions and node2 actions

if they add and remove each others' effects

return true"""

for a1 in node_a1.action.effect_add:

for a2 in node_a2.action.effect_rem:

if (a1 == a2):

return True

for a2 in node_a2.action.effect_add:

for a1 in node_a1.action.effect_rem:

if (a1 == a2):

return True

return False

def interference_mutex(self, node_a1: PgNode_a, node_a2: PgNode_a) -> bool:

'''

Test a pair of actions for mutual exclusion, returning True if the

effect of one action is the negation of a precondition of the other.

HINT: The Action instance associated with an action node is accessible

through the PgNode_a.action attribute. See the Action class

documentation for details on accessing the effects and preconditions of

an action.

:param node_a1: PgNode_a

:param node_a2: PgNode_a

:return: bool

'''

# TODO test for Interference between nodes

"""loop all node1 actions and node2 actions

if there's interference

return true"""

for a1 in node_a1.action.effect_add:

for a2 in node_a2.action.precond_neg:

if (a1==a2): return True

for a2 in node_a2.action.effect_add:

for a1 in node_a1.action.precond_neg:

if (a1==a2): return True

for a1 in node_a1.action.effect_rem:

for a2 in node_a2.action.precond_pos:

if (a1==a2): return True

for a2 in node_a2.action.effect_rem:

for a1 in node_a1.action.precond_pos:

if (a1==a2): return True

return False

def competing_needs_mutex(self, node_a1: PgNode_a, node_a2: PgNode_a) -> bool:

'''

Test a pair of actions for mutual exclusion, returning True if one of

the precondition of one action is mutex with a precondition of the

other action.

:param node_a1: PgNode_a

:param node_a2: PgNode_a

:return: bool

'''

# TODO test for Competing Needs between nodes

"""loop parents' mutexes

compare parents' mutexness

return true if so"""

p1 = next(iter(node_a1.parents))

p2 = next(iter(node_a2.parents))

for mutex in p2.mutex :

if (p1.__eq__(mutex)): return True

for mutex in p1.mutex:

if (p2.__eq__(mutex)): return True

return False

def update_s_mutex(self, nodeset: set):

''' Determine and update sibling mutual exclusion for S-level nodes

Mutex action tests section from 3rd Ed. 10.3 or 2nd Ed. 11.4

A mutex relation holds between literals at a given level

if either of the two conditions hold between the pair:

Negation

Inconsistent support

:param nodeset: set of PgNode_a (siblings in the same level)

:return:

mutex set in each PgNode_a in the set is appropriately updated

'''

nodelist = list(nodeset)

for i, n1 in enumerate(nodelist[:-1]):

for n2 in nodelist[i + 1:]:

if self.negation_mutex(n1, n2) or self.inconsistent_support_mutex(n1, n2):

mutexify(n1, n2)

def negation_mutex(self, node_s1: PgNode_s, node_s2: PgNode_s) -> bool:

'''

Test a pair of state literals for mutual exclusion, returning True if

one node is the negation of the other, and False otherwise.

HINT: Look at the PgNode_s.__eq__ defines the notion of equivalence for

literal expression nodes, and the class tracks whether the literal is

positive or negative.

:param node_s1: PgNode_s

:param node_s2: PgNode_s

:return: bool

'''

# TODO test for negation between nodes

if ((node_s1.symbol == node_s2.symbol) and (node_s1.is_pos!= node_s2.is_pos)):

return True

return False

def inconsistent_support_mutex(self, node_s1: PgNode_s, node_s2: PgNode_s):

'''

Test a pair of state literals for mutual exclusion, returning True if

there are no actions that could achieve the two literals at the same

time, and False otherwise. In other words, the two literal nodes are

mutex if all of the actions that could achieve the first literal node

are pairwise mutually exclusive with all of the actions that could

achieve the second literal node.

HINT: The PgNode.is_mutex method can be used to test whether two nodes

are mutually exclusive.

:param node_s1: PgNode_s

:param node_s2: PgNode_s

:return: bool

'''

# TODO test for Inconsistent Support between nodes

"""loop parents and compare mutexness

return accordingly"""

if node_s1.parents.intersection(node_s2.parents):

return False

for p1 in node_s1.parents:

for p2 in node_s2.parents:

if p1.__eq__(p2): return False

for mutex in p2.mutex :

if (p1.__eq__(mutex)): return True

for mutex in p1.mutex:

if (p2.__eq__(mutex)): return True # If no mutex conflicts found, return False

return False

def h_levelsum(self) -> int:

'''The sum of the level costs of the individual goals (admissible if goals independent)

:return: int

'''

level_sum = 0

# TODO implement

# for each goal in the problem, determine the level cost, then add them together

"""loop goals

loop levels

add the cost"""

for subgoal in self.problem.goal:

for level in range(len(self.s_levels)):

if any([subgoal == state_node.literal for state_node in self.s_levels[level]]):

level_sum+=level

break # for loop