diff --git a/planning.py b/planning.py
index 30b8a79f6..edfb39f19 100644
--- a/planning.py
+++ b/planning.py
@@ -2,7 +2,8 @@
"""
import itertools
-from utils import Expr, expr, first
+from search import Node
+from utils import Expr, expr, first, FIFOQueue
from logic import FolKB
@@ -574,3 +575,312 @@ def goal_test(kb):
go = Action(expr("Go(actor, to)"), [precond_pos, precond_neg], [effect_add, effect_rem])
return PDDL(init, [hit, go], goal_test)
+
+
+class HLA(Action):
+ """
+ Define Actions for the real-world (that may be refined further), and satisfy resource
+ constraints.
+ """
+ unique_group = 1
+
+ def __init__(self, action, precond=[None, None], effect=[None, None], duration=0,
+ consume={}, use={}):
+ """
+ As opposed to actions, to define HLA, we have added constraints.
+ duration holds the amount of time required to execute the task
+ consumes holds a dictionary representing the resources the task consumes
+ uses holds a dictionary representing the resources the task uses
+ """
+ super().__init__(action, precond, effect)
+ self.duration = duration
+ self.consumes = consume
+ self.uses = use
+ self.completed = False
+ # self.priority = -1 # must be assigned in relation to other HLAs
+ # self.job_group = -1 # must be assigned in relation to other HLAs
+
+ def do_action(self, job_order, available_resources, kb, args):
+ """
+ An HLA based version of act - along with knowledge base updation, it handles
+ resource checks, and ensures the actions are executed in the correct order.
+ """
+ # print(self.name)
+ if not self.has_usable_resource(available_resources):
+ raise Exception('Not enough usable resources to execute {}'.format(self.name))
+ if not self.has_consumable_resource(available_resources):
+ raise Exception('Not enough consumable resources to execute {}'.format(self.name))
+ if not self.inorder(job_order):
+ raise Exception("Can't execute {} - execute prerequisite actions first".
+ format(self.name))
+ super().act(kb, args) # update knowledge base
+ for resource in self.consumes: # remove consumed resources
+ available_resources[resource] -= self.consumes[resource]
+ self.completed = True # set the task status to complete
+
+ def has_consumable_resource(self, available_resources):
+ """
+ Ensure there are enough consumable resources for this action to execute.
+ """
+ for resource in self.consumes:
+ if available_resources.get(resource) is None:
+ return False
+ if available_resources[resource] < self.consumes[resource]:
+ return False
+ return True
+
+ def has_usable_resource(self, available_resources):
+ """
+ Ensure there are enough usable resources for this action to execute.
+ """
+ for resource in self.uses:
+ if available_resources.get(resource) is None:
+ return False
+ if available_resources[resource] < self.uses[resource]:
+ return False
+ return True
+
+ def inorder(self, job_order):
+ """
+ Ensure that all the jobs that had to be executed before the current one have been
+ successfully executed.
+ """
+ for jobs in job_order:
+ if self in jobs:
+ for job in jobs:
+ if job is self:
+ return True
+ if not job.completed:
+ return False
+ return True
+
+
+class Problem(PDDL):
+ """
+ Define real-world problems by aggregating resources as numerical quantities instead of
+ named entities.
+
+ This class is identical to PDLL, except that it overloads the act function to handle
+ resource and ordering conditions imposed by HLA as opposed to Action.
+ """
+ def __init__(self, initial_state, actions, goal_test, jobs=None, resources={}):
+ super().__init__(initial_state, actions, goal_test)
+ self.jobs = jobs
+ self.resources = resources
+
+ def act(self, action):
+ """
+ Performs the HLA given as argument.
+
+ Note that this is different from the superclass action - where the parameter was an
+ Expression. For real world problems, an Expr object isn't enough to capture all the
+ detail required for executing the action - resources, preconditions, etc need to be
+ checked for too.
+ """
+ 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))
+ list_action.do_action(self.jobs, self.resources, self.kb, args)
+ # print(self.resources)
+
+ def refinements(hla, state, library): # TODO - refinements may be (multiple) HLA themselves ...
+ """
+ state is a Problem, containing the current state kb
+ library is a dictionary containing details for every possible refinement. eg:
+ {
+ "HLA": [
+ "Go(Home,SFO)",
+ "Go(Home,SFO)",
+ "Drive(Home, SFOLongTermParking)",
+ "Shuttle(SFOLongTermParking, SFO)",
+ "Taxi(Home, SFO)"
+ ],
+ "steps": [
+ ["Drive(Home, SFOLongTermParking)", "Shuttle(SFOLongTermParking, SFO)"],
+ ["Taxi(Home, SFO)"],
+ [], # empty refinements ie primitive action
+ [],
+ []
+ ],
+ "precond_pos": [
+ ["At(Home), Have(Car)"],
+ ["At(Home)"],
+ ["At(Home)", "Have(Car)"]
+ ["At(SFOLongTermParking)"]
+ ["At(Home)"]
+ ],
+ "precond_neg": [[],[],[],[],[]],
+ "effect_pos": [
+ ["At(SFO)"],
+ ["At(SFO)"],
+ ["At(SFOLongTermParking)"],
+ ["At(SFO)"],
+ ["At(SFO)"]
+ ],
+ "effect_neg": [
+ ["At(Home)"],
+ ["At(Home)"],
+ ["At(Home)"],
+ ["At(SFOLongTermParking)"],
+ ["At(Home)"]
+ ]
+ }
+ """
+ e = Expr(hla.name, hla.args)
+ indices = [i for i,x in enumerate(library["HLA"]) if expr(x).op == hla.name]
+ for i in indices:
+ action = HLA(expr(library["steps"][i][0]), [ # TODO multiple refinements
+ [expr(x) for x in library["precond_pos"][i]],
+ [expr(x) for x in library["precond_neg"][i]]
+ ],
+ [
+ [expr(x) for x in library["effect_pos"][i]],
+ [expr(x) for x in library["effect_neg"][i]]
+ ])
+ if action.check_precond(state.kb, action.args):
+ yield action
+
+ def hierarchical_search(problem, hierarchy):
+ """
+ [Figure 11.5] 'Hierarchical Search, a Breadth First Search implementation of Hierarchical
+ Forward Planning Search'
+
+ The problem is a real-world prodlem defined by the problem class, and the hierarchy is
+ a dictionary of HLA - refinements (see refinements generator for details)
+ """
+ act = Node(problem.actions[0])
+ frontier = FIFOQueue()
+ frontier.append(act)
+ while(True):
+ if not frontier: #(len(frontier)==0):
+ return None
+ plan = frontier.pop()
+ print(plan.state.name)
+ hla = plan.state #first_or_null(plan)
+ prefix = None
+ if plan.parent:
+ prefix = plan.parent.state.action #prefix, suffix = subseq(plan.state, hla)
+ outcome = Problem.result(problem, prefix)
+ if hla is None:
+ if outcome.goal_test():
+ return plan.path()
+ else:
+ print("else")
+ for sequence in Problem.refinements(hla, outcome, hierarchy):
+ print("...")
+ frontier.append(Node(plan.state, plan.parent, sequence))
+
+ def result(problem, action):
+ """The outcome of applying an action to the current problem"""
+ if action is not None:
+ problem.act(action)
+ return problem
+ else:
+ return problem
+
+
+def job_shop_problem():
+ """
+ [figure 11.1] JOB-SHOP-PROBLEM
+
+ A job-shop scheduling problem for assembling two cars,
+ with resource and ordering constraints.
+
+ Example:
+ >>> from planning import *
+ >>> p = job_shop_problem()
+ >>> p.goal_test()
+ False
+ >>> p.act(p.jobs[1][0])
+ >>> p.act(p.jobs[1][1])
+ >>> p.act(p.jobs[1][2])
+ >>> p.act(p.jobs[0][0])
+ >>> p.act(p.jobs[0][1])
+ >>> p.goal_test()
+ False
+ >>> p.act(p.jobs[0][2])
+ >>> p.goal_test()
+ True
+ >>>
+ """
+ init = [expr('Car(C1)'),
+ expr('Car(C2)'),
+ expr('Wheels(W1)'),
+ expr('Wheels(W2)'),
+ expr('Engine(E2)'),
+ expr('Engine(E2)')]
+
+ def goal_test(kb):
+ # print(kb.clauses)
+ required = [expr('Has(C1, W1)'), expr('Has(C1, E1)'), expr('Inspected(C1)'),
+ expr('Has(C2, W2)'), expr('Has(C2, E2)'), expr('Inspected(C2)')]
+ for q in required:
+ # print(q)
+ # print(kb.ask(q))
+ if kb.ask(q) is False:
+ return False
+ return True
+
+ resources = {'EngineHoists': 1, 'WheelStations': 2, 'Inspectors': 2, 'LugNuts': 500}
+
+ # AddEngine1
+ precond_pos = []
+ precond_neg = [expr("Has(C1,E1)")]
+ effect_add = [expr("Has(C1,E1)")]
+ effect_rem = []
+ add_engine1 = HLA(expr("AddEngine1"),
+ [precond_pos, precond_neg], [effect_add, effect_rem],
+ duration=30, use={'EngineHoists': 1})
+
+ # AddEngine2
+ precond_pos = []
+ precond_neg = [expr("Has(C2,E2)")]
+ effect_add = [expr("Has(C2,E2)")]
+ effect_rem = []
+ add_engine2 = HLA(expr("AddEngine2"),
+ [precond_pos, precond_neg], [effect_add, effect_rem],
+ duration=60, use={'EngineHoists': 1})
+
+ # AddWheels1
+ precond_pos = []
+ precond_neg = [expr("Has(C1,W1)")]
+ effect_add = [expr("Has(C1,W1)")]
+ effect_rem = []
+ add_wheels1 = HLA(expr("AddWheels1"),
+ [precond_pos, precond_neg], [effect_add, effect_rem],
+ duration=30, consume={'LugNuts': 20}, use={'WheelStations': 1})
+
+ # AddWheels2
+ precond_pos = []
+ precond_neg = [expr("Has(C2,W2)")]
+ effect_add = [expr("Has(C2,W2)")]
+ effect_rem = []
+ add_wheels2 = HLA(expr("AddWheels2"),
+ [precond_pos, precond_neg], [effect_add, effect_rem],
+ duration=15, consume={'LugNuts': 20}, use={'WheelStations': 1})
+
+ # Inspect1
+ precond_pos = []
+ precond_neg = [expr("Inspected(C1)")]
+ effect_add = [expr("Inspected(C1)")]
+ effect_rem = []
+ inspect1 = HLA(expr("Inspect1"),
+ [precond_pos, precond_neg], [effect_add, effect_rem],
+ duration=10, use={'Inspectors': 1})
+
+ # Inspect2
+ precond_pos = []
+ precond_neg = [expr("Inspected(C2)")]
+ effect_add = [expr("Inspected(C2)")]
+ effect_rem = []
+ inspect2 = HLA(expr("Inspect2"),
+ [precond_pos, precond_neg], [effect_add, effect_rem],
+ duration=10, use={'Inspectors': 1})
+
+ job_group1 = [add_engine1, add_wheels1, inspect1]
+ job_group2 = [add_engine2, add_wheels2, inspect2]
+
+ return Problem(init, [add_engine1, add_engine2, add_wheels1, add_wheels2, inspect1, inspect2],
+ goal_test, [job_group1, job_group2], resources)
+
diff --git a/tests/test_planning.py b/tests/test_planning.py
index e13bcfd92..0e57ffca6 100644
--- a/tests/test_planning.py
+++ b/tests/test_planning.py
@@ -81,3 +81,51 @@ def test_graph_call():
graph()
assert levels_size == len(graph.levels) - 1
+
+
+def test_job_shop_problem():
+ p = job_shop_problem()
+ assert p.goal_test() is False
+
+ solution = [p.jobs[1][0],
+ p.jobs[0][0],
+ p.jobs[0][1],
+ p.jobs[0][2],
+ p.jobs[1][1],
+ p.jobs[1][2]]
+
+ for action in solution:
+ p.act(action)
+
+ assert p.goal_test()
+
+def test_refinements() :
+ init = [expr('At(Home)')]
+ def goal_test(kb):
+ return kb.ask(expr('At(SFO)'))
+
+ library = {"HLA": ["Go(Home,SFO)","Taxi(Home, SFO)"],
+ "steps": [["Taxi(Home, SFO)"],[]],
+ "precond_pos": [["At(Home)"],["At(Home)"]],
+ "precond_neg": [[],[]],
+ "effect_pos": [["At(SFO)"],["At(SFO)"]],
+ "effect_neg": [["At(Home)"],["At(Home)"],]}
+ # Go SFO
+ precond_pos = [expr("At(Home)")]
+ precond_neg = []
+ effect_add = [expr("At(SFO)")]
+ effect_rem = [expr("At(Home)")]
+ go_SFO = HLA(expr("Go(Home,SFO)"),
+ [precond_pos, precond_neg], [effect_add, effect_rem])
+ # Taxi SFO
+ precond_pos = [expr("At(Home)")]
+ precond_neg = []
+ effect_add = [expr("At(SFO)")]
+ effect_rem = [expr("At(Home)")]
+ taxi_SFO = HLA(expr("Go(Home,SFO)"),
+ [precond_pos, precond_neg], [effect_add, effect_rem])
+ prob = Problem(init, [go_SFO, taxi_SFO], goal_test)
+ result = [i for i in Problem.refinements(go_SFO, prob, library)]
+ assert(len(result) == 1)
+ assert(result[0].name == "Taxi")
+ assert(result[0].args == (expr("Home"), expr("SFO")))
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