class BDIBehaviour(PeriodicBehaviour):
async def on_start(self):
self.terminate = False
self.SUCCESS = False
self.verbose = False
# Initialization
self.beliefs = WorldModel() # B := B0; Initial Beliefs
self.goals = self.beliefs.current_world_model.goals
self.intentions = self.beliefs.current_world_model.goals # I := I0; Initial Intentions
self.htn_planner = HierarchicalTaskNetworkPlanner(self.beliefs)
self.perception = Perception(self.beliefs)
self.coordination = Coordination(self.beliefs)
self.monitoring = Monitoring()
self.what, self.why, self.how_well, self.what_else, self.why_failed = "", "", "", "", ""
self.plans = []
self.selected_plan = []
self.percept = {}
self.action = ""
self.start_time = datetime.datetime.now()
async def run(self):
"""
Agent control loop version 7 (Intention Reconsideration)
I := I0; Initial Intentions
B := B0; Initial Beliefs
while true do
get next percept ρ; # OBSERVE the world
B:= brf(B, ρ); # Belief revision function
D: = option(B, I);
I := filter(B, D, I);
π := plan(B, I); # MEANS_END REASONING
while not (empty(π) or succeeded(Ι, Β) or impossible(I, B)) do # Drop impossible or succeeded intentions
α := hd(π); # Pop first action
execute(α);
π := tail(π);
get next percept ρ;
B:= brf(B, ρ);
if reconsider(I, B) then # Meta-level control: explicit decision, to avoid reconsideration cost
D := options(B, I);
I := filter(B, D, I);
end-if
if not sound(π, I, B) then # Re-activity, re-plan
π := plan(B, I);
end-if
end-while
end-while
"""
if self.verbose:
print(f"--- arm_agent: "
f"PeriodicSenderBehaviour running "
f"at {datetime.datetime.now().time()}: "
f"{self.beliefs.current_world_model.ticks}")
# while true do
if not self.SUCCESS and not self.terminate and self.beliefs.update_tick(
) < self.beliefs.current_world_model.max_ticks:
if len(self.selected_plan) == 0:
# msg = Message(to="madks2@temp3rr0r-pc") # Instantiate the message # TODO: place holder for IM
# msg.body = "Hello World: " + str(self.counter) # Set the message content
# await self.send(msg)
# TODO: engrave figures: arm IK, gripper
self.percept = self.perception.get_percept(
text_engraving=(self.why_failed, self.how_well)
) # get next percept ρ; OBSERVE the world
self.beliefs = self.perception.belief_revision(
self.beliefs, self.percept) # B:= brf(B, ρ);
self.beliefs = self.monitoring.fire_events(
self.beliefs, self.percept)
self.intentions = self.deliberate(
self.beliefs, self.intentions
) # DELIBERATE about what INTENTION to achieve next
self.SUCCESS = True if self.intentions == "" else False
self.plans = self.htn_planner.get_plans(
self.beliefs.current_world_model, self.intentions
) # π := plan(B, I); MEANS_END REASONING
if self.plans != False:
if len(self.plans) > 0:
self.plans.sort(
key=len
) # TODO: check why sorting doesn't work on "deeper" levels
if self.verbose:
print("{}: Plan: {}".format(
self.beliefs.current_world_model.tick,
self.plans[0]))
self.selected_plan = deque(
self.plans[0]
) # TODO: Use a "cost function" to evaluate the best plan, not shortest
self.why_failed = ""
else:
self.why_failed = self.htn_planner.failure_reason
if self.verbose:
print("Plan failure_reason: {}".format(
self.why_failed),
end=" ")
self.how_well = (
self.beliefs.current_world_model.tick,
self.beliefs.current_world_model.max_ticks,
int((datetime.datetime.now() -
self.start_time).total_seconds() *
1000), # milliseconds
self.plans)
if self.verbose:
print("how_well: {}".format(self.how_well))
else: # while not (empty(π) or succeeded(Ι, Β) or impossible(I, B)) do
self.action, self.selected_plan = self.selected_plan.popleft(
), self.selected_plan # α := hd(π); π := tail(π);
if self.verbose:
print("{}: Action: {}".format(
self.beliefs.current_world_model.tick,
self.action))
self.coordination.execute_action(
self.action,
self.beliefs.current_world_model) # execute(α);
self.what, self.why = self.action, self.intentions
self.how_well = (
self.beliefs.current_world_model.tick,
self.beliefs.current_world_model.max_ticks,
int((datetime.datetime.now() -
self.start_time).total_seconds() *
1000), # milliseconds
self.selected_plan)
self.what_else = self.plans[1] if len(
self.plans) > 1 else self.plans[0]
if self.verbose:
self.print_answers(self.what, self.why, self.how_well,
self.what_else)
# get next percept ρ; OBSERVE the world
# TODO: Don't update target object location, when it is obscured
if self.action != ('move_arm_above',
'target_object') and self.action != (
'move_arm', 'target_object'):
self.percept = self.perception.get_percept(
text_engraving=(self.what, self.why, self.how_well,
self.what_else))
self.beliefs = self.perception.belief_revision(
self.beliefs, self.percept)
self.beliefs = self.monitoring.fire_events(
self.beliefs, self.percept)
# TODO: trigger sound percept?
if self.action == ('initialize', 'arm'):
self.percept = {
"initialized": {
'arm': True
}
} # TODO: post conditions or monitoring
self.beliefs = self.perception.belief_revision(
self.beliefs,
self.percept) # TODO: post conditions
self.beliefs = self.monitoring.fire_events(
self.beliefs, self.percept)
elif self.action == ('move_arm', 'target_object'):
self.percept = {
"distance": {
'distance_to_gripper': 2.2
}
} # TODO: update with monitoring
self.beliefs = self.perception.belief_revision(
self.beliefs, self.percept)
self.beliefs = self.monitoring.fire_events(
self.beliefs, self.percept)
elif self.action == ('move_arm_above', 'container'):
self.percept = {
"location": {
"target_object": "container"
},
"grabbed": {
'target_object': False
}
}
self.beliefs = self.perception.belief_revision(
self.beliefs, self.percept)
self.beliefs = self.monitoring.fire_events(
self.beliefs, self.percept)
# if reconsider(I, B) then
# D := options(B, I);
# I := filter(B, D, I);
# if not sound(π, I, B) then
# π := plan(B, I)
# TODO: Backtracking of action?
else:
self.done()
self.kill()
async def on_end(self):
print("-- on_end")
print("Done.")
self.perception.destroy()
# stop agent from behaviour
await self.agent.stop()
# async def _step(self): # TODO: Need?
# # the bdi stuff
# pass
def done(self):
print("-- Sender Agent: Done")
show_history = True
print("Final World model:")
print("-- Ticks: {}".format(self.beliefs.current_world_model.tick))
print("-- initialized: {}".format(
self.beliefs.current_world_model.initialized))
print("-- location: {}".format(
self.beliefs.current_world_model.location))
print("-- grabbed: {}".format(
self.beliefs.current_world_model.grabbed))
if show_history:
print()
print("World model History:")
for tick in range(len(self.beliefs.world_model_history)):
print("Tick {}:".format(tick))
print("-- initialized: {}".format(
self.beliefs.world_model_history[tick].initialized))
print("-- location: {}".format(
self.beliefs.world_model_history[tick].location))
print("-- grabbed: {}".format(
self.beliefs.world_model_history[tick].grabbed))
print()
print("{}!".format("SUCCESS" if self.SUCCESS else "FAIL"))
def print_answers(self, what_answer, why_answer, how_well_answer,
what_else_answer):
print(
"Q: What is the robot doing? A: {}\n"
"Q: Why is it doing it? A: {}\n"
"Q: How well is it doing it? A: {}\n"
"Q: What else could it have been doing instead? A: {}".format(
what_answer, why_answer, how_well_answer,
what_else_answer))
def deliberate(self, current_beliefs, current_intentions):
"""
Decide WHAT sate of affairs you want to achieve.
:param current_beliefs: WordModel instance, the world model, information about the world.
:param current_intentions: List of tuples, tasks that the agent has COMMITTED to accomplish.
:return: List of tuples, filtered intentions that the agent COMMITS to accomplish.
"""
# 1. Option Generation: The agent generates a set of possible alternatives.
current_desires = current_intentions # TODO: D := option(B, I);
# 2. Filtering: Agent chooses between competing alternatives and commits to achieving them.
current_intentions = self.filter_intentions(
current_beliefs, current_desires,
current_intentions) # I := filter(B, D, I);
return current_intentions
def filter_intentions(self, current_beliefs, current_desires,
current_intentions):
"""
Choose between competing alternatives and COMMITTING to achieving them.
:param current_beliefs: WordModel instance, the world model, information about the world.
:param current_desires: List of tuples, tasks that the agent would like to accomplish.
:param current_intentions: List of tuples, tasks that the agent has COMMITTED to accomplish.
:return: List of tuples, filtered intentions that the agent COMMITS to accomplish.
"""
for current_intention in current_intentions:
if current_intention == self.goals[0] and current_beliefs.current_world_model.location["target_object"] \
== "container":
current_intentions = "" # if goal(s) achieved, empty I
return current_intentions
class BDIBehaviour(PeriodicBehaviour):
async def on_start(self):
self.terminate = False
self.SUCCESS = False
self.verbose = False
# Initialization
self.htn_planner = HierarchicalTaskNetworkPlanner()
self.goal = [('transfer_target_object_to_container', 'arm',
'target_object', 'table', 'container')]
self.intentions = self.goal # I := I0; Initial Intentions
self.beliefs = WorldModel() # B := B0; Initial Beliefs
self.monitoring = Monitoring()
self.perception = Perception()
self.coordination = Coordination()
# Disable all 3 coordination switches for testing
self.coordination.control.send_requests = True
self.coordination.control.center_init = False
self.coordination.control.detect_last_position = True
self.what, self.why, self.how_well, self.what_else, self.why_failed = "", "", "", "", ""
self.plans = []
self.start_time = datetime.datetime.now()
async def run(self):
# print(f"-- Sender Agent: PeriodicSenderBehaviour running at {datetime.datetime.now().time()}: {self.counter}")
print("run tick: {}".format(self.beliefs.current_world_model.tick))
# msg = Message(to="madks2@temp3rr0r-pc") # Instantiate the message
# msg.body = "Hello World: " + str(self.counter) # Set the message content
# await self.send(msg)
self.add_belief()
self.add_desire()
self.add_intention()
print("-- Sender Agent: Message sent!")
if self.beliefs.update_tick(
) > self.beliefs.current_world_model.max_ticks:
self.done()
self.kill()
else:
SUCCESS = True if self.intentions == "" else False
self.plans = self.htn_planner.get_plans(
self.beliefs.current_world_model,
self.intentions) # π := plan(B, I); MEANS_END REASONING
async def on_end(self):
print("-- on_end")
# stop agent from behaviour
await self.agent.stop()
# async def _step(self):
# # the bdi stuff
# pass
#
def add_belief(self):
print("-- Sender Agent: add_belief")
percept = self.perception.get_percept(text_engraving=(
self.why_failed,
self.how_well)) # get next percept ρ; OBSERVE the world
self.beliefs = self.perception.belief_revision(
self.beliefs, percept) # B:= brf(B, ρ);
self.beliefs = self.monitoring.fire_events(self.beliefs, percept)
def add_desire(self):
print("-- Sender Agent: add_desire")
def add_intention(self):
print("-- Sender Agent: add_intention")
self.intentions = self.deliberate(
self.beliefs, self.intentions
) # DELIBERATE about what INTENTION to achieve next
def done(self):
# the done evaluation
print("-- Sender Agent: Done")
def deliberate(self, current_beliefs, current_intentions):
"""
Decide WHAT sate of affairs you want to achieve.
:param current_beliefs: WordModel instance, the world model, information about the world.
:param current_intentions: List of tuples, tasks that the agent has COMMITTED to accomplish.
:return: List of tuples, filtered intentions that the agent COMMITS to accomplish.
"""
# 1. Option Generation: The agent generates a set of possible alternatives.
current_desires = current_intentions # TODO: D := option(B, I);
# 2. Filtering: Agent chooses between competing alternatives and commits to achieving them.
current_intentions = self.filter_intentions(
current_beliefs, current_desires,
current_intentions) # I := filter(B, D, I);
return current_intentions
def filter_intentions(self, current_beliefs, current_desires,
current_intentions):
"""
Choose between competing alternatives and COMMITTING to achieving them.
:param current_beliefs: WordModel instance, the world model, information about the world.
:param current_desires: List of tuples, tasks that the agent would like to accomplish.
:param current_intentions: List of tuples, tasks that the agent has COMMITTED to accomplish.
:return: List of tuples, filtered intentions that the agent COMMITS to accomplish.
"""
for current_intention in current_intentions:
if current_intention == (
'transfer_target_object_to_container', 'arm', 'target_object', 'table', 'container') \
and current_beliefs.current_world_model.location["target_object"] == "container":
current_intentions = "" # if goal(s) achieved, empty I
return current_intentions
end-if
end-while
end-while
"""
# while true do
while not SUCCESS and not terminate and beliefs.update_tick(
) < beliefs.current_world_model.max_ticks:
# TODO: engrave figures: arm IK, gripper
percept = perception.get_percept(
text_engraving=(why_failed,
how_well)) # get next percept ρ; OBSERVE the world
beliefs = perception.belief_revision(beliefs,
percept) # B:= brf(B, ρ);
beliefs = monitoring.fire_events(beliefs, percept)
intentions = deliberate(
beliefs,
intentions) # DELIBERATE about what INTENTION to achieve next
SUCCESS = True if intentions == "" else False
plans = htn_planner.get_plans(
beliefs.current_world_model,
intentions) # π := plan(B, I); MEANS_END REASONING
if plans != False:
if len(plans) > 0:
plans.sort(
key=len
) # TODO: check why sorting doesn't work on "deeper" levels
if verbose: