def next_action(self, dialogue_state):
"""
Generate a response given which conditions are met by the current
dialogue state.
:param dialogue_state:
:return:
"""
# Check for terminal state
if dialogue_state.is_terminal_state:
return [DialogueAct('bye', [DialogueActItem('', Operator.EQ, '')])]
# Select intent
intent = random.choice(self.intents)
dact = DialogueAct(intent, [])
# Select slot
if intent in ['inform', 'request']:
if intent == 'inform':
# The Dialogue Manager will fill the slot's value
slot = random.choice(self.ontology.ontology['requestable'])
else:
slot = \
random.choice(self.ontology.ontology['system_requestable'])
dact.params = [DialogueActItem(slot, Operator.EQ, '')]
return [dact]
def start_dialogue(self, args=None):
"""
Reset or initialize internal structures at the beginning of the
dialogue. May issue first utterance if this agent has the initiative.
:param args:
:return:
"""
self.initialize()
self.dialogue_turn = 0
if args and 'goal' in args:
self.agent_goal = deepcopy(args['goal'])
elif self.goal_generator:
self.agent_goal = self.goal_generator.generate()
print(f'GOAL:\n=====\n{self.agent_goal}')
# TODO: Get initial trigger from config
if self.INTERACTION_MODE == 'dialogue_acts':
self.prev_m_out = \
ConversationalFrame([DialogueAct('hello')])
else:
self.prev_m_out = ConversationalFrame('hello')
self.continue_dialogue(args)
return {
'input_utterance': None,
'output_raw': self.prev_m_out.content,
'output_dacts': '',
'goal': self.agent_goal
}
def test_nlg(self):
self.assertEqual(
SlotFillingNLG().generate_output({
'dacts': [DialogueAct('request', [DialogueActItem('pricerange', Operator.EQ, '')])],
'system': True}
),
'Which price range do you prefer? '
)
def receive_input(self, system_acts):
"""
Process input received and do some housekeeping.
:param system_acts: list containing dialogue acts from the system
:return: nothing
"""
if self.prev_sys_acts and self.prev_sys_acts == system_acts:
self.curr_patience -= 1
else:
self.curr_patience = self.patience
self.prev_sys_acts = deepcopy(system_acts)
self.input_system_acts = deepcopy(system_acts)
# Check for goal satisfaction
# Update user goal
for system_act in system_acts:
if system_act.intent == 'offer':
self.offer_made = True
# Reset past requests
# TODO: Is this reasonable?
self.goal.actual_requests = {}
for item in self.goal.requests:
self.goal.requests[item].value = ''
# Gather all inform or offer params into one dialogue act
inform_dact = DialogueAct('inform', [])
for system_act in system_acts:
if system_act.intent in ['inform', 'offer']:
inform_dact.params += deepcopy(system_act.params)
# Check for constraint satisfaction
if self.offer_made:
# Check that the venue provided meets the constraints
meets_constraints = all([
i.value == self.goal.constraints[i.slot].value
for i in inform_dact.params if i.slot in self.goal.constraints
])
# If it meets the constraints, update the requests
if meets_constraints:
for item in inform_dact.params:
if item.slot in self.goal.actual_requests:
self.goal.actual_requests[item.slot].value = item.value
if item.slot in self.goal.requests:
self.goal.requests[item.slot].value = item.value
# Use the true requests for asserting goal is met
self.goal_met = True
for slot in self.goal.requests:
if not self.goal.requests[slot].value:
self.goal_met = False
break
def test_nlu(self):
self.assertEqual(
SlotFillingNLU({'ontology': 'example/domains/CamRestaurants-rules.json',
'database': 'example/domains/CamRestaurants-dbase.db'}).process_input(
'looking for an expensive restaurant'
),
[DialogueAct('inform',
[DialogueActItem('pricerange', Operator.EQ, 'expensive')])]
)
def respond(self):
"""
Creates the response of the simulated user.
:return: List of DialogueActs as a response
"""
if self.curr_patience == 0:
if self.nlg:
return self.nlg.generate_output({
'dacts': [DialogueAct('bye', [])],
'system':
False
})
else:
return [DialogueAct('bye', [])]
# Sample the number of acts to pop.
acts = []
pops = min(
random.choices(range(1,
len(self.pop_distribution) + 1),
weights=self.pop_distribution)[0],
self.agenda.size())
for pop in range(pops):
act = self.error_model.semantic_noise(self.agenda.pop())
# Keep track of actual requests made. These are used in reward and
# success calculation
if act.intent == 'request' and act.params:
self.goal.actual_requests[act.params[0].slot] = act.params[0]
acts.append(act)
if self.nlg:
acts = self.nlg.generate_output({'dacts': acts, 'system': False})
return acts
def consistency_check(self):
"""
Perform some basic checks to ensure that items in the agenda are
consistent - i.e. not duplicate, not
contradicting with current goal, etc.
:return: Nothing
"""
# Remove all requests for slots that are filled in the goal
if self.goal:
for slot in self.goal.actual_requests:
if self.goal.actual_requests[slot].value:
self.remove(
DialogueAct('request',
[DialogueActItem(slot, Operator.EQ, '')]))
else:
print('Warning! Agenda consistency check called without goal. '
'Did you forget to initialize?')
def initialize(self, goal, us_has_initiative=False):
"""
Initialize the Agenda at the beginning of each dialogue
:param goal: the new goal for the current dialogue
:param us_has_initiative: if the simulator has the initiative at the
first turn
:return: nothing
"""
self.goal = goal
self.clear()
# Generate candidate actions
dacts = []
# If there are sub-goals
# Iterate from last to first because the acts will be popped in
# reverse order.
for i in range(len(self.goal.subgoals) - 1, -1, -1):
sg = self.goal.subgoals[i]
# Acknowledge completion of subgoal
dacts.append(DialogueAct('ack_subgoal', []))
for constr in sg.constraints.values():
dacts.append(DialogueAct('inform', [constr]))
for req in goal.requests.values():
dacts.append((DialogueAct('request', [req])))
for constr in goal.constraints.values():
dacts.append(DialogueAct('inform', [constr]))
# Push actions into the agenda
self.push(DialogueAct('bye', []))
for da in dacts:
self.push(da, force=True)
if us_has_initiative:
self.push(DialogueAct('hello', []))
def end_dialogue(self):
"""
Perform final dialogue turn. Train and ave models if applicable.
:return:
"""
if self.dialogue_episode % \
self.train_switch_trainable_agents_every == 0:
self.train_system = not self.train_system
# Record final state
if self.curr_state:
if not self.curr_state.is_terminal_state:
self.curr_state.is_terminal_state = True
self.prev_reward, self.prev_success, self.prev_task_success = \
self.reward_func.calculate(
self.curr_state,
[DialogueAct('bye', [])],
goal=self.agent_goal,
agent_role=self.agent_role
)
else:
print(
'Warning! Conversational Multi Agent attempted to end the'
'dialogue with no state. This dialogue will NOT be saved.')
return
self.recorder.record(
self.curr_state,
self.curr_state,
self.prev_action,
self.prev_reward,
self.prev_success,
input_utterance=self.prev_usr_utterance,
output_utterance=self.prev_sys_utterance,
task_success=self.prev_task_success,
role=self.agent_role,
force_terminate=True
)
self.dialogue_episode += 1
if self.IS_TRAINING:
if not self.train_alternate_training or \
(self.train_system and
self.agent_role == 'system' or
not self.train_system and
self.agent_role == 'user'):
if self.dialogue_episode % self.train_interval == 0 and \
len(self.recorder.dialogues) >= self.minibatch_length:
for epoch in range(self.train_epochs):
print(
'{0}: Training epoch {1} of {2}'.format(
self.agent_role,
(epoch+1),
self.train_epochs
)
)
# Sample minibatch
minibatch = random.sample(
self.recorder.dialogues,
self.minibatch_length
)
if self.nlu:
self.nlu.train(minibatch)
self.dialogue_manager.train(minibatch)
if self.nlg:
self.nlg.train(minibatch)
self.cumulative_rewards += \
self.recorder.dialogues[-1][-1]['cumulative_reward']
if self.dialogue_turn > 0:
self.total_dialogue_turns += self.dialogue_turn
if self.dialogue_episode % self.SAVE_INTERVAL == 0:
if self.nlu:
self.nlu.save()
if self.dialogue_manager:
self.dialogue_manager.save()
if self.nlg:
self.nlg.save()
# Count successful dialogues
if self.recorder.dialogues[-1][-1]['success']:
print(
'{0} SUCCESS! (reward: {1})'.format(
self.agent_role,
sum([t['reward'] for t in self.recorder.dialogues[-1]])
)
)
self.num_successful_dialogues += \
int(self.recorder.dialogues[-1][-1]['success'])
else:
print(
'{0} FAILURE. (reward: {1})'.format(
self.agent_role,
sum([t['reward'] for t in self.recorder.dialogues[-1]])
)
)
if self.recorder.dialogues[-1][-1]['task_success']:
self.num_task_success += int(
self.recorder.dialogues[-1][-1]['task_success']
)
def continue_dialogue(self, args):
"""
Perform next dialogue turn.
:return: this agent's output
"""
if 'other_input_raw' not in args:
raise ValueError(
'ConversationalMultiAgent called without raw input!'
)
other_input_raw = args['other_input_raw']
other_input_dacts = None
if 'other_input_dacts' in args:
other_input_dacts = args['other_input_dacts']
goal = None
if 'goal' in args:
goal = args['goal']
if goal:
self.agent_goal = goal
sys_utterance = ''
other_input_nlu = deepcopy(other_input_raw)
if self.nlu and isinstance(other_input_raw, str):
# Process the other agent's utterance
other_input_nlu = self.nlu.process_input(
other_input_raw,
self.dialogue_manager.get_state()
)
elif other_input_dacts:
# If no utterance provided, use the dacts
other_input_nlu = other_input_dacts
self.dialogue_manager.receive_input(other_input_nlu)
# Keep track of prev_state, for the DialogueEpisodeRecorder
# Store here because this is the state that the dialogue
# manager will use to make a decision.
self.curr_state = deepcopy(self.dialogue_manager.get_state())
# Update goal's ground truth
if self.agent_role == 'system':
self.agent_goal.ground_truth = deepcopy(
self.curr_state.item_in_focus
)
if self.dialogue_turn < self.MAX_TURNS:
response = self.dialogue_manager.generate_output()
self.agent_goal = self.dialogue_manager.DSTracker.DState.user_goal
else:
# Force dialogue stop
response = [DialogueAct('bye', [])]
rew, success, task_success = self.reward_func.calculate(
self.dialogue_manager.get_state(),
response,
goal=self.agent_goal,
agent_role=self.agent_role
)
if self.USE_NLG:
sys_utterance = self.nlg.generate_output(
{
'dacts': response,
'system': self.agent_role == 'system',
'last_sys_utterance': other_input_raw
}
) + ' '
print('{0} > {1}'.format(self.agent_role.upper(), sys_utterance))
if self.USE_SPEECH:
tts = gTTS(text=sys_utterance, lang='en')
tts.save('sys_output.mp3')
os.system('afplay sys_output.mp3')
else:
print(
'{0} > {1} \n'.format(
self.agent_role.upper(),
'; '.join([str(sr) for sr in response])
)
)
if self.prev_state:
self.recorder.record(
self.prev_state,
self.curr_state,
self.prev_action,
self.prev_reward,
self.prev_success,
input_utterance=self.prev_usr_utterance,
output_utterance=self.prev_sys_utterance,
task_success=self.prev_task_success
)
self.dialogue_turn += 1
self.prev_state = deepcopy(self.curr_state)
self.prev_usr_utterance = deepcopy(other_input_raw)
self.prev_sys_utterance = deepcopy(sys_utterance)
self.prev_action = deepcopy(response)
self.prev_reward = rew
self.prev_success = success
self.prev_task_success = task_success
return {'input_utterance': None,
'output_raw': sys_utterance,
'output_dacts': response,
'goal': self.agent_goal
}
def start_dialogue(self, args=None):
"""
Perform initial dialogue turn.
:param args: optional arguments
:return:
"""
self.dialogue_turn = 0
if self.agent_role == 'user':
self.agent_goal = self.goal_generator.generate()
self.dialogue_manager.update_goal(self.agent_goal)
print('DEBUG > usr goal:')
for c in self.agent_goal.constraints:
print(f'\t\tConstr({self.agent_goal.constraints[c].slot}='
f'{self.agent_goal.constraints[c].value})')
print('\t\t-------------')
for r in self.agent_goal.requests:
print(f'\t\tReq({self.agent_goal.requests[r].slot})')
print('\n')
elif args and 'goal' in args:
# No deep copy here so that all agents see the same goal.
self.agent_goal = args['goal']
else:
raise ValueError(
'ConversationalMultiAgent - no goal provided '
'for agent {0}!'.format(self.agent_role)
)
self.dialogue_manager.restart({'goal': self.agent_goal})
response = [DialogueAct('welcomemsg', [])]
response_utterance = ''
# The above forces the DM's initial utterance to be the welcomemsg act.
# The code below can be used to push an empty list of DialogueActs to
# the DM and get a response from the model.
# self.dialogue_manager.receive_input([])
# response = self.dialogue_manager.respond()
if self.agent_role == 'system':
response = [DialogueAct('welcomemsg', [])]
if self.USE_NLG:
response_utterance = self.nlg.generate_output(
{
'dacts': response,
'system': self.agent_role == 'system',
'last_sys_utterance': ''
}
)
print(
'{0} > {1}'.format(
self.agent_role.upper(),
response_utterance
)
)
if self.USE_SPEECH:
tts = gTTS(text=response_utterance, lang='en')
tts.save('sys_output.mp3')
os.system('afplay sys_output.mp3')
else:
print(
'{0} > {1}'.format(
self.agent_role.upper(),
'; '.join([str(sr) for sr in response])
)
)
# TODO: Generate output depending on initiative - i.e.
# have users also start the dialogue
self.prev_state = None
# Re-initialize these for good measure
self.curr_state = None
self.prev_usr_utterance = None
self.prev_sys_utterance = None
self.prev_action = None
self.prev_reward = None
self.prev_success = None
return {'input_utterance': None,
'output_raw': response_utterance,
'output_dacts': response,
'goal': self.agent_goal
}
def decode_action(self, action_enc, system=True):
"""
Decode the action, given the role. Note that does not have to match
the agent's role, as the agent may be decoding another agent's action
(e.g. a system decoding the previous user act).
:param action_enc: action encoding to be decoded
:param system: whether the role whose action we are decoding is a
'system'
:return: the decoded action
"""
if system:
if action_enc < len(self.dstc2_acts_sys):
return [DialogueAct(self.dstc2_acts_sys[action_enc], [])]
if action_enc < len(self.dstc2_acts_sys) + \
len(self.system_requestable_slots):
return [
DialogueAct('request', [
DialogueActItem(
self.system_requestable_slots[
action_enc - len(self.dstc2_acts_sys)],
Operator.EQ, '')
])
]
if action_enc < len(self.dstc2_acts_sys) + \
len(self.system_requestable_slots) + \
len(self.requestable_slots):
index = action_enc - len(self.dstc2_acts_sys) - \
len(self.system_requestable_slots)
return [
DialogueAct('inform', [
DialogueActItem(self.requestable_slots[index],
Operator.EQ, '')
])
]
else:
if action_enc < len(self.dstc2_acts_usr):
return [DialogueAct(self.dstc2_acts_usr[action_enc], [])]
if action_enc < len(self.dstc2_acts_usr) + \
len(self.requestable_slots):
return [
DialogueAct('request', [
DialogueActItem(
self.requestable_slots[action_enc -
len(self.dstc2_acts_usr)],
Operator.EQ, '')
])
]
if action_enc < len(self.dstc2_acts_usr) + \
2 * len(self.requestable_slots):
return [
DialogueAct('inform', [
DialogueActItem(
self.requestable_slots[
action_enc - len(self.dstc2_acts_usr) -
len(self.requestable_slots)], Operator.EQ, '')
])
]
# Default fall-back action
print('Reinforce dialogue policy ({0}) policy action decoder warning: '
'Selecting default action (index: {1})!'.format(
self.agent_role, action_enc))
return [DialogueAct('bye', [])]
def continue_dialogue(self):
"""
Perform next dialogue turn.
:return: nothing
"""
usr_utterance = ''
sys_utterance = ''
if self.USE_USR_SIMULATOR:
usr_input = self.user_simulator.respond()
# TODO: THIS FIRST IF WILL BE HANDLED BY ConversationalAgentGeneric
# -- SHOULD NOT LIVE HERE
if isinstance(self.user_simulator, DTLUserSimulator):
print('USER (nlg) > %s \n' % usr_input)
usr_input = self.nlu.process_input(
usr_input,
self.dialogue_manager.get_state()
)
elif self.USER_SIMULATOR_NLG:
print('USER > %s \n' % usr_input)
if self.nlu:
usr_input = self.nlu.process_input(usr_input)
# Otherwise it will just print the user's nlg but use the
# simulator's output DActs to proceed.
else:
print('USER (DACT) > %s \n' % '; '.join(
[str(ui) for ui in usr_input]))
else:
if self.USE_SPEECH:
# Listen for input from the microphone
with speech_rec.Microphone() as source:
print('(listening...)')
audio = self.asr.listen(source, phrase_time_limit=3)
try:
# This uses the default key
usr_utterance = self.asr.recognize_google(audio)
print("Google ASR: " + usr_utterance)
except speech_rec.UnknownValueError:
print("Google ASR did not understand you")
except speech_rec.RequestError as e:
print("Google ASR request error: {0}".format(e))
else:
usr_utterance = input('USER > ')
# Process the user's utterance
if self.nlu:
usr_input = self.nlu.process_input(
usr_utterance,
self.dialogue_manager.get_state()
)
else:
raise EnvironmentError(
'agent: No nlu defined for '
'text-based interaction!'
)
self.dialogue_manager.receive_input(usr_input)
# Keep track of prev_state, for the DialogueEpisodeRecorder
# Store here because this is the state that the dialogue manager
# will use to make a decision.
self.curr_state = deepcopy(self.dialogue_manager.get_state())
if self.dialogue_turn < self.MAX_TURNS:
sys_response = self.dialogue_manager.generate_output()
else:
# Force dialogue stop
sys_response = [DialogueAct('bye', [])]
if self.USE_NLG:
sys_utterance = self.nlg.generate_output({'dacts': sys_response})
print('SYSTEM > %s ' % sys_utterance)
if self.USE_SPEECH:
try:
tts = gTTS(text=sys_utterance, lang='en')
tts.save('sys_output.mp3')
os.system('afplay sys_output.mp3')
except:
print('WARNING: gTTS encountered an error. '
'Falling back to sys TTS.')
os.system('say ' + sys_utterance)
else:
print('SYSTEM > %s ' % '; '.join([str(sr) for sr in sys_response]))
if self.USE_USR_SIMULATOR:
usim_input = sys_response
if self.USER_SIMULATOR_NLU and self.USE_NLG:
usim_input = \
self.user_simulator.nlu.process_input(sys_utterance)
self.user_simulator.receive_input(usim_input)
rew, success, task_success = \
self.reward_func.calculate(
self.dialogue_manager.get_state(),
sys_response,
self.user_simulator.goal
)
else:
rew, success, task_success = 0, None, None
if self.prev_state:
self.recorder.record(
self.prev_state,
self.curr_state,
self.prev_action,
self.prev_reward,
self.prev_success,
input_utterance=self.prev_usr_utterance,
output_utterance=self.prev_sys_utterance,
task_success=self.prev_task_success
)
self.dialogue_turn += 1
self.prev_state = deepcopy(self.curr_state)
self.prev_action = deepcopy(sys_response)
self.prev_usr_utterance = deepcopy(usr_utterance)
self.prev_sys_utterance = deepcopy(sys_utterance)
self.prev_reward = rew
self.prev_success = success
self.prev_task_success = task_success
def decode_action(self, action_enc, system=True):
"""
Decode the action, given the role. Note that does not have to match
the agent's role, as the agent may be decoding another agent's action
(e.g. a system decoding the previous user act).
:param action_enc: action encoding to be decoded
:param system: whether the role whose action we are decoding is a
'system'
:return: the decoded action
"""
if system:
if action_enc < len(self.dstc2_acts_sys):
return [DialogueAct(self.dstc2_acts_sys[action_enc], [])]
if action_enc < len(self.dstc2_acts_sys) + \
len(self.system_requestable_slots):
return [
DialogueAct('request', [
DialogueActItem(
self.system_requestable_slots[
action_enc - len(self.dstc2_acts_sys)],
Operator.EQ, '')
])
]
if action_enc < len(self.dstc2_acts_sys) + \
len(self.system_requestable_slots) +\
len(self.requestable_slots):
index = action_enc - \
len(self.dstc2_acts_sys) - \
len(self.system_requestable_slots)
return [
DialogueAct('inform', [
DialogueActItem(self.requestable_slots[index],
Operator.EQ, '')
])
]
else:
if action_enc < len(self.dstc2_acts_usr):
return [DialogueAct(self.dstc2_acts_usr[action_enc], [])]
if action_enc < len(self.dstc2_acts_usr) + \
len(self.requestable_slots):
return [
DialogueAct('request', [
DialogueActItem(
self.requestable_slots[action_enc -
len(self.dstc2_acts_usr)],
Operator.EQ, '')
])
]
if action_enc < len(self.dstc2_acts_usr) + \
2 * len(self.requestable_slots):
return [
DialogueAct('inform', [
DialogueActItem(
self.requestable_slots[
action_enc - len(self.dstc2_acts_usr) -
len(self.requestable_slots)], Operator.EQ, '')
])
]
def test_dialogue_act(self):
self.assertEqual(
str(DialogueAct('intent', [DialogueActItem('slot', Operator.EQ, 'value')])),
'intent(slot = value, )'
)
def next_action(self, state):
"""
Consult the dialogue policy and produce the agent's response
:param state: the current dialogue state
:return: a list of actions, representing the agent's response
"""
sys_acts = []
state_enc = self.encode_state(state)
if state not in self.policy:
# TODO: Reactive dialogue policy. Fix this properly.
state_enc = ''
if state.user_acts:
for sa in state.user_acts:
state_enc = sa.intent
# This is due to the DM rules and to be fair to the other
# policies
if sa.intent == 'offer':
state_enc += '_name'
elif sa.params:
state_enc += '_' + sa.params[0].slot
state_enc += ';'
state_enc = state_enc[:-1]
if state_enc in self.policy:
sys_actions = list(self.policy[state_enc]['dacts'].keys())
probs = [self.policy[state_enc]['dacts'][i] for i in sys_actions]
sys_act_slots = \
deepcopy(random.choices(
sys_actions, weights=probs)[0]).split(';')
for sys_act_slot in sys_act_slots:
if not sys_act_slot:
# Skip empty sys_act_slot strings (e.g. case where
# there is ; at the end: inform_food;inform_area;)
continue
sys_act = DialogueAct('UNK')
sys_act_slot_parts = sys_act_slot.split('_')
sys_act.intent = sys_act_slot_parts[0]
if len(sys_act_slot_parts) > 1:
sys_act.params = \
[DialogueActItem(
sys_act_slot_parts[1],
Operator.EQ, '')]
if sys_act.intent == 'offer':
sys_act.params = []
elif sys_act.intent == 'canthelp.exception':
sys_act.intent = 'canthelp'
sys_acts.append(sys_act)
else:
print(f'Warning! {self.agent_role} Calculated dialogue policy: '
f'state not found, selecting random action.')
sys_act = DialogueAct('UNK')
if self.agent_role == 'system':
sys_act.intent = \
random.choice(['welcomemsg', 'inform', 'request'])
elif self.agent_role == 'user':
sys_act.intent = random.choice(['hello', 'inform', 'request'])
else:
sys_act.intent = random.choice(['bye', 'inform', 'request'])
sys_acts.append(sys_act)
return sys_acts
def process_input(self, utterance, dialogue_state=None):
"""
Query the Ludwig model with the given utterance to obtain predictions
for IOB tags and intent. Then parse the results and package them into
a list of dialogue Acts.
:param utterance: a string, the utterance to be recognised
:param dialogue_state: the current dialogue state, if available
:return: a list of dialogue acts containing the recognised intents
"""
# Pre-process utterance
utterance = utterance.rstrip().lower()
utterance = utterance.translate(self.punctuation_remover)
# Warning: Make sure the same tokenizer that was used to train the
# model is used during prediction
result = \
self.model.predict(
pd.DataFrame(
data={'transcript': [utterance]}), return_type=dict)
dacts = []
# Only keep the first act for now
last_sys_act = dialogue_state.last_sys_acts[0] \
if dialogue_state and dialogue_state.last_sys_acts else None
utterance_parts = utterance.split(' ')
iob_tags = [tag for tag in result['iob']['predictions'][0]]
for intent in result['intent']['predictions'][0]:
intent_parts = intent.split('_')
intent = intent_parts[0]
if intent == 'request' and len(intent_parts) > 1:
dacts.append(
DialogueAct(
intent,
[DialogueActItem(intent_parts[1], Operator.EQ, '')]))
elif intent == 'dontcare' and len(intent_parts) > 1:
if intent_parts[1] == 'this':
if dialogue_state and last_sys_act and last_sys_act.params:
dacts.append(
DialogueAct('inform', [
DialogueActItem(last_sys_act.params[0].slot,
Operator.EQ, 'dontcare')
]))
else:
dacts.append(
DialogueAct('inform', [
DialogueActItem(intent_parts[1], Operator.EQ,
'dontcare')
]))
# Exclude acts that take slot-value arguments
# (will be handled below)
elif intent not in ['inform', 'confirm', 'offer']:
dacts.append(DialogueAct(intent, []))
# If no act was recognised
if not dacts:
# Search for tags
intent = ''
slot = ''
value = ''
# iob_tags is a fixed length but the utterance may be shorter.
for t in range(len(utterance_parts)):
# If however we encounter input longer than the IOB tags,
# we can't handle it.
if t >= len(iob_tags):
print('Warning! camrest_nlu cannot handle such a long '
'sequence. Returning partial result.')
break
if iob_tags[t][0] == 'B':
# Case where we have B-slot1 I-slot1 I-slot1 B-slot2 ...
if value:
# Correct offer / inform mis-prediction
if slot == 'name':
dacts.append(
DialogueAct('offer', [
DialogueActItem(slot, Operator.EQ, value)
]))
else:
dacts.append(
DialogueAct('inform', [
DialogueActItem(slot, Operator.EQ, value)
]))
else:
tag_parts = iob_tags[t].split('-')
intent = tag_parts[1]
slot = tag_parts[2]
value = utterance_parts[t]
elif iob_tags[t][0] == 'I':
# Case where nlu doesn't work perfectly well and the first
# tag is I-... instead of B-...
if not value or not slot or not intent:
tag_parts = iob_tags[t].split('-')
intent = tag_parts[1]
slot = tag_parts[2]
value = utterance_parts[t]
value += ' ' + utterance_parts[t]
elif iob_tags[t] == 'O' and value:
if slot == 'name':
dacts.append(
DialogueAct(
'offer',
[DialogueActItem(slot, Operator.EQ, value)]))
else:
dacts.append(
DialogueAct(
'inform',
[DialogueActItem(slot, Operator.EQ, value)]))
# Reset intent, slot, and value
intent = ''
slot = ''
value = ''
# Special case for when a tag is at the end of the utterance
if value and intent:
# Save the recognised slot value
dacts.append(
DialogueAct(intent,
[DialogueActItem(slot, Operator.EQ, value)]))
# If still no act is recognised
if not dacts:
print('WARNING! camrest_nlu did not understand slots or values '
'for utterance: {0}\n'.format(utterance))
return dacts
def next_action(self, dialogue_state):
"""
Generate a response given which conditions are met by the current
dialogue state.
:param dialogue_state:
:return:
"""
# Check for terminal state
if dialogue_state.is_terminal_state:
return [DialogueAct('bye', [DialogueActItem('', Operator.EQ, '')])]
# Check if the user has made any requests
elif dialogue_state.requested_slot:
if dialogue_state.item_in_focus and \
dialogue_state.system_made_offer:
requested_slot = dialogue_state.requested_slot
# Reset request as we attempt to address it
dialogue_state.requested_slot = ''
value = 'not available'
if requested_slot in dialogue_state.item_in_focus and \
dialogue_state.item_in_focus[requested_slot]:
value = dialogue_state.item_in_focus[requested_slot]
return \
[DialogueAct(
'inform',
[DialogueActItem(requested_slot, Operator.EQ, value)])]
# Else, if no item is in focus or no offer has been made,
# ignore the user's request
# Try to fill slots
requestable_slots = \
deepcopy(self.ontology.ontology['system_requestable'])
if not hasattr(dialogue_state, 'requestable_slot_entropies') or \
not dialogue_state.requestable_slot_entropies:
slot = random.choice(requestable_slots)
while dialogue_state.slots_filled[slot] and \
len(requestable_slots) > 1:
requestable_slots.remove(slot)
slot = random.choice(requestable_slots)
else:
slot = ''
slots = \
[k for k, v in
dialogue_state.requestable_slot_entropies.items()
if v == max(
dialogue_state.requestable_slot_entropies.values())
and v > 0 and k in requestable_slots]
if slots:
slot = random.choice(slots)
while dialogue_state.slots_filled[slot] \
and dialogue_state.requestable_slot_entropies[
slot] > 0 \
and len(requestable_slots) > 1:
requestable_slots.remove(slot)
slots = \
[k for k, v in
dialogue_state.requestable_slot_entropies.items()
if v == max(
dialogue_state.requestable_slot_entropies.values())
and k in requestable_slots]
if slots:
slot = random.choice(slots)
else:
break
if slot and not dialogue_state.slots_filled[slot]:
return [DialogueAct(
'request',
[DialogueActItem(slot, Operator.EQ, '')])]
elif dialogue_state.item_in_focus:
name = dialogue_state.item_in_focus['name'] \
if 'name' in dialogue_state.item_in_focus \
else 'unknown'
dacts = [DialogueAct(
'offer',
[DialogueActItem('name', Operator.EQ, name)])]
for slot in dialogue_state.slots_filled:
if slot != 'requested' and dialogue_state.slots_filled[slot]:
if slot in dialogue_state.item_in_focus:
if slot not in ['id', 'name']:
dacts.append(
DialogueAct(
'inform',
[DialogueActItem(
slot,
Operator.EQ,
dialogue_state.item_in_focus[slot])]))
else:
dacts.append(DialogueAct(
'inform',
[DialogueActItem(
slot,
Operator.EQ,
'no info')]))
return dacts
else:
# Fallback action - cannot help!
# Note: We can have this check (no item in focus) at the beginning,
# but this would assume that the system
# queried a database before coming in here.
return [DialogueAct('canthelp', [])]
def process_input(self, utterance, dialogue_state=None):
"""
Process the utterance and see if any intent pattern matches.
:param utterance: a string, the utterance to be recognised
:param dialogue_state: the current dialogue state, if available
:return: a list of recognised dialogue acts
"""
dacts = []
dact = DialogueAct('UNK', [])
# TODO: Remove this once nlg is updated
utterance = utterance.replace('<PAD>', '')
if not utterance:
return [dact]
last_sys_act = \
dialogue_state.last_sys_acts[0] \
if dialogue_state and dialogue_state.last_sys_acts else None
utterance = utterance.rstrip().lower()
utterance = utterance.translate(self.punctuation_remover)
# Replace synonyms
utterance = utterance.replace('location', 'area')
utterance = utterance.replace('part of town', 'area')
utterance = utterance.replace('center', 'centre')
utterance = utterance.replace('cheaply', 'cheap')
utterance = utterance.replace('moderately', 'moderate')
utterance = utterance.replace('expensively', 'expensive')
utterance = utterance.replace('address', 'addr')
utterance = utterance.replace('telephone', 'phone')
utterance = utterance.replace('postal code', 'postcode')
utterance = utterance.replace('post code', 'postcode')
utterance = utterance.replace('zip code', 'postcode')
utterance = utterance.replace('price range', 'pricerange')
# First check if the user doesn't care
if last_sys_act and last_sys_act.intent in ['request', 'expl-conf']:
for p in self.dontcare_pattern:
# Look for exact matches here only (i.e. user just says
# 'i don't care')
if p == utterance:
dact.intent = 'inform'
dact.params.append(
DialogueActItem(
last_sys_act.params[0].slot,
Operator.EQ,
'dontcare'))
return [dact]
# Look for slot keyword and corresponding value
words = utterance.split(' ')
for p in self.ack_pattern:
if p == utterance:
dact.intent = 'ack'
break
for p in self.deny_pattern:
if p == utterance:
dact.intent = 'deny'
break
for p in self.affirm_pattern:
if p == utterance:
dact.intent = 'affirm'
break
# Check for dialogue ending
for p in self.bye_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'bye'
break
# Search for 'welcome' first because it may contain 'hello'
if dact.intent == 'UNK':
for p in self.welcome_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'welcomemsg'
break
if dact.intent == 'UNK':
for p in self.hi_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'hello'
break
if dact.intent == 'UNK':
for p in self.reqalts_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'reqalts'
break
if dact.intent == 'UNK':
for p in self.reqmore_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'reqmore'
break
if dact.intent == 'UNK':
for p in self.repeat_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'repeat'
break
if dact.intent == 'UNK':
for p in self.restart_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'restart'
break
if dact.intent == 'UNK':
for p in self.thankyou_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'thankyou'
break
if dact.intent == 'UNK':
for p in self.request_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'request'
break
if dact.intent == 'UNK':
for p in self.select_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'select'
break
if dact.intent == 'UNK':
for p in self.confirm_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'confirm'
break
if dact.intent == 'UNK':
for p in self.expl_conf_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'expl-conf'
break
if dact.intent == 'UNK':
for p in self.cant_help_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'canthelp'
dact.params = []
return [dact]
if dact.intent == 'UNK':
dact.intent = 'inform'
# Check if there is no information about the slot
if 'no info' in utterance:
# Search for a slot name in the utterance
for slot in self.ontology.ontology['requestable']:
if slot in utterance:
dact.params.append(
DialogueActItem(slot, Operator.EQ, 'no info'))
return [dact]
# Else try to grab slot name from the other agent's
# previous act
if last_sys_act and \
last_sys_act.intent in ['request', 'expl-conf']:
dact.params.append(
DialogueActItem(
last_sys_act.params[0].slot,
Operator.EQ,
'dontcare'))
return [dact]
# Else do nothing, and see if anything matches below
if dact.intent in ['inform', 'request']:
for word in words:
# Check for requests. Requests for informable slots are
# captured below
if word in self.requestable_only_slots:
if dact.intent == 'request':
dact.params.append(
DialogueActItem(word, Operator.EQ, ''))
break
elif word != 'name':
if 'is' not in utterance and 'its' not in utterance:
dact.intent = 'request'
dact.params.append(
DialogueActItem(word, Operator.EQ, ''))
break
# For any other kind of intent, we have no way of
# determining the slot's value, since such
# information is not in the ontology.
# Check for informs (most intensive)
if word in self.ontology.ontology['informable']:
# If a request intent has already been recognized,
# do not search for slot values
if dact.intent == 'request':
dact.params.append(
DialogueActItem(word, Operator.EQ, ''))
break
found = False
for p in self.ontology.ontology['informable'][word]:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
if word == 'name':
dact.intent = 'offer'
else:
dact.intent = 'inform'
dact.params.append(
DialogueActItem(word, Operator.EQ, p))
found = True
break
if not found:
# Search for dontcare (e.g. I want any area)
for p in self.dontcare_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.intent = 'inform'
dact.params.append(
DialogueActItem(
word,
Operator.EQ,
'dontcare'))
return [dact]
dact.intent = 'request'
dact.params.append(
DialogueActItem(word, Operator.EQ, ''))
# If nothing was recognised, do an even more brute-force search
if dact.intent in ['UNK', 'inform'] and not dact.params:
slot_vals = self.ontology.ontology['informable']
if self.slot_values:
slot_vals = self.slot_values
for slot in slot_vals:
for value in slot_vals[slot]:
if value and \
value.lower().translate(self.punctuation_remover) \
in utterance:
if slot == 'name':
dact.intent = 'offer'
di = DialogueActItem(slot, Operator.EQ, value)
if di not in dact.params:
dact.params.append(di)
# Check if something has been missed (e.g. utterance is dont care and
# there's no previous sys act)
if dact.intent == 'inform':
# Check to see that all slots have an identified value
if dact.params:
for dact_item in dact.params:
if not dact_item.slot or not dact_item.value:
dact.params.remove(dact_item)
if not dact.params:
dact.intent = 'UNK'
break
# Else, break up the inform into several acts
elif dact_item.slot == 'name':
dacts.append(DialogueAct('offer', [dact_item]))
else:
dacts.append(DialogueAct('inform', [dact_item]))
else:
# Try to see if the utterance refers to a slot in the
# requestable ones (e.g. 'I prefer any phone')
for slot in self.ontology.ontology['requestable']:
if slot in utterance:
# We can only handle 'dontcare' kind of values here,
# as we do not know values of req. slots.
for p in self.dontcare_pattern:
match = re.search(r'\b{0}\b'.format(p), utterance)
if match:
dact.params = \
[DialogueActItem(
slot,
Operator.EQ,
'dontcare')]
dact.intent = 'UNK'
else:
dacts.append(dact)
return dacts
def start_dialogue(self, args=None):
"""
Perform initial dialogue turn.
:param args: optional args
:return:
"""
self.dialogue_turn = 0
sys_utterance = ''
if self.USE_USR_SIMULATOR:
self.user_simulator.initialize(self.user_simulator_args)
print('DEBUG > usr goal:')
print(self.user_simulator.goal)
self.dialogue_manager.restart({})
if not self.USER_HAS_INITIATIVE:
# sys_response = self.dialogue_manager.respond()
sys_response = [DialogueAct('welcomemsg', [])]
if self.USE_NLG:
sys_utterance = self.nlg.generate_output(
{'dacts': sys_response}
)
print('SYSTEM > %s ' % sys_utterance)
if self.USE_SPEECH:
try:
tts = gTTS(sys_utterance)
tts.save('sys_output.mp3')
os.system('afplay sys_output.mp3')
except Exception as e:
print(
'WARNING: gTTS encountered an error: {0}. '
'Falling back to sys TTS.'.format(e)
)
os.system('say ' + sys_utterance)
else:
print(
'SYSTEM > %s ' % '; '.
join([str(sr) for sr in sys_response])
)
if self.USE_USR_SIMULATOR:
usim_input = sys_response
if self.USER_SIMULATOR_NLU and self.USE_NLG:
usim_input = self.user_simulator.nlu.process_input(
sys_utterance
)
self.user_simulator.receive_input(usim_input)
rew, success, task_success = self.reward_func.calculate(
self.dialogue_manager.get_state(),
sys_response,
self.user_simulator.goal
)
else:
rew, success, task_success = 0, None, None
self.recorder.record(
deepcopy(self.dialogue_manager.get_state()),
self.dialogue_manager.get_state(),
sys_response,
rew,
success,
task_success,
output_utterance=sys_utterance
)
self.dialogue_turn += 1
self.prev_state = None
# Re-initialize these for good measure
self.curr_state = None
self.prev_usr_utterance = None
self.prev_sys_utterance = None
self.prev_action = None
self.prev_reward = None
self.prev_success = None
self.prev_task_success = None
self.continue_dialogue()
def receive_input_policy(self, system_acts):
"""
Handle the input according to a dialogue_policy
:param system_acts: a list with the system's dialogue acts
:return: Nothing
"""
if self.prev_system_acts and self.prev_system_acts == system_acts:
self.curr_patience -= 1
else:
self.curr_patience = self.patience
self.prev_system_acts = deepcopy(system_acts)
for system_act in system_acts:
# 'bye' doesn't seem to appear in the CamRest data
if system_act.intent == 'bye' or self.curr_patience == 0 or \
self.dialogue_turn > 15:
self.agenda.push(DialogueAct('bye', []))
return
sys_act_slot = 'inform' if system_act.intent == 'offer' else \
system_act.intent
if system_act.params and system_act.params[0].slot:
sys_act_slot += '_' + system_act.params[0].slot
# Attempt to recover
if sys_act_slot not in self.policy:
if sys_act_slot == 'inform_name':
sys_act_slot = 'offer_name'
if sys_act_slot not in self.policy:
if system_act.intent == 'inform' and system_act.params and \
system_act.params[0].slot in self.goal.constraints:
user_act_slots = ['inform_' + system_act.params[0].slot]
else:
print('Warning! ABUS policy does not know what to do for'
' %s' % sys_act_slot)
return
else:
dacts = list(self.policy[sys_act_slot]['dacts'].keys())
probs = [self.policy[sys_act_slot]['dacts'][i] for i in dacts]
user_act_slots = random.choices(dacts, weights=probs)
for user_act_slot in user_act_slots:
intent, slot = user_act_slot.split('_')
if slot == 'this' and system_act.params and \
system_act.params[0].slot:
slot = system_act.params[0].slot
value = ''
if intent == 'inform':
if slot in self.goal.constraints:
value = self.goal.constraints[slot].value
else:
value = 'dontcare'
dact = \
DialogueAct(intent,
[DialogueActItem(slot, Operator.EQ, value)])
self.agenda.remove(dact)
self.agenda.push(dact)
def generate_output(self, args=None):
"""
Consult the current policy to generate a response.
:return: List of DialogueAct representing the system's output.
"""
d_state = self.DSTracker.get_state()
sys_acts = self.policy.next_action(d_state)
# Copy the sys_acts to be able to iterate over all sys_acts while also
# replacing some acts
sys_acts_copy = deepcopy(sys_acts)
new_sys_acts = []
# Safeguards to support policies that make decisions on intents only
# (i.e. do not output slots or values)
for sys_act in sys_acts:
if sys_act.intent == 'canthelp' and not sys_act.params:
slots = \
[
s for s in d_state.slots_filled if
d_state.slots_filled[s]
]
if slots:
slot = random.choice(slots)
# Remove the empty canthelp
sys_acts_copy.remove(sys_act)
new_sys_acts.append(
DialogueAct(
'canthelp',
[DialogueActItem(
slot,
Operator.EQ,
d_state.slots_filled[slot])]))
else:
print('DialogueManager Warning! No slot provided by '
'policy for canthelp and cannot find a reasonable '
'one!')
if sys_act.intent == 'offer' and not sys_act.params:
# Remove the empty offer
sys_acts_copy.remove(sys_act)
if d_state.item_in_focus:
new_sys_acts.append(
DialogueAct(
'offer',
[DialogueActItem(
'name',
Operator.EQ,
d_state.item_in_focus['name'])]))
# Only add these slots if no other acts were output
# by the DM
if len(sys_acts) == 1:
for slot in d_state.slots_filled:
if slot in d_state.item_in_focus:
if slot not in ['id', 'name'] and \
slot != d_state.requested_slot:
new_sys_acts.append(
DialogueAct(
'inform',
[DialogueActItem(
slot,
Operator.EQ,
d_state.item_in_focus[slot])]))
else:
new_sys_acts.append(
DialogueAct(
'inform',
[DialogueActItem(
slot,
Operator.EQ,
'no info')]))
elif sys_act.intent == 'inform':
if self.agent_role == 'system':
if sys_act.params and sys_act.params[0].value:
continue
if sys_act.params:
slot = sys_act.params[0].slot
else:
slot = d_state.requested_slot
if not slot:
slot = random.choice(list(d_state.slots_filled.keys()))
if d_state.item_in_focus:
if slot not in d_state.item_in_focus or \
not d_state.item_in_focus[slot]:
new_sys_acts.append(
DialogueAct(
'inform',
[DialogueActItem(
slot,
Operator.EQ,
'no info')]))
else:
if slot == 'name':
new_sys_acts.append(
DialogueAct(
'offer',
[DialogueActItem(
slot,
Operator.EQ,
d_state.item_in_focus[slot])]))
else:
new_sys_acts.append(
DialogueAct(
'inform',
[DialogueActItem(
slot,
Operator.EQ,
d_state.item_in_focus[slot])]))
else:
new_sys_acts.append(
DialogueAct(
'inform',
[DialogueActItem(
slot,
Operator.EQ,
'no info')]))
elif self.agent_role == 'user':
if sys_act.params:
slot = sys_act.params[0].slot
# Do nothing if the slot is already filled
if sys_act.params[0].value:
continue
elif d_state.last_sys_acts and d_state.user_acts and \
d_state.user_acts[0].intent == 'request':
slot = d_state.user_acts[0].params[0].slot
else:
slot = \
random.choice(
list(d_state.user_goal.constraints.keys()))
# Populate the inform with a slot from the user goal
if d_state.user_goal:
# Look for the slot in the user goal
if slot in d_state.user_goal.constraints:
value = d_state.user_goal.constraints[slot].value
else:
value = 'dontcare'
new_sys_acts.append(
DialogueAct(
'inform',
[DialogueActItem(
slot,
Operator.EQ,
value)]))
# Remove the empty inform
sys_acts_copy.remove(sys_act)
elif sys_act.intent == 'request':
# If the policy did not select a slot
if not sys_act.params:
found = False
if self.agent_role == 'system':
# Select unfilled slot
for slot in d_state.slots_filled:
if not d_state.slots_filled[slot]:
found = True
new_sys_acts.append(
DialogueAct(
'request',
[DialogueActItem(
slot,
Operator.EQ,
'')]))
break
elif self.agent_role == 'user':
# Select request from goal
if d_state.user_goal:
for req in d_state.user_goal.requests:
if not d_state.user_goal.requests[req].value:
found = True
new_sys_acts.append(
DialogueAct(
'request',
[DialogueActItem(
req,
Operator.EQ,
'')]))
break
if not found:
# All slots are filled
new_sys_acts.append(
DialogueAct(
'request',
[DialogueActItem(
random.choice(
list(
d_state.slots_filled.keys())[:-1]),
Operator.EQ, '')]))
# Remove the empty request
sys_acts_copy.remove(sys_act)
# Append unique new sys acts
for sa in new_sys_acts:
if sa not in sys_acts_copy:
sys_acts_copy.append(sa)
self.DSTracker.update_state_sysact(sys_acts_copy)
return sys_acts_copy
def receive_input_handcrafted(self, system_acts):
"""
Handle the input according to probabilistic rules
:param system_acts: a list with the system's dialogue acts
:return: Nothing
"""
# TODO: Revise these rules wrt other operators (i.e. not only EQ)
if self.prev_system_acts and self.prev_system_acts == system_acts:
self.curr_patience -= 1
else:
self.curr_patience = self.patience
self.prev_system_acts = deepcopy(system_acts)
for system_act in system_acts:
# Update user goal (in ABUS the state is factored into the goal
# and the agenda)
if system_act.intent == 'bye' or self.dialogue_turn > 15:
self.agenda.clear()
self.agenda.push(DialogueAct('bye', []))
elif system_act.intent in ['inform', 'offer']:
# Check that the venue provided meets the constraints
meets_constraints = True
for item in system_act.params:
if item.slot in self.goal.constraints and \
self.goal.constraints[item.slot].value != \
'dontcare':
# Remove the inform from the agenda, assuming the
# value provided is correct. If it is not, the
# act will be pushed again and will be on top of the
# agenda (this way we avoid adding / removing
# twice.
dact = \
DialogueAct(
'inform',
[DialogueActItem(
deepcopy(item.slot),
deepcopy(
self.goal.constraints[item.slot].op),
deepcopy(
self.goal.constraints[
item.slot].value))])
# Remove and push to make sure the act is on top -
# if it already exists
self.agenda.remove(dact)
if item.value != \
self.goal.constraints[item.slot].value:
meets_constraints = False
# For each violated constraint add an inform
# TODO: Make this a deny-inform or change
# operator to NE
self.agenda.push(dact)
# If it meets the constraints, update the requests
if meets_constraints:
for item in system_act.params:
if item.slot in self.goal.actual_requests:
self.goal.actual_requests[item.slot].value = \
item.value
# Mark the value only if the slot has been
# requested and is in the requests
if item.slot in self.goal.requests:
self.goal.requests[item.slot].value = \
item.value
# Remove any requests from the agenda that ask
# for that slot
# TODO: Revise this for all operators
self.agenda.remove(
DialogueAct('request', [
DialogueActItem(item.slot, Operator.EQ, '')
]))
# When the system makes a new offer, replace all requests in
# the agenda
if system_act.intent == 'offer':
for r in self.goal.requests:
req = deepcopy(self.goal.requests[r])
req_dact = DialogueAct('request', [req])
# The agenda will replace the old act first
self.agenda.push(req_dact)
# Push appropriate acts into the agenda
elif system_act.intent == 'request':
if system_act.params:
for item in system_act.params:
if item.slot in self.goal.constraints:
self.agenda.push(
DialogueAct('inform', [
DialogueActItem(
deepcopy(item.slot),
deepcopy(self.goal.constraints[
item.slot].op),
deepcopy(self.goal.constraints[
item.slot].value))
]))
else:
self.agenda.push(
DialogueAct('inform', [
DialogueActItem(deepcopy(item.slot),
Operator.EQ, 'dontcare')
]))
