def __init__(self, configuration):
"""
Initialize the internal structures of this agent.
:param configuration: a dictionary representing the configuration file
"""
super(ConversationalSingleAgent, self).__init__()
self.configuration = configuration
# There is only one agent in this setting
self.agent_id = 0
# dialogue statistics
self.dialogue_episode = 0
self.dialogue_turn = 0
self.num_successful_dialogues = 0
self.num_task_success = 0
self.cumulative_rewards = 0
self.total_dialogue_turns = 0
# Default meta-parameter values
self.minibatch_length = 500
self.train_interval = 50
self.train_epochs = 3
# True values here would imply some default modules
self.USE_USR_SIMULATOR = False
self.USER_SIMULATOR_NLU = False
self.USER_SIMULATOR_NLG = False
self.USE_NLG = False
self.USE_SPEECH = False
self.USER_HAS_INITIATIVE = True
self.SAVE_LOG = True
self.SAVE_INTERVAL = 10000
# The dialogue will terminate after MAX_TURNS (this agent will issue
# a bye() dialogue act.
self.MAX_TURNS = 15
self.dialogue_turn = -1
self.ontology = None
self.database = None
self.domain = None
self.global_args = {}
self.dialogue_manager = None
self.user_model = None
self.user_simulator = None
self.user_simulator_args = {}
self.nlu = None
self.nlg = None
self.agent_role = None
self.agent_goal = None
self.goal_generator = None
self.curr_state = None
self.prev_state = None
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.user_model = UserModel()
self.recorder = DialogueEpisodeRecorder()
# TODO: Handle this properly - get reward function type from config
self.reward_func = SlotFillingReward()
# self.reward_func = SlotFillingGoalAdvancementReward()
if self.configuration:
# Error checks for options the config must have
if not self.configuration['GENERAL']:
raise ValueError('Cannot run Plato without GENERAL settings!')
elif not self.configuration['GENERAL']['interaction_mode']:
raise ValueError('Cannot run Plato without an '
'interaction mode!')
elif not self.configuration['DIALOGUE']:
raise ValueError('Cannot run Plato without DIALOGUE settings!')
elif not self.configuration['AGENT_0']:
raise ValueError('Cannot run Plato without at least '
'one agent!')
# dialogue domain self.settings
if 'DIALOGUE' in self.configuration and \
self.configuration['DIALOGUE']:
if 'initiative' in self.configuration['DIALOGUE']:
self.USER_HAS_INITIATIVE = bool(
self.configuration['DIALOGUE']['initiative'] == 'user'
)
self.user_simulator_args['us_has_initiative'] = \
self.USER_HAS_INITIATIVE
if self.configuration['DIALOGUE']['domain']:
self.domain = self.configuration['DIALOGUE']['domain']
if 'ontology_path' in self.configuration['DIALOGUE']:
if os.path.isfile(
self.configuration['DIALOGUE']['ontology_path']
):
self.ontology = ontology.Ontology(
self.configuration['DIALOGUE']['ontology_path']
)
else:
raise FileNotFoundError(
'domain file %s not found' %
self.configuration['DIALOGUE']['ontology_path'])
# Alternatively, look at global_arguments for ontology path
elif 'global_arguments' in self.configuration['GENERAL'] \
and 'ontology' in \
self.configuration['GENERAL']['global_arguments']:
if os.path.isfile(
self.configuration['GENERAL'][
'global_arguments']['ontology']
):
self.ontology = ontology.Ontology(
self.configuration['GENERAL'][
'global_arguments']['ontology']
)
else:
raise FileNotFoundError(
'domain file %s not found' %
self.configuration['GENERAL'][
'global_arguments']['ontology'])
if 'db_path' in self.configuration['DIALOGUE']:
if os.path.isfile(
self.configuration['DIALOGUE']['db_path']
):
if 'db_type' in self.configuration['DIALOGUE']:
if self.configuration['DIALOGUE']['db_type'] == \
'sql':
self.database = database.SQLDataBase(
self.configuration['DIALOGUE']['db_path']
)
else:
self.database = database.DataBase(
self.configuration['DIALOGUE']['db_path']
)
else:
# Default to SQL
self.database = database.SQLDataBase(
self.configuration['DIALOGUE']['db_path']
)
else:
raise FileNotFoundError(
'Database file %s not found' %
self.configuration['DIALOGUE']['db_path']
)
# Alternatively, look at global arguments for db path
elif 'global_arguments' in self.configuration['GENERAL'] \
and 'database' in \
self.configuration['GENERAL']['global_arguments']:
if os.path.isfile(
self.configuration['GENERAL'][
'global_arguments']['database']
):
self.database = database.DataBase(
self.configuration['GENERAL'][
'global_arguments']['database']
)
else:
raise FileNotFoundError(
'domain file %s not found' %
self.configuration['GENERAL'][
'global_arguments']['ontology'])
if 'goals_path' in self.configuration['DIALOGUE']:
if os.path.isfile(
self.configuration['DIALOGUE']['goals_path']
):
self.goals_path = \
self.configuration['DIALOGUE']['goals_path']
else:
raise FileNotFoundError(
'Goals file %s not found' %
self.configuration['DIALOGUE']['goals_path']
)
# General settings
if 'GENERAL' in self.configuration and \
self.configuration['GENERAL']:
if 'global_arguments' in self.configuration['GENERAL']:
self.global_args = \
self.configuration['GENERAL']['global_arguments']
if 'experience_logs' in self.configuration['GENERAL']:
dialogues_path = None
if 'path' in \
self.configuration['GENERAL']['experience_logs']:
dialogues_path = \
self.configuration['GENERAL'][
'experience_logs']['path']
if 'load' in \
self.configuration['GENERAL']['experience_logs'] \
and bool(self.configuration['GENERAL'][
'experience_logs']['load']):
if dialogues_path and os.path.isfile(dialogues_path):
self.recorder.load(dialogues_path)
else:
raise FileNotFoundError(
'dialogue Log file %s not found (did you '
'provide one?)' % dialogues_path)
if 'save' in \
self.configuration['GENERAL']['experience_logs']:
self.recorder.set_path(dialogues_path)
self.SAVE_LOG = bool(
self.configuration['GENERAL'][
'experience_logs']['save']
)
if self.configuration['GENERAL']['interaction_mode'] == \
'simulation':
self.USE_USR_SIMULATOR = True
elif self.configuration['GENERAL']['interaction_mode'] == \
'speech':
self.USE_SPEECH = True
self.asr = speech_rec.Recognizer()
# Agent Settings
# Retrieve agent role
if 'role' in self.configuration['AGENT_0']:
self.agent_role = self.configuration['AGENT_0']['role']
else:
raise ValueError(
'agent: No role assigned for agent {0} in '
'config!'.format(self.agent_id)
)
if self.agent_role == 'user':
if self.ontology and self.database:
self.goal_generator = GoalGenerator({
'ontology': self.ontology,
'database': self.database
})
else:
raise ValueError(
'Conversational Single Agent (user): Cannot generate '
'goal without ontology and database.'
)
# Retrieve agent parameters
if 'max_turns' in self.configuration['AGENT_0']:
self.MAX_TURNS = self.configuration['AGENT_0']['max_turns']
if 'train_interval' in self.configuration['AGENT_0']:
self.train_interval = \
self.configuration['AGENT_0']['train_interval']
if 'train_minibatch' in self.configuration['AGENT_0']:
self.minibatch_length = \
self.configuration['AGENT_0']['train_minibatch']
if 'train_epochs' in self.configuration['AGENT_0']:
self.train_epochs = \
self.configuration['AGENT_0']['train_epochs']
if 'save_interval' in self.configuration['AGENT_0']:
self.SAVE_INTERVAL = \
self.configuration['AGENT_0']['save_interval']
# usr Simulator
# Check for specific simulator self.settings, otherwise
# default to agenda
if 'USER_SIMULATOR' in self.configuration['AGENT_0']:
# Agent 0 simulator configuration
if 'package' in \
self.configuration['AGENT_0']['USER_SIMULATOR'] and \
'class' in \
self.configuration['AGENT_0']['USER_SIMULATOR']:
if 'arguments' in \
self.configuration['AGENT_0']['USER_SIMULATOR']:
self.user_simulator_args =\
self.configuration[
'AGENT_0']['USER_SIMULATOR']['arguments']
self.user_simulator_args.update(self.global_args)
self.user_simulator = \
ConversationalGenericAgent.load_module(
self.configuration['AGENT_0']['USER_SIMULATOR'][
'package'],
self.configuration['AGENT_0']['USER_SIMULATOR'][
'class'],
self.user_simulator_args
)
if hasattr(self.user_simulator, 'nlu'):
self.USER_SIMULATOR_NLU = self.user_simulator.nlu
if hasattr(self.user_simulator, 'nlg'):
self.USER_SIMULATOR_NLG = self.user_simulator.nlg
else:
# Fallback to agenda based simulator with default settings
self.user_simulator = AgendaBasedUS(
self.user_simulator_args
)
# NLU Settings
if 'NLU' in self.configuration['AGENT_0']:
nlu_args = {}
if 'package' in self.configuration['AGENT_0']['NLU'] and \
'class' in self.configuration['AGENT_0']['NLU']:
if 'arguments' in \
self.configuration['AGENT_0']['NLU']:
nlu_args = \
self.configuration['AGENT_0']['NLU']['arguments']
nlu_args.update(self.global_args)
self.nlu = \
ConversationalGenericAgent.load_module(
self.configuration['AGENT_0']['NLU'][
'package'],
self.configuration['AGENT_0']['NLU'][
'class'],
nlu_args
)
# DM Settings
if 'DM' in self.configuration['AGENT_0']:
dm_args = dict(
zip(
['settings', 'ontology', 'database', 'domain',
'agent_id',
'agent_role'],
[self.configuration,
self.ontology,
self.database,
self.domain,
self.agent_id,
self.agent_role
]
)
)
if 'package' in self.configuration['AGENT_0']['DM'] and \
'class' in self.configuration['AGENT_0']['DM']:
if 'arguments' in \
self.configuration['AGENT_0']['DM']:
dm_args.update(
self.configuration['AGENT_0']['DM']['arguments']
)
dm_args.update(self.global_args)
self.dialogue_manager = \
ConversationalGenericAgent.load_module(
self.configuration['AGENT_0']['DM'][
'package'],
self.configuration['AGENT_0']['DM'][
'class'],
dm_args
)
# NLG Settings
if 'NLG' in self.configuration['AGENT_0']:
nlg_args = {}
if 'package' in self.configuration['AGENT_0']['NLG'] and \
'class' in self.configuration['AGENT_0']['NLG']:
if 'arguments' in \
self.configuration['AGENT_0']['NLG']:
nlg_args = \
self.configuration['AGENT_0']['NLG']['arguments']
nlg_args.update(self.global_args)
self.nlg = \
ConversationalGenericAgent.load_module(
self.configuration['AGENT_0']['NLG'][
'package'],
self.configuration['AGENT_0']['NLG'][
'class'],
nlg_args
)
if self.nlg:
self.USE_NLG = True
# True if at least one module is training
self.IS_TRAINING = self.nlu and self.nlu.training or \
self.dialogue_manager and self.dialogue_manager.training or \
self.nlg and self.nlg.training
def __init__(self, configuration, agent_id):
"""
Initialize the internal structures of this agent.
:param configuration: a dictionary representing the configuration file
:param agent_id: an integer, this agent's id
"""
super(ConversationalMultiAgent, self).__init__()
self.agent_id = agent_id
# Flag to alternate training between roles
self.train_system = True
# dialogue statistics
self.dialogue_episode = 1
# Note that since this is a multi-agent setting, dialogue_turn refers
# to this agent's turns only
self.dialogue_turn = 0
self.num_successful_dialogues = 0
self.num_task_success = 0
self.cumulative_rewards = 0
self.total_dialogue_turns = 0
self.minibatch_length = 200
self.train_interval = 50
self.train_epochs = 3
self.global_args = {}
# Alternate training between the agents
self.train_alternate_training = True
self.train_switch_trainable_agents_every = self.train_interval
self.configuration = configuration
# True values here would imply some default modules
self.USE_NLU = False
self.USE_NLG = False
self.USE_SPEECH = False
self.USER_HAS_INITIATIVE = True
self.SAVE_LOG = True
self.SAVE_INTERVAL = 10000
# The dialogue will terminate after MAX_TURNS (this agent will issue
# a bye() dialogue act.
self.MAX_TURNS = 10
self.ontology = None
self.database = None
self.domain = None
self.dialogue_manager = None
self.user_model = None
self.nlu = None
self.nlg = None
self.agent_role = None
self.agent_goal = None
self.goal_generator = None
self.goals_path = None
self.prev_state = None
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.user_model = UserModel()
# The size of the experience pool is a hyperparameter.
# Do not have an experience window larger than the current batch,
# as past experience may not be relevant since both agents learn.
self.recorder = DialogueEpisodeRecorder(size=20000)
# TODO: Get reward type from the config
self.reward_func = SlotFillingReward()
if self.configuration:
ag_id_str = 'AGENT_' + str(self.agent_id)
# Error checks for options the config must have
if not self.configuration['GENERAL']:
raise ValueError('Cannot run Plato without GENERAL settings!')
elif not self.configuration['GENERAL']['interaction_mode']:
raise ValueError('Cannot run Plato without an interaction '
'mode!')
elif not self.configuration['DIALOGUE']:
raise ValueError('Cannot run Plato without DIALOGUE settings!')
elif not self.configuration[ag_id_str]:
raise ValueError('Cannot run Plato without at least '
'one agent!')
# General settings
if 'GENERAL' in self.configuration and \
self.configuration['GENERAL']:
if 'global_arguments' in self.configuration['GENERAL']:
self.global_args = \
self.configuration['GENERAL']['global_arguments']
if 'experience_logs' in self.configuration['GENERAL']:
dialogues_path = None
if 'path' in \
self.configuration['GENERAL']['experience_logs']:
dialogues_path = \
self.configuration['GENERAL'][
'experience_logs']['path']
if 'load' in \
self.configuration['GENERAL'][
'experience_logs'] and \
bool(
self.configuration[
'GENERAL'
]['experience_logs']['load']
):
if dialogues_path and os.path.isfile(dialogues_path):
self.recorder.load(dialogues_path)
else:
raise FileNotFoundError(
'dialogue Log file %s not found (did you '
'provide one?)' % dialogues_path
)
if 'save' in \
self.configuration['GENERAL']['experience_logs']:
self.recorder.set_path(dialogues_path)
self.SAVE_LOG = bool(
self.configuration['GENERAL'][
'experience_logs']['save']
)
# Retrieve agent role
if 'role' in self.configuration[ag_id_str]:
self.agent_role = self.configuration[ag_id_str][
'role']
else:
raise ValueError(
'agent: No role assigned for '
'agent {0} in '
'config!'.format(self.agent_id)
)
# Dialogue settings
if 'DIALOGUE' in self.configuration and \
self.configuration['DIALOGUE']:
if 'initiative' in self.configuration['DIALOGUE']:
self.USER_HAS_INITIATIVE = bool(
self.configuration['DIALOGUE']['initiative'] == 'user'
)
if 'domain' in self.configuration['DIALOGUE']:
self.domain = self.configuration['DIALOGUE']['domain']
elif 'domain' in self.global_args:
self.domain = self.global_args['domain']
ontology_path = None
if 'ontology_path' in self.configuration['DIALOGUE']:
ontology_path = \
self.configuration['DIALOGUE']['ontology_path']
elif 'ontology' in self.global_args:
ontology_path = self.global_args['ontology']
if ontology_path and os.path.isfile(ontology_path):
self.ontology = ontology.Ontology(ontology_path)
else:
raise FileNotFoundError(
'domain file %s not found' % ontology_path
)
db_path = None
if 'db_path' in self.configuration['DIALOGUE']:
db_path = self.configuration['DIALOGUE']['db_path']
elif 'database' in self.global_args:
db_path = self.global_args['database']
if db_path and os.path.isfile(db_path):
if db_path[-3:] == '.db':
self.database = database.SQLDataBase(db_path)
else:
self.database = database.DataBase(db_path)
else:
raise FileNotFoundError(
'Database file %s not found' % db_path
)
if 'goals_path' in self.configuration['DIALOGUE']:
if os.path.isfile(
self.configuration['DIALOGUE']['goals_path']
):
self.goals_path = \
self.configuration['DIALOGUE']['goals_path']
else:
raise FileNotFoundError(
'Goals file %s not '
'found' % self.configuration[
'DIALOGUE'
]['goals_path']
)
# Agent Settings
# Retrieve agent role
if 'role' in self.configuration[ag_id_str]:
self.agent_role = self.configuration[ag_id_str][
'role']
else:
raise ValueError(
'agent: No role assigned for agent {0} in '
'config!'.format(self.agent_id)
)
if self.agent_role == 'user':
if self.ontology and self.database:
self.goal_generator = GoalGenerator({
'ontology': self.ontology,
'database': self.database
})
else:
raise ValueError(
'Conversational Multi Agent (user): Cannot generate '
'goal without ontology and database.'
)
# Retrieve agent parameters
if 'max_turns' in self.configuration[ag_id_str]:
self.MAX_TURNS = self.configuration[ag_id_str][
'max_turns']
if 'train_interval' in self.configuration[ag_id_str]:
self.train_interval = \
self.configuration[ag_id_str]['train_interval']
if 'train_minibatch' in self.configuration[ag_id_str]:
self.minibatch_length = \
self.configuration[ag_id_str]['train_minibatch']
if 'train_epochs' in self.configuration[ag_id_str]:
self.train_epochs = \
self.configuration[ag_id_str]['train_epochs']
if 'save_interval' in self.configuration[ag_id_str]:
self.SAVE_INTERVAL = \
self.configuration[ag_id_str]['save_interval']
# NLU Settings
if 'NLU' in self.configuration[ag_id_str]:
nlu_args = {}
if 'package' in self.configuration[ag_id_str]['NLU'] and \
'class' in self.configuration[ag_id_str]['NLU']:
if 'arguments' in \
self.configuration[ag_id_str]['NLU']:
nlu_args = \
self.configuration[ag_id_str]['NLU'][
'arguments']
nlu_args.update(self.global_args)
self.nlu = \
ConversationalGenericAgent.load_module(
self.configuration[ag_id_str]['NLU'][
'package'],
self.configuration[ag_id_str]['NLU'][
'class'],
nlu_args
)
# DM Settings
if 'DM' in self.configuration[ag_id_str]:
dm_args = dict(
zip(
['settings', 'ontology', 'database',
'domain',
'agent_id',
'agent_role'],
[self.configuration,
self.ontology,
self.database,
self.domain,
self.agent_id,
self.agent_role
]
)
)
if 'package' in self.configuration[ag_id_str]['DM'] and \
'class' in self.configuration[ag_id_str]['DM']:
if 'arguments' in \
self.configuration[ag_id_str]['DM']:
dm_args.update(
self.configuration[ag_id_str]['DM'][
'arguments']
)
dm_args.update(self.global_args)
self.dialogue_manager = \
ConversationalGenericAgent.load_module(
self.configuration[ag_id_str]['DM'][
'package'],
self.configuration[ag_id_str]['DM'][
'class'],
dm_args
)
# NLG Settings
if 'NLG' in self.configuration[ag_id_str]:
nlg_args = {}
if 'package' in self.configuration[ag_id_str]['NLG'] and \
'class' in self.configuration[ag_id_str]['NLG']:
if 'arguments' in \
self.configuration[ag_id_str]['NLG']:
nlg_args = \
self.configuration[ag_id_str]['NLG'][
'arguments']
nlg_args.update(self.global_args)
self.nlg = \
ConversationalGenericAgent.load_module(
self.configuration[ag_id_str]['NLG'][
'package'],
self.configuration[ag_id_str]['NLG'][
'class'],
nlg_args
)
if self.nlg:
self.USE_NLG = True
# True if at least one module is training
self.IS_TRAINING = self.nlu and self.nlu.training or \
self.dialogue_manager and self.dialogue_manager.training or \
self.nlg and self.nlg.training