interaction_model
=================

.. py:module:: interaction_model

.. autoapi-nested-parse::

   Interaction model.

   The interaction model is responsible for defining the allowed transitions
   between dialogue acts based on their intents and updating the agenda.



Attributes
----------

.. autoapisummary::

   interaction_model.logger


Classes
-------

.. autoapisummary::

   interaction_model.InteractionModel


Module Contents
---------------

.. py:data:: logger

.. py:class:: InteractionModel(config_file: str, domain: usersimcrs.core.simulation_domain.SimulationDomain, annotated_conversations: List[dialoguekit.core.dialogue.Dialogue])

   Initializes the interaction model.

   :param config_file: Path to configuration file.
   :param domain: Simulation domain.
   :param annotated_conversations: Annotated conversations.


   .. py:attribute:: REQUIRED_INTENTS


   .. py:method:: initialize_transition_matrices(annotated_conversations: List[dialoguekit.core.dialogue.Dialogue]) -> Tuple[pandas.DataFrame, pandas.DataFrame]

      Initializes transition matrices from annotated conversations.

      We consider two transition matrices. The first one uses single intents
      as states, while the second one uses compound intents. For example:
      Dialogue acts in utt. 1: [GREETING(), REQUEST(genre=?, year=?)]
      Dialogue acts in utt. 2: [INFORM(genre=action, year=2024)]

      The single intent transition matrix will be:
      GREETING -> INFORM : 1
      REQUEST -> INFORM : 1
      The compound intent transition matrix will be:
      GREETING_REQUEST -> INFORM : 1

      Note that the compound intent may also include single intents, in case
      an utterance has a single dialogue act.

      :param annotated_conversations: Annotated conversations.

      :returns: Transition matrices.



   .. py:method:: initialize_agenda(information_need: usersimcrs.core.information_need.InformationNeed)

      Initializes user agenda.

      :param information_need: Information need.



   .. py:method:: is_agent_intent_elicit(agent_intent: dialoguekit.core.intent.Intent) -> bool

      Checks if the given agent intent is elicitation.

      :param agent_intent: Agent's intent.

      :returns: True if it is an elicitation intent.



   .. py:method:: is_agent_intent_set_retrieval(agent_intent: dialoguekit.core.intent.Intent) -> bool

      Checks if the given agent intent is set retrieval.

      :param agent_intent: Agent's intent.

      :returns: True if it is a set retrieval intent.



   .. py:method:: is_agent_intent_inquire(agent_intent: dialoguekit.core.intent.Intent) -> bool

      Checks if the given agent intent is inquiry.

      :param agent_intent: Agent's intent.

      :returns: True if it is an inquiry intent.



   .. py:method:: get_next_dialogue_acts(n: int = 1) -> List[dialoguekit.core.dialogue_act.DialogueAct]

      Returns the next n dialogue acts from the stack.

      :param n: Number of dialogue acts to return. Defaults to 1.

      :returns: List of dialogue acts.



   .. py:method:: update_agenda(information_need: usersimcrs.core.information_need.InformationNeed, preference_model: usersimcrs.user_modeling.preference_model.PreferenceModel, item_collection: usersimcrs.items.item_collection.ItemCollection) -> None

      Updates the agenda based on the last agent dialogue acts and state.

      Each agent dialogue act results in a push operation on the stack. We
      consider four cases: the agent elicits, recommends, inquires, or
      neither. Once the push operations are done, we clean the stack.

      :param information_need: Information need.