Abstract: The present disclosure relates to a method for a dialog system. The method comprises: receiving a user input at the dialog system. A machine learning module may receive from the dialog system a dialog system response to the user input. In response to determining that the machine learning module is in a deactivated mode, selected one or more training data items of the dialog system response and the user input may be used for training the machine learning module. In response to determining that the machine learning module is in an active mode the trained machine learning module may estimate from the set of output data items and the user input a machine learning module response to the user input. The machine learning module response or the dialog system response may be provided.

Abstract: A computer-implemented method includes determining a current state and a current context of an environment in which an automated agent runs to execute a contingent plan. The state indicates that one or more fluents of a plurality of fluents are true, and the plurality of fluents are associated with a contingent problem solved by the contingent plan. The context describes values corresponding to the one or more fluents. An action is performed with respect to at least a subset of the context. A nondeterministic effect of the action on the environment is evaluated, using a computer processor. The state is updated based on the nondeterministic effect. The context is updated based on the nondeterministic effect.

Abstract: The present disclosure relates to a method for a dialog system. The method comprises: receiving a user input at the dialog system. A machine learning module may receive from the dialog system a dialog system response to the user input. In response to determining that the machine learning module is in a deactivated mode, selected one or more training data items of the dialog system response and the user input may be used for training the machine learning module. In response to determining that the machine learning module is in an active mode the trained machine learning module may estimate from the set of output data items and the user input a machine learning module response to the user input. The machine learning module response or the dialog system response may be provided.

Abstract: A computer-implemented method includes determining a current state and a current context of an environment in which an automated agent runs to execute a contingent plan. The state indicates that one or more fluents of a plurality of fluents are true, and the plurality of fluents are associated with a contingent problem solved by the contingent plan. The context describes values corresponding to the one or more fluents. An action is performed with respect to at least a subset of the context. A nondeterministic effect of the action on the environment is evaluated, using a computer processor. The state is updated based on the nondeterministic effect. The context is updated based on the nondeterministic effect.