Techniques are described related to turn-based reinforcement learning for dialog management. In various implementations, dialog states and corresponding responsive actions generated during a multi-turn human-to-computer dialog session may be obtained. A plurality of turn-level training instances may be generated, each including: a given dialog state of the plurality of dialog states at an outset of a given turn of the human-to-computer dialog session; and a given responsive action that was selected based on the given dialog state. One or more of the turn-level training instances may further include a turn-level feedback value that reflects on the given responsive action selected during the given turn. A reward value may be generated based on an outcome of the human-to-computer dialog session. The dialog management policy model may be trained based on turn-level feedback values of the turn-level training instance(s) and the reward value.

Turn-based reinforcement learning for dialog management

The present teaching relates to method, system, medium, and implementations for user machine dialogue. An instruction is received by an agent device for rendering a communication directed to a user involved in a dialogue in a dialogue scene and is used to render the communication. A first representation of a mindset of the agent is updated accordingly after the rendering. Input data are received in one or more media types capturing a response from the user and information surrounding the dialogue scene and a second representation of a mindset of the user is updated based on the response from the user and the information surrounding of the dialogue scene. A next communication to the user is then determined based on the first representation of the mindset of the agent and the second representation of the mindset of the user.

System and method for dialogue management

A first request to establish a communication session with a contact center is received from a user communication device. For example, a request to establish a voice communication session is received at the contact center. A machine learning model uses prior communication sessions with the contact center to identify a first set of one or more sensors that are external to the user communication device. External sensor information is received (e.g., during the request to establish the communication session) by the contact center. The external sensor information is from the first set of one or more sensors that are external to the user communication device. The communication session in the contact center is routed based on the received external sensor information. For example, instead of the communication session being routed to an Interactive Voice Response (IVR) system, the communication session is routed to a high priority contact center queue.

System and method for providing enhanced routing in a contact center

Procedure dialogs are improved through knowledge mining within a reinforcement learning framework. Taking an existing procedure dialog as input, a machine learning model is generated. User interactions with the machine learning model are monitored and used to update the machine learning model. The updates to the machine learning model are applied to the existing procedure dialog for review and revision by subject matter experts.

Procedure dialogs using reinforcement learning

A computer-implemented method, computer program product, and computer processing system are provided for generating inferences from a forest of predefined problem determination trees using a processor-based conversation platform. The method includes selecting a tree from among the forest of predefined problem determination trees, responsive to user utterances uttered during an inference generating session. The method further includes navigating the tree to allocate a relevant tree node to generate a problem diagnosis question or a problem resolution action by understanding the user utterances among common interaction patterns in problem diagnosis and problem resolution dialogs. The method also includes providing speech for uttering the problem diagnosis question or the problem resolution action to a user.

Jie Ma

Papers

Procedure dialogs using reinforcement learning

Forest inference engine on conversation platform