A computer-implemented technique is described herein for training a personal digital assistant (PDA) component and a simulated user (SU) component via a self-learning strategy. The technique involves conducting interactions between the PDA component and the SU component over the course of plural dialogs, and with respect to plural tasks. These interactions yield training data. A training system uses the training data to generate and update analysis components used by both the PDA component and the SU component. According to one illustrative aspect, the SU component is configured to mimic the behavior of actual users, across a range of different user types.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, including one or more computing devices, for use in training analysis components via a self-learning strategy, comprising: a personal digital assistant (PDA) component, the PDA component being implemented by one or more computing devices, and being configured to use one or more PDA-analysis components to at least interpret a user utterance and to select a dialog utterance in response to input parameter information; a simulated user (SU) component, the SU component being implemented by one or more computing devices and including an SU utterance-generating component that is configured to use an SU-analysis component to select a dialog utterance in response to input parameter information; a scenario-selecting component, implemented by one or more computing devices, configured to select a particular dialog scenario type from a set of possible dialog scenario types, the particular dialog scenario type pertaining, at least in part, to a particular task to be completed; the SU component and the PDA component being configured to conduct a particular dialog that includes an exchange of utterances between the SU component and the PDA component, the particular dialog pertaining to the particular dialog scenario type that has been selected; and a training system, implemented by one or more computing devices, the training system using a self-learning strategy to train the PDA component and the SU component, the training system including: a collection component configured to collect training data in course of the exchange of utterances; a PDA learning component configured to update the at least one of said one or more PDA-analysis components used by the PDA component, based on the training data; and an SU learning component configured to update the SU-analysis component used by the SU utterance-generating component, via reinforcement learning, based on the training data. 2. The system of claim 1, wherein the particular dialog scenario type specifies a particular type of user from among a set of possible types of users, wherein each user type pertains to users who exhibit a particular type of behavior when interacting with the PDA component. 3. The system of claim 1, wherein the SU reinforcement learning aims to achieve an objective that promotes at least: (a) efficient completion of tasks; and (b) dialog behavior that resembles actual user behavior exhibited by two or more user types. 4. The system of claim 1, wherein the PDA learning component is configured to perform learning that aims to achieve an objective that promotes at least efficient completion of tasks. 5. The system of claim 1, wherein the SU utterance-generating component uses a neural network that is configured based on weighting values specified by the SU-analysis component, and wherein the neural network is configured to map an input PDA utterance to an output SU utterance. 6. A method, implemented by one or more computing devices, for training analysis components via a self-learning strategy, comprising: providing a personal digital assistant (PDA) component, the PDA component being implemented by one or more computing devices and being configured to use one or more PDA-analysis components to at least interpret a user utterance and to select a dialog utterance in response to input parameter information; providing a simulated user (SU) component, the SU component being implemented by one or more computing devices and including an SU utterance-generating component that is configured to use an SU-analysis component to select a dialog utterance in response to input parameter information; using a scenario-selecting component to select a particular dialog scenario type from a set of possible dialog scenario types, the particular dialog scenario type identifying, at least in part, a particular task to be completed; using the SU component and the PDA component to conduct a particular dialog that includes an exchange of utterances between the SU component and the PDA component, the particular dialog pertaining to the particular dialog scenario type that has been selected; collecting training data in course of the exchange of utterances; using a PDA learning component, that is implemented by one or more computing devices, to apply a self-learning strategy to update at least one of said one or more PDA-analysis components used by the PDA component, based on the training data; and using an SU learning component, that is implemented by one or more computing devices, to apply a self-learning strategy to update the SU-analysis component used by the SU utterance-generating component, via SU reinforcement learning, based on the training data. 7. The method of claim 6, wherein the particular dialog scenario type specifies a particular type of user from among a set of possible user types, wherein each user type pertains to users who exhibit a particular type of behavior when interacting with the PDA component. 8. The method of claim 7, wherein different user types exhibit different levels of experience in interacting with the PDA component. 9. The method of claim 7, wherein the particular dialog scenario type also specifies one or more of: a time value corresponding to a time at which the particular dialog is assumed to be conducted; and/or a location value corresponding to a location at which the particular dialog is assumed to be conducted. 10. The method of claim 7, wherein the SU utterance-generating component is configured to choose a particular kind of SU-analysis component that is suited to the particular type of user that has been specified. 11. The method of claim 7, wherein the SU utterance-generating component is configured to receive input parameter information which specifies the particular type of user, wherein the SU-analysis component is a general-purpose SU-analysis component that is configured to generate SU utterances associated with two or more types of users, and wherein the SU utterance-generating component is configured to use the general-purpose SU-analysis component to generate SU utterances in a manner that is appropriate for the particular type of user. 12. The method of claim 6, wherein conducting the particular dialog includes, at the SU component: receiving a first instance of input parameter information, the first instance of input parameter information specifying at least a most recent utterance made by the PDA component, if any; using the SU-generating component to map the first instance of input parameter information to a particular SU utterance; and sending the particular SU utterance to the PDA component, and wherein said conducting the particular dialog also includes, at the PDA component: receiving a second instance of input parameter information, the second instance of input parameter information specifying at least the particular SU utterance generated by the SU component; using the PDA component to map the second instance of input parameter information to a particular PDA utterance; and sending the particular PDA utterance to the SU component, wherein said conducting the particular dialog repeats exchanges of utterances between the PDA component and the SU component until the particular task has been completed or the particular dialog is aborted. 13. The method of claim 6, wherein the SU reinforcement learning aims to achieve an objective that promotes at least: (a) efficient completion of tasks; and (b) dialog behavior that resembles actual user behavior exhibited by two or more types of users. 14. The method of claim 13, wherein the SU reinforcement learning is configured to approximate different user behavior based on training data that describes the actual user behavior. 15. The method of claim 13, wherein the SU reinforcement learning is configured to approximate different user behavior based on one or more specified constraints in an objective function that characterize the different user behavior. 16. The method of claim 13, wherein the SU reinforcement learning comprises, with respect to a particular individual instance of training: receiving training input parameter information; using the SU-analysis component to map the training input parameter information to an estimated utterance; identifying value information associated with the estimated utterance, the value information corresponding to an expected reward that will ensue upon taking the estimated utterance, and the value information incorporating a first measure that reflects an extent to which the estimated utterance is projected to lead to efficient task completion, and a second measure which reflects an extent to which the estimated utterance is projected to lead to dialog behavior that resembles target user behavior being modeled; based on the value information that has been identified, generating an update for application to the SU-analysis component; and carrying out the update to produce an updated SU-analysis component. 17. The method of claim 6, wherein the SU-analysis component includes paraphrasing functionality, and wherein the SU learning component trains the paraphrasing functionality in an initial bootstrapping phase based on a corpus of training examples obtained from one or more sources. 18. The method of claim 6, wherein the SU utterance-generating component uses a deep neural network that is configured based on weighting values specified by the SU-analysis component, wherein the deep neural network is configured to map an input PDA utterance into an output SU utterance, and wherein the deep neural network includes feedforward neural network functionality, or convolutional neural network functionality, or recursive neural network functionality, or gating neural network functionality, and/or any combination thereof. 19. The method of claim 6, wherein the PDA reinforcement learning aims to achieve an objective that promotes at least efficient completion of tasks. 20. A computer-readable storage medium for storing computer-readable instructions, the computer-readable instructions, when executed by one or more processor devices, performing a method by a simulated user (SU) component that comprises: receiving an instance of input parameter information, the instance of input parameter information specifying at least a most recent utterance made by a personal digital assistant (PDA) component, if any; automatically mapping the instance of input parameter information to a particular SU utterance, using an SU-analysis component; sending the particular SU utterance to the PDA component; repeating said receiving, mapping and sending until a particular task associated with a selected dialog has been completed; providing training data to a training system that characterizes utterances exchanged between the SU component and the PDA component over a course of one or more dialogs; and receiving an updated analysis component that is generated by the training system, based on the training data.