A Tool for Staging Mixed-initiative Dialogs

We discuss and demonstrate a tool for prototyping dialog-based systems that, given a high-level specification of a human-computer dialog, stages the dialog for interactive use. The tool enables a dialog designer to evaluate a variety of dialogs without having to program each individual dialog, and serves as a proof-of-concept for our approach to mixed-initiative dialog modeling and implementation from a programming language-based perspective

Modeling and Operationalizing Flexible Human-Computer Dialogs

We demonstrate a tool for rapidly prototyping dialog-based systems for interactive use. The tool enables a dialog designer to evaluate a variety of dialogs without having to program each individual dialog, and provides a proof-of-concept for our approach to mixed-initiative dialog modeling and implementation. Applications of our Our tool can be applied to human-computer dialogs common in automated teller machines (ATMs), kiosks, personal assistants, and online forms including course scheduling.https://ecommons.udayton.edu/stander_posters/1783/thumbnail.jp

Mixed-initiative Personal Assistants

Specification and implementation of flexible human-computer dialogs is challenging because of the complexity involved in rendering the dialog responsive to a vast number of varied paths through which users might desire to complete the dialog. To address this problem, we developed a toolkit for modeling and implementing task-based, mixed-initiative dialogs based on metaphors from lambda calculus. Our toolkit can automatically operationalize a dialog that involves multiple prompts and/or sub-dialogs, given a high-level dialog specification of it. Our current research entails incorporating the use of natural language to make the flexibility in communicating user utterances commensurate with that in dialog completion paths

CSE: U: Mixed-initiative Personal Assistant Agents

Specification and implementation of flexible human-computer dialogs is challenging because of the complexity involved in rendering the dialog responsive to a vast number of varied paths through which users might desire to complete the dialog. To address this problem, we developed a toolkit for modeling and implementing task-based, mixed-initiative dialogs based on metaphors from lambda calculus. Our toolkit can automatically operationalize a dialog that involves multiple prompts and/or sub-dialogs, given a high-level dialog specification of it. The use of natural language with the resulting dialogs makes the flexibility in communicating user utterances commensurate with that in dialog completion paths—an aspect missing from commercial assistants like Siri. Our results demonstrate that the dialogs authored with our toolkit support the end user’s completion of a human-computer dialog in a manner that is most natural to them—in a mixed-initiative fashion—that resembles human-human interaction

Natural Language, Mixed-Initiative Personal Assistant Agents

The increasing popularity and use of personal voice assistant technologies, such as Siri and Google Now, is driving and expanding progress toward the long-term and lofty goal of using artificial intelligence to build human-computer dialog systems capable of understanding natural language. While dialog-based systems such as Siri support utterances communicated through natural language, they are limited in the flexibility they afford to the user in interacting with the system and, thus, support primarily action-requesting and information-seeking tasks. Mixed-initiative interaction, on the other hand, is a flexible interaction technique where the user and the system act as equal participants in an activity, and is often exhibited in human-human conversations. In this paper, we study user support for mixed-initiative interaction with dialog-based systems through natural language using a bag-of-words model and k-nearest-neighbor classifier. We study this problem in the context of a toolkit we developed for automated, mixed-initiative dialog system construction, involving a dialog authoring notation and management engine based on lambda calculus, for specifying and implementing task-based, mixed-initiative dialogs. We use ordering at Subway through natural language, human-computer dialogs as a case study. Our results demonstrate that the dialogs authored with our toolkit support the end user\u27s completion of a natural language, human-computer dialog in a mixed-initiative fashion. The use of natural language in the resulting mixed-initiative dialogs afford the user the ability to experience multiple self-directed paths through the dialog and makes the flexibility in communicating user utterances commensurate with that in dialog completion paths---an aspect missing from commercial assistants like Siri