13,572 research outputs found
Comparing User Responses to Limited and Flexible Interaction in a Conversational Interface
The principles governing written communication have been well studied, and well incorporated in interactive computer systems. However, the role of spoken language and in human-computer interaction, while an increasingly popular modality, still needs to be explored further [3]. Evidence suggests that this technology must further evolve in order to support more "natural" conversations [2], and that the use of speech interfaces is correlated with a high cognitive demand and attention [4]. In the context of spoken dialogue systems, a continuum has long been identified between "systeminitiative" interactions, where the system is in complete control of the overall interaction and the user answers a series of prescribed questions, and "user-initiative" interactions, where the user is free to say anything and the system must respond [5]. However, much of the work in this area predates the recent explosive growth of conversational interfaces
Conversational Sensing
Recent developments in sensing technologies, mobile devices and context-aware
user interfaces have made it possible to represent information fusion and
situational awareness as a conversational process among actors - human and
machine agents - at or near the tactical edges of a network. Motivated by use
cases in the domain of security, policing and emergency response, this paper
presents an approach to information collection, fusion and sense-making based
on the use of natural language (NL) and controlled natural language (CNL) to
support richer forms of human-machine interaction. The approach uses a
conversational protocol to facilitate a flow of collaborative messages from NL
to CNL and back again in support of interactions such as: turning eyewitness
reports from human observers into actionable information (from both trained and
untrained sources); fusing information from humans and physical sensors (with
associated quality metadata); and assisting human analysts to make the best use
of available sensing assets in an area of interest (governed by management and
security policies). CNL is used as a common formal knowledge representation for
both machine and human agents to support reasoning, semantic information fusion
and generation of rationale for inferences, in ways that remain transparent to
human users. Examples are provided of various alternative styles for user
feedback, including NL, CNL and graphical feedback. A pilot experiment with
human subjects shows that a prototype conversational agent is able to gather
usable CNL information from untrained human subjects
PIWeCS: enhancing human/machine agency in an interactive composition system
This paper focuses on the infrastructure and aesthetic approach used in PIWeCS: a Public Space Interactive Web-based Composition System. The concern was to increase the sense of dialogue between human and machine agency in an interactive work by adapting Paine's (2002) notion of a conversational model of interaction as a ‘complex system’. The machine implementation of PIWeCS is achieved through integrating intelligent agent programming with MAX/MSP. Human input is through a web infrastructure. The conversation is initiated and continued by participants through arrangements and composition based on short performed samples of traditional New Zealand Maori instruments. The system allows the extension of a composition through the electroacoustic manipulation of the source material
Chatbots for learning: A review of educational chatbots for the Facebook Messenger
With the exponential growth in the mobile device market over the last decade, chatbots are becoming an increasingly popular option to interact with users, and their popularity and adoption are rapidly spreading. These mobile devices change the way we communicate and allow ever-present learning in various environments. This study examined educational chatbots for Facebook Messenger to support learning. The independent web directory was screened to assess chatbots for this study resulting in the identification of 89 unique chatbots. Each chatbot was classified by language, subject matter and developer's platform. Finally, we evaluated 47 educational chatbots using the Facebook Messenger platform based on the analytic hierarchy process against the quality attributes of teaching, humanity, affect, and accessibility. We found that educational chatbots on the Facebook Messenger platform vary from the basic level of sending personalized messages to recommending learning content. Results show that chatbots which are part of the instant messaging application are still in its early stages to become artificial intelligence teaching assistants. The findings provide tips for teachers to integrate chatbots into classroom practice and advice what types of chatbots they can try out.Web of Science151art. no. 10386
Convo: What does conversational programming need? An exploration of machine learning interface design
Vast improvements in natural language understanding and speech recognition
have paved the way for conversational interaction with computers. While
conversational agents have often been used for short goal-oriented dialog, we
know little about agents for developing computer programs. To explore the
utility of natural language for programming, we conducted a study (=45)
comparing different input methods to a conversational programming system we
developed. Participants completed novice and advanced tasks using voice-based,
text-based, and voice-or-text-based systems. We found that users appreciated
aspects of each system (e.g., voice-input efficiency, text-input precision) and
that novice users were more optimistic about programming using voice-input than
advanced users. Our results show that future conversational programming tools
should be tailored to users' programming experience and allow users to choose
their preferred input mode. To reduce cognitive load, future interfaces can
incorporate visualizations and possess custom natural language understanding
and speech recognition models for programming.Comment: 9 pages, 7 figures, submitted to VL/HCC 2020, for associated user
study video: https://youtu.be/TC5P3OO5ex
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