28,376 research outputs found
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
USER-AWARENESS AND ADAPTATION IN CONVERSATIONAL AGENTS
This paper considers the research question of developing user-aware and adaptive conversational agents. The conversational agent is a system which is user-aware to the extent that it recognizes the user identity and his/her emotional states that are relevant in a given interaction domain. The conversational agent is user-adaptive to the extent that it dynamically adapts its dialogue behavior according to the user and his/her emotional state. The paper summarizes some aspects of our previous work and presents work-in-progress in the field of speech-based human-machine interaction. It focuses particularly on the development of speech recognition modules in cooperation with both modules for emotion recognition and speaker recognition, as well as the dialogue management module. Finally, it proposes an architecture of a conversational agent that integrates those modules and improves each of them based on some kind of synergies among themselves
Context-Aware Conversational Agents Using POMDPs and Agenda-Based Simulation
Proceedings of: Workshop on User-Centric Technologies and Applications (CONTEXTS 2011), Salamanca, April 6-8, 2011Context-aware systems in combination with mobile devices offer new opportunities in the areas of knowledge representation, natural language processing and intelligent information retrieval. Our vision is that natural spoken conversation with these devices can eventually become the preferred mode for managing their services by means of conversational agents. In this paper, we describe the application of POMDPs and agenda-based user simulation to learn optimal dialog policies for the dialog manager in a conversational agent. We have applied this approach to develop a statistical dialog manager for a conversational agent which acts as a voice logbook to collect home monitored data from patients suffering from diabetes.Funded by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485), and DPS2008-07029-C02-02.Publicad
No Grice: Computers that Lie, Deceive and Conceal
In the future our daily life interactions with other people, with computers, robots and smart environments will be recorded and interpreted by computers or embedded intelligence in environments, furniture, robots, displays, and wearables. These sensors record our activities, our behavior, and our interactions. Fusion of such information and reasoning about such information makes it possible, using computational models of human behavior and activities, to provide context- and person-aware interpretations of human behavior and activities, including determination of attitudes, moods, and emotions. Sensors include cameras, microphones, eye trackers, position and proximity sensors, tactile or smell sensors, et cetera. Sensors can be embedded in an environment, but they can also move around, for example, if they are part of a mobile social robot or if they are part of devices we carry around or are embedded in our clothes or body. \ud
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Our daily life behavior and daily life interactions are recorded and interpreted. How can we use such environments and how can such environments use us? Do we always want to cooperate with these environments; do these environments always want to cooperate with us? In this paper we argue that there are many reasons that users or rather human partners of these environments do want to keep information about their intentions and their emotions hidden from these smart environments. On the other hand, their artificial interaction partner may have similar reasons to not give away all information they have or to treat their human partner as an opponent rather than someone that has to be supported by smart technology.\ud
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This will be elaborated in this paper. We will survey examples of human-computer interactions where there is not necessarily a goal to be explicit about intentions and feelings. In subsequent sections we will look at (1) the computer as a conversational partner, (2) the computer as a butler or diary companion, (3) the computer as a teacher or a trainer, acting in a virtual training environment (a serious game), (4) sports applications (that are not necessarily different from serious game or education environments), and games and entertainment applications
An End-to-End Conversational Style Matching Agent
We present an end-to-end voice-based conversational agent that is able to
engage in naturalistic multi-turn dialogue and align with the interlocutor's
conversational style. The system uses a series of deep neural network
components for speech recognition, dialogue generation, prosodic analysis and
speech synthesis to generate language and prosodic expression with qualities
that match those of the user. We conducted a user study (N=30) in which
participants talked with the agent for 15 to 20 minutes, resulting in over 8
hours of natural interaction data. Users with high consideration conversational
styles reported the agent to be more trustworthy when it matched their
conversational style. Whereas, users with high involvement conversational
styles were indifferent. Finally, we provide design guidelines for multi-turn
dialogue interactions using conversational style adaptation
SUM’20: State-based user modelling
Capturing and effectively utilising user states and goals is becoming a timely challenge for successfully leveraging intelligent and usercentric systems in differentweb search and data mining applications. Examples of such systems are conversational agents, intelligent assistants, educational and contextual information retrieval systems, recommender/match-making systems and advertising systems, all of which rely on identifying the user state in order to provide the most relevant information and assist users in achieving their goals. There has been, however, limited work towards building such state-aware intelligent learning mechanisms. Hence, devising information systems that can keep track of the user's state has been listed as one of the grand challenges to be tackled in the next few years [1]. It is thus timely to organize a workshop that re-visits the problem of designing and evaluating state-aware and user-centric systems, ensuring that the community (spanning academic and industrial backgrounds) works together to tackle these challenges
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