A virtual diary companion

Chatbots and embodied conversational agents show turn based conversation behaviour. In current research we almost always assume that each utterance of a human conversational partner should be followed by an intelligent and/or empathetic reaction of chatbot or embodied agent. They are assumed to be alert, trying to please the user. There are other applications which have not yet received much attention and which require a more patient or relaxed attitude, waiting for the right moment to provide feedback to the human partner. Being able and willing to listen is one of the conditions for being successful. In this paper we have some observations on listening behaviour research and introduce one of our applications, the virtual diary companion

Emotions, behaviour and belief regulation in an intelligent guide with attitude

Abstract unavailable please refer to PD

Personalized Memory Transfer for Conversational Recommendation Systems

Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach

Impact of perceived value on intention to use voice assistants: The moderating effects of personal innovativeness and experience

Voice assistants (VAs), such as Alexa, Siri, and Google Assistant, are instruments increasingly used by consumers to perform daily tasks. The objectives of the present study are to examine the antecedents of consumers' continuance intention to use VAs and the moderating effects of personal innovativeness and experience. Based on behavioral reasoning theory, a research model is proposed to provide insights into the drivers of continuance intention to use. Two empirical studies, based on data collected via online surveys, were conducted. The model was analyzed through partial least squares structural equation modeling. The findings of the studies showed that emotional value and performance expectancy were key antecedents of continuance intention to use, which in turn positively influenced actual use and word‐of‐mouth intention. In contrast, the quality value was a significant antecedent of continuance intention to use in only one of the two studies, and the influence of price value, social value, effort expectancy, and privacy risk was not found to be significant. However, the second study showed that several of these relationships are moderated by the consumer's experience and personal innovativeness; specifically, less innovative users are sensitive to quality value, and experienced users are sensitive to social valueAndalusian Research, Development and Innovation Plan (PAIDI 2020)Grant: Group SEJ‐567 (Spain)Universidad de Málaga/CBU

Become a Lifesaver - How to Design Conversational Agents to Increase Users’ Intention to Donate Blood

Donating blood is a selfless act that impacts public welfare, potentially saving human lives. However, blood shortage is a rising worldwide issue due to increased demand. Thus, finding ways to animate and motivate potential donors to donate blood is para-mount. In this context, conversational agents (CAs) offer a promising approach to edu-cating, promoting, and achieving desired behaviors. In this paper, we conducted an online experimental study (N=303) and investigated the effect of a human-like designed CA and fear-inducing communication on users’ intention to donate. Our results show that users’ intention is driven by perceived persuasiveness rather than perceived human-ness and that fear-inducing communication does not significantly affect the intention to donate. Against this background, we provide numerous theoretical and practical impli-cations, contributing to information system literature by enhancing our understanding of how fear-inducing communication is used in CA interactions

A Satisfaction-based Model for Affect Recognition from Conversational Features in Spoken Dialog Systems

Detecting user affect automatically during real-time conversation is the main challenge towards our greater aim of infusing social intelligence into a natural-language mixed-initiative High-Fidelity (Hi-Fi) audio control spoken dialog agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. This paper attempts to address part of this challenge by considering the role of user satisfaction ratings and also conversational/dialog features in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. However, given the laboratory constraints, users might be positively biased when rating the system, indirectly making the reliability of the satisfaction data questionable. Machine learning experiments were conducted on two datasets, users and annotators, which were then compared in order to assess the reliability of these datasets. Our results indicated that standard classifiers were significantly more successful in discriminating the abovementioned emotions and their intensities (reflected by user satisfaction ratings) from annotator data than from user data. These results corroborated that: first, satisfaction data could be used directly as an alternative target variable to model affect, and that they could be predicted exclusively by dialog features. Second, these were only true when trying to predict the abovementioned emotions using annotator?s data, suggesting that user bias does exist in a laboratory-led evaluation

Когнитивни процеси, емоции и интелигентни интерфејси

Студијата презентира истражувања од повеќе научни дисциплини, како вештачка интелигенција, невронауки, психологија, лингвистика и филозофија, кои имаат потенцијал за креирање на интелигентни антропоморфни агенти и интерактивни технологии. Се разгледуваат системите од симболичка и конекционистичка вештачка интелигенција за моделирање на човековите когнитивни процеси, мислење, донесување одлуки, меморија и учење. Се анализираат моделите во вештачка интелигенција и роботика кои користат емоции како механизам за контрола на остварување на целите на роботот, како реакција на одредени ситуации, за одржување на процесот на социјална интеракција и за создавање на поуверливи антропормфни агенти. Презентираните интердисциплинарни методологии и концепти се мотивација за создавање на анимирани агенти кои користат говор, гестови, интонација и други невербални модалитети при конверзација со корисниците во интелигентните интерфејси