THE EMPATHIC COMPANION: A CHARACTER-BASED INTERFACE THAT ADDRESSES USERS' AFFECTIVE STATES

By Helmut Prendinger, Mitsuru Ishizuka. Applied Artificial Intelligence, 2005

336 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/e3569567eb3b2a0ed3b578e9481a140c9223759f

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In Artificial Intelligence

In Experimental and Cognitive Psychology

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Citations in the corpus

Emotionally-Aware Chatbots: A Survey (2019, background): “Some existing studies shows that adding emotion information into dialogue systems is able to improve user-satisfaction [43, 61].”
Empathy in Virtual Agents and Robots (2017, methodology): “Prendinger and Ishizuka (2005) proposed the Empathic Companion, an embodied virtual character that supports users in the setting of a virtual job interview task (see Figure 9).”
Should an Agent Be Ignoring It?: A Study of Verbal Abuse Types and Conversational Agents' Response Styles (2019, background): “Prendinger [7] identified that negative emotions reduced when users receive empathetic feedback from a service agent.”
Affect-aware tutors: recognising and responding to student affect (2009, background): “, 2002); users have immediately lowered stress level (via skin conductance) after empathy and after an apology (Prendinger et al., 2003; Prendinger and Ishizuka, 2005) and relational skills improve long-term ratings of caring, trust, respect, desire to keep working (Bickmore and Picard, 2004).”
Making Social Robots More Attractive: The Effects of Voice Pitch, Humor and Empathy (2013, background): “However, research has shown that it is possible to model empathy using only oral style of interaction: Prendinger and Ishizuka [61] implemented empathetic behaviors in a lifelike character companion attached to a web-based application with successful results.”
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory (2017, background): “Experiments in (Prendinger and Ishizuka, 2005) showed that an empathetic computer agent can contribute to a more positive perception of the interaction.”
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, background): “…states in an attempt to develop more effective, user-friendly, and naturalistic applications (Conati and Maclaren 2009; Forbes-Riley et al. 2008; Hudlicka and McNeese 2002; Madsen et al. 2008; McQuiggan et al. 2008; Paiva et al. 2007; Porayska-Pomsta et al. 2008; Prendinger and Ishizuka 2005).”
Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments (2010, background): “One possibility is automatic detection of cognitive-affective states by computers (D'Mello , Picard, & Graesser, 2007; Kapoor, Burleson, & Picard, 2007; Prendinger & Ishizuka, 2005).”
Empathy Is All You Need: How a Conversational Agent Should Respond to Verbal Abuse (2020, background): “Prendinger and Ishizuka [51] note that users’ negative emotions diminished when users receive empathetic feedback from a virtual assistant.”
Automatic detection of learner’s affect from conversational cues (2008, background): “Researchers in the field of human–computer interaction (HCI) have recently been modeling the affect of users (feelings, moods, emotions) in an attempt to develop more effective, user-friendly, and naturalistic applications (Bianchi-Berthouze and Lisetti 2002; Hudlicka and McNeese 2002; Klein et al. ”
The influence of empathy in human-robot relations (2013, background): “Empathy is a research topic that has received a lot of attention in the field of virtual agents (Paiva et al., 2004; Prendinger and Ishizuka, 2005; Bickmore and Picard, 2005; Brave et al., 2005; McQuiggan and Lester, 2007).”
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “The aim here is to equip emotional intelligence into these systems to improve user interest and engagement, Partala & Surakka [166], Prendinger & Ishizuka [176].”