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- ZeitschriftenartikelSpecial Issue on Emotion and Computing(KI - Künstliche Intelligenz: Vol. 25, No. 3, 2011) Reichardt, Dirk M.
- ZeitschriftenartikelBehaviour Coordination for Models of Affective Behaviour(KI - Künstliche Intelligenz: Vol. 25, No. 3, 2011) Rank, StefanSoftware or robotic agents that can reproduce some of the (human) phenomena labelled as emotional have a range of applications in entertainment, pedagogy, and human computer interaction in general. Based on previous experience in modelling emotion, the method of scenario-based analysis for the comparison and design of affective agent architectures as well as a new approach towards incremental modelling of emotional phenomena are introduced. The approach uses concurrent processes, resources, and explicitly modelled related limitations as building blocks for affective agent architectures in order to work towards coordination mechanisms in a concurrent model of affective competences.
- ZeitschriftenartikelSocial Signal Interpretation (SSI)(KI - Künstliche Intelligenz: Vol. 25, No. 3, 2011) Wagner, Johannes; Lingenfelser, Florian; Bee, Nikolaus; André, ElisabethThe development of anticipatory user interfaces is a key issue in human-centred computing. Building systems that allow humans to communicate with a machine in the same natural and intuitive way as they would with each other requires detection and interpretation of the user’s affective and social signals. These are expressed in various and often complementary ways, including gestures, speech, mimics etc. Implementing fast and robust recognition engines is not only a necessary, but also challenging task. In this article, we introduce our Social Signal Interpretation (SSI) tool, a framework dedicated to support the development of such online recognition systems. The paper at hand discusses the processing of four modalities, namely audio, video, gesture and biosignals, with focus on affect recognition, and explains various approaches to fuse the extracted information to a final decision.
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 25, No. 3, 2011)
- ZeitschriftenartikelEmpathy-Based Emotional Alignment for a Virtual Human: A Three-Step Approach(KI - Künstliche Intelligenz: Vol. 25, No. 3, 2011) Boukricha, Hana; Wachsmuth, IpkeAllowing virtual humans to align to others’ perceived emotions is believed to enhance their cooperative and communicative social skills. In our work, emotional alignment is realized by endowing a virtual human with the ability to empathize. Recent research shows that humans empathize with each other to different degrees depending on several factors including, among others, their mood, their personality, and their social relationships. Although providing virtual humans with features like affect, personality, and the ability to build social relationships, little attention has been devoted to the role of such features as factors modulating their empathic behavior. Supported by psychological models of empathy, we propose an approach to model empathy for the virtual human EMMA—an Empathic MultiModal Agent—consisting of three processing steps: First, the Empathy Mechanism by which an empathic emotion is produced. Second, the Empathy Modulation by which the empathic emotion is modulated. Third, the Expression of Empathy by which EMMA’s multiple modalities are triggered through the modulated empathic emotion. The proposed model of empathy is illustrated in a conversational agent scenario involving the virtual humans MAX and EMMA.
- ZeitschriftenartikelTheatre, Perception, Symbol(KI - Künstliche Intelligenz: Vol. 25, No. 3, 2011) Sonntag, Daniel
- ZeitschriftenartikelComputational Assessment of Interest in Speech—Facing the Real-Life Challenge(KI - Künstliche Intelligenz: Vol. 25, No. 3, 2011) Wöllmer, Martin; Weninger, Felix; Eyben, Florian; Schuller, BjörnAutomatic detection of a speaker’s level of interest is of high relevance for many applications, such as automatic customer care, tutoring systems, or affective agents. However, as the latest Interspeech 2010 Paralinguistic Challenge has shown, reliable estimation of non-prototypical natural interest in spontaneous conversations independent of the subject still remains a challenge. In this article, we introduce a fully automatic combination of brute-forced acoustic features, linguistic analysis, and non-linguistic vocalizations, exploiting cross-entity information in an early feature fusion. Linguistic information is based on speech recognition by a multi-stream approach fusing context-sensitive phoneme predictions and standard acoustic features. We provide subject-independent results for interest assessment using Bidirectional Long Short-Term Memory networks on the official Challenge task and show that our proposed system leads to the best recognition accuracies that have ever been reported for this task. The according TUM AVIC corpus consists of highly spontaneous speech from face-to-face commercial presentations. The techniques presented in this article are also used in the SEMAINE system, which features an emotion sensitive embodied conversational agent.
- ZeitschriftenartikelInterview with Rosalind Picard(KI - Künstliche Intelligenz: Vol. 25, No. 3, 2011) Reichardt, Dirk M.
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 25, No. 3, 2011)
- ZeitschriftenartikelA Neuroscientific View on the Role of Emotions in Behaving Cognitive Agents(KI - Künstliche Intelligenz: Vol. 25, No. 3, 2011) Vitay, Julien; Hamker, Fred H.While classical theories systematically opposed emotion and cognition, suggesting that emotions perturbed the normal functioning of the rational thought, recent progress in neuroscience highlights on the contrary that emotional processes are at the core of cognitive processes, directing attention to emotionally-relevant stimuli, favoring the memorization of external events, valuating the association between an action and its consequences, biasing decision making by allowing to compare the motivational value of different goals and, more generally, guiding behavior towards fulfilling the needs of the organism. This article first proposes an overview of the brain areas involved in the emotional modulation of behavior and suggests a functional architecture allowing to perform efficient decision making. It then reviews a series of biologically-inspired computational models of emotion dealing with behavioral tasks like classical conditioning and decision making, which highlight the computational mechanisms involved in emotional behavior. It underlines the importance of embodied cognition in artificial intelligence, as emotional processing is at the core of the cognitive computations deciding which behavior is more appropriate for the agent.