Conversational agents are supposed to combine speech with non-verbal modalities for intelligible multimodal utterances. In this paper, we focus on the generation of gesture and speech from XML-based descriptions of their overt form. An incremental production model is presented that combines the synthesis of synchronized gestural, verbal, and facial behaviors with mechanisms for linking them in fluent utterances with natural co-articulation and transition effects. In particular, an efficient kinematic approach for animating hand gestures from shape specifications is presented, which provides fine adaptation to temporal constraints that are imposed by cross-modal synchrony.

Abstract. This paper describes an application of the conversational agent Max in a real-world setting. The agent is employed as guide in a public computer museum, where he engages with visitors in natural face-to-face communication, provides them with information about the museum or the exhibition, and conducts natural small talk conversations. The design of the system is described with a focus on how the conversational behavior is achieved. Logfiles from interactions between Max and museum visitors were analyzed for the kinds of dialogue people are willing to have with Max. Results indicate that Max engages people in interactions where they are likely to use human-like communication strategies, suggesting the attribution of sociality to the agent.

This article surveys the area of computational empathy, analysing different ways by which artificial agents can simulate and trigger empathy in their interactions with humans. Empathic agents can be seen as agents that have the capacity to place themselves into the position of a user’s or another agent’s emotional situation and respond appropriately. We also survey artificial agents that, by their design and behaviour, can lead users to respond emotionally as if they were experiencing the agent’s situation. In the course of this survey, we present the research conducted to date on empathic agents in light of the principles and mechanisms of empathy found in humans. We end by discussing some of the main challenges that this exciting area will be facing in the future.

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We introduce the WASABI 1 Affect Simulation Architecture, in which a virtual human's cognitive reasoning capabilities are combined with simulated embodiment to achieve the simulation of primary and secondary emotions. In modeling primary emotions we follow the idea of "Core Affect" in combination with a continuous progression of bodily feeling in three-dimensional emotion space (PAD space), that is subsequently categorized into discrete emotions. In humans, primary emotions are understood as onto-genetically earlier emotions, which directly influence facial expressions. Secondary emotions, in contrast, afford the ability to reason about current events in the light of experiences and expectations. By technically representing aspects of each secondary emotion's connotative meaning in PAD space, we not only assure their mood-congruent elicitation, but also combine them with facial expressions, that are concurrently driven by primary emotions. Results of an empirical study suggest that human players in a card game scenario judge our virtual human MAX significantly older when secondary emotions are simulated in addition to primary ones.

How is communicative gesture behavior in robots perceived by humans? Although gesture is crucial in social interaction, this research question is still largely unexplored in the field of social robotics. Thus, the main objective of the present work is to investigate how gestural machine behaviors can be used to design more natural communication in social robots. The chosen approach is twofold. Firstly, the technical challenges encountered when implementing a speech-gesture generation model on a robotic platform are tackled. We present a framework that enables the humanoid robot to flexibly produce synthetic speech and co-verbal hand and arm gestures at run-time, while not being limited to a predefined repertoire of motor actions. Secondly, the achieved flexibility in robot gesture is exploited in controlled experiments. To gain a deeper understanding of how communicative robot gesture might impact and shape human perception and evaluation of human-robot interaction, we conducted a between-subjects experimental study using the humanoid robot in a joint task scenario. We manipulated the non-verbal behaviors of the robot in three experimental conditions, so that it would refer to objects by utilizing either (1) unimodal (i.e., speech only) utterances, (2) congruent multimodal (i.e., semantically matching speech and gesture) or (3) incongruent multimodal (i.e., semantically non-matching speech and gesture) utterances. Our findings reveal that the robot is evaluated more positively when nonverbal behaviors such as hand and arm gestures are displayed along with speech, even if they do not semantically match the spoken utterance.

Abstract. We describe an implemented system for the simulation and visualisation of the emotional state of a multimodal conversational agent called Max. The focus of the presented work lies on modeling a coherent course of emotions over time. The basic idea of the underlying emotion system is the linkage of two interrelated psychological concepts: an emotion axisrepresenting short-time system states -and an orthogonal mood axis that stands for an undirected, longer lasting system state. A third axis was added to realize a dimension of boredom. To enhance the believability and lifelikeness of Max, the emotion system has been integrated in the agent's architecture. In result, Max's facial expression, gesture, speech, and secondary behaviors as well as his cognitive functions are modulated by the emotional system that, in turn, is affected by information arising at various levels within the agent's architecture.

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