It is increasingly common to embed embodied, human-like, virtual agents into immersive virtual environments for either of the two use cases: (1) populating architectural scenes as anonymous members of a crowd and (2) meeting or supporting users as individual, intelligent and conversational agents. However, the new trend towards intelligent cyber physical systems inherently combines both use cases. Thus, we argue for the necessity of multiagent systems consisting of anonymous and autonomous agents, who temporarily turn into intelligent individuals. Besides purely enlivening the scene, each agent can thus be engaged into a situation-dependent interaction by the user, e.g., into a conversation or a joint task. To this end, we devise components for an agent’s behavioral design modeling the transition between an anonymous and an individual agent when a user approaches.
Embodied, virtual agents provide users assistance in agent-based support systems. To this end, two closely linked factors have to be considered for the agents’ behavioral design: their presence time (PT), i.e., the time in which the agents are visible, and the approaching time (AT), i.e., the time span between the user’s calling for an agent and the agent’s actual availability.
This work focuses on human-like assistants that are embedded in immersive scenes but that are required only temporarily. To the best of our knowledge, guidelines for a suitable trade-off between PT and AT of these assistants do not yet exist. We address this gap by presenting the results of a controlled within-subjects study in a CAVE. While keeping a low PT so that the agent is not perceived as annoying, three strategies affecting the AT, namely fading, walking, and running, are evaluated by 40 subjects. The results indicate no clear preference for either behavior. Instead, the necessity of a better trade-off between a low AT and an agent’s realistic behavior is demonstrated.
Traditionally, experimental economics uses controlled and incentivized field and lab experiments to analyze economic behavior. However, investigating peer effects in the classic settings is challenging due to the reflection problem: Who is influencing whom?
To overcome this, we enlarge the methodological toolbox of these experiments by means of Virtual Reality. After introducing and validating a real-effort sorting task, we embed a virtual agent as peer of a human subject, who independently performs an identical sorting task. We conducted two experiments investigating (a) the subject’s productivity adjustment due to peer effects and (b) the incentive effects on competition. Our results indicate a great potential for Virtual-Reality-based economic experiments.