From observation to simulation: generating culture-specific behavior for interactive systems

Rehm, Matthias; Nakano, Yukiko I.; André, Elisabeth; Nishida, Toyoaki; Bee, Nikolaus; Endraß, Birgit; Wißner, Michael; Lipi, Afia Akhter; Huang, Hung-Hsuan

doi:10.1007/s00146-009-0216-3

Cited by 39 publications

(17 citation statements)

References 23 publications

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“…It has been shown [11] that more body contact can be expected in Germany due to ritualistic handshakes during the greeting. The corpus analysis in CUBE-G could confirm these findings (only partially for the body contact claim, see [28]). Additional results include differences in posture and in gestural expressivity.…”

Section: Scenario 1: First Meetingmentioning

confidence: 81%

“…The results reveal significant differences between German and Japanese samples in preferred hand and arm postures, in most of the dimensions for gestural expressivity as well as in communication management and small talk behavior. Details of the analyses can be found in [28] and [9]. As has been noted earlier, it can be expected that such a purely statistical analysis does only reveal some general trends and that the semantics of the dimensions and the interaction context have to be taken into account.…”

Section: Resultsmentioning

confidence: 99%

“…An in-depth description of the annotation schemes for gestures and postures as well as some results from the corpus study can be found in [28]. As we expect culture-specific differences in the observed behavior, we also have to assume culture-specific differences in the analysis of this behavior.…”

Section: Phenomena Occurrencesmentioning

confidence: 99%

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Creating Standardized Video Recordings of Multimodal Interactions across Cultures

et al. 2009

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Abstract. Trying to adapt the behavior of an interactive system to the cultural background of the user requires information on how relevant behaviors differ as a function of the user's cultural background. To gain such insights in the interrelation of culture and behavior patterns, the information from the literature is often too anecdotal to serve as the basis for modeling a system's behavior, making it necessary to collect multimodal corpora in a standardized fashion in different cultures. In this chapter, the challenges of such an endeavor are introduced and solutions are presented by examples from a German-Japanese project that aims at modeling culture-specific behaviors for Embodied Conversational Agents.

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Section: Scenario 1: First Meetingmentioning

confidence: 81%

Section: Resultsmentioning

confidence: 99%

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Creating Standardized Video Recordings of Multimodal Interactions across Cultures

et al. 2009

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“…Artificial agents are no longer restricted simply to performing logistical functions such as resource scheduling, but are now capable of more complex interpersonal workplace behavior such as using social intelligence to effectively manage the limitations, abilities, and expectations of human employees [1], recognizing and manifesting culture-specific behaviors in interactions with human colleagues [2], and assessing the performance of human members of virtual teams [3]. It is thus gradually becoming more feasible to design artificial agents capable of performing the four key functions carried out by human managers, which are planning, organizing, leading, and controlling [4].…”

Section: The Need For a Common Temporal Measure Of Work Effortmentioning

confidence: 99%

A Fractal Measure for Comparing the Work Effort of Human and Artificial Agents Performing Management Functions

Gladden

2014

Annals of Computer Science and Information Systems

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Abstract-Thanks to the growing sophistication of artificial agent technologies, businesses will increasingly face decisions of whether to have a human employee or artificial agent perform a particular function. This makes it desirable to have a common temporal measure for comparing the work effort that human beings and artificial agents can apply to a role. Existing temporal measures of work effort are formulated to apply either to human employees (e.g., FTE and billable hours) or computer-based systems (e.g., mean time to failure and availability) but not both. In this paper we propose a new temporal measure of work effort based on fractal dimension that applies equally to the work of human beings and artificial agents performing management functions. We then consider four potential cases to demonstrate the measure's diagnostic value in assessing strengths (e.g., flexibility) and risks (e.g., switch costs) reflected by the temporal work dynamics of particular managers. I. THE NEED FOR A COMMON TEMPORAL MEASURE OF WORK EFFORTHE increasing power and sophistication of artificial agent technology is allowing businesses to employ artificial agents in a growing number of roles. Artificial agents are no longer restricted simply to performing logistical functions such as resource scheduling, but are now capable of more complex interpersonal workplace behavior such as using social intelligence to effectively manage the limitations, abilities, and expectations of human employees [1], recognizing and manifesting culture-specific behaviors in interactions with human colleagues [2], and assessing the performance of human members of virtual teams [3]. It is thus gradually becoming more feasible to design artificial agents capable of performing the four key functions carried out by human managers, which are planning, organizing, leading, and controlling [4]. TAs a result of such recent and anticipated future advances, businesses will increasingly be faced with concrete decisions about whether, for example, the manager of a new corporate call center should be an experienced human manager or the latest artificial agent system. Such decisions will be shaped by a large number of strategic, financial, technological, political, legal, ethical, and operational factors. One particular element to be taken into account is that of temporal work effort: i.e., how much time would a human manager actually be able to dedicate to carrying out the necessary work functions, given the fact that physiological, cultural, legal, and ethical constraints limit the number of hours per week that a human being is capable of working? Similarly, how much time would an artificial agent be able to dedicate to carrying out the necessary work functions, given the fact that scheduled maintenance or unscheduled outages can limit the uptime of computer-based systems? Knowing how much time per day (or week, or other relevant time interval) a manager will be available to carry out his or her functions of planning, organizing, leading, and controlling becomes espe...

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“…[36] for an overview) and makes it applicable on a mobile platform. It allows training culture-specific gestures making use of the sensor technology of current smartphones and applying the gestures in role-plays with virtual characters.…”

Section: Introductionmentioning

confidence: 98%

Gesture-based mobile training of intercultural behavior

Rehm

Leichtenstern

2011

Multimedia Systems

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Cultural heuristics determine acceptable verbal and non-verbal behavior in interpersonal encounters and are often the main reason for problems in intercultural communication. In this article, we present an approach to intercultural training of non-verbal behaviors that makes use of enculturated virtual agents, i.e. interactive systems that take cultural heuristics for interpreting and generating behavior into account. Because current trends in intercultural training highlight the importance of a coaching approach, i.e. the ability to offer training units anytime and anywhere, the system was developed as a mobile solution taking the sensoric capabilities of smart phones into account for the user interaction in form of gesture recognition. After an introduction of the theoretical background on culture and enculturated systems, the system features are discussed in detail followed by an account of the application itself, emphasizing the importance of situated role-plays. Two evaluation studies are presented next that analyze the usability of the approach as well as the more important question of whether training with the system gives better results than traditional methods.

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From observation to simulation: generating culture-specific behavior for interactive systems

Cited by 39 publications

References 23 publications

Creating Standardized Video Recordings of Multimodal Interactions across Cultures

Creating Standardized Video Recordings of Multimodal Interactions across Cultures

A Fractal Measure for Comparing the Work Effort of Human and Artificial Agents Performing Management Functions

Gesture-based mobile training of intercultural behavior

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