scholarly journals Human-Likeness in Utterance Generation: Effects of Variability

Author(s):  
Anna Hjalmarsson ◽  
Jens Edlund
2021 ◽  
Vol 94 ◽  
pp. 102823
Author(s):  
Lu Lu ◽  
Pei Zhang ◽  
Tingting (Christina) Zhang
Keyword(s):  

2015 ◽  
Vol 24 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Himalaya Patel ◽  
Karl F. MacDorman

Just as physical appearance affects social influence in human communication, it may also affect the processing of advice conveyed through avatars, computer-animated characters, and other human-like interfaces. Although the most persuasive computer interfaces are often the most human-like, they have been predicted to incur the greatest risk of falling into the uncanny valley, the loss of empathy attributed to characters that appear eerily human. Previous studies compared interfaces on the left side of the uncanny valley, namely, those with low human likeness. To examine interfaces with higher human realism, a between-groups factorial experiment was conducted through the internet with 426 midwestern U.S. undergraduates. This experiment presented a hypothetical ethical dilemma followed by the advice of an authority figure. The authority was manipulated in three ways: depiction (digitally recorded or computer animated), motion quality (smooth or jerky), and advice (disclose or refrain from disclosing sensitive information). Of these, only the advice changed opinion about the ethical dilemma, even though the animated depiction was significantly eerier than the human depiction. These results indicate that compliance with an authority persists even when using an uncannily realistic computer-animated double.


2006 ◽  
Vol 20 (10) ◽  
pp. 1147-1163 ◽  
Author(s):  
Takashi Minato ◽  
Michihiro Shimada ◽  
Shoji Itakura ◽  
Kang Lee ◽  
Hiroshi Ishiguro
Keyword(s):  

2022 ◽  
Author(s):  
Ivan Bouchardet da Fonseca Grebot ◽  
Pedro Henrique Pinheiro Cintra ◽  
Emilly Fátima Ferreira de Lima ◽  
Michella Vaz de Castro ◽  
Rui de Moraes

Author(s):  
Iskander Umarov ◽  
Maxim Mozgovoy

The rapid development of complex virtual worlds (most notably, in 3D computer and video games) introduces new challenges for the creation of virtual agents, controlled by artificial intelligence (AI) systems. Two important subproblems in this topic area which need to be addressed are (a) believability and (b) effectiveness of agents’ behavior, i.e., human-likeness of the characters and high ability to achieving their own goals. In this paper, the authors study current approaches to believability and effectiveness of AI behavior in virtual worlds. They examine the concepts of believability and effectiveness, and analyze several successful attempts to address these challenges.


2018 ◽  
Author(s):  
Jari Kätsyri ◽  
Beatrice de Gelder ◽  
Tapio Takala

The uncanny valley (UV) hypothesis suggests that increasingly human-like robots or virtual characters elicit more familiarity in their observers (positive affinity) with the exception of near-human characters that elicit strong feelings of eeriness (negative affinity). We studied this hypothesis in three experiments with carefully matched images of virtual faces varying from artificial to realistic. We investigated both painted and computer-generated (CG) faces to tap a broad range of human-likeness and to test whether CG faces would be particularly sensitive to the UV effect. Overall, we observed a linear relationship with a slight upward curvature between human-likeness and affinity. In other words, less realistic faces triggered greater eeriness in an accelerating manner. We also observed a weak UV effect for CG faces; however, least human-like faces elicited much more negative affinity in comparison. We conclude that although CG faces elicit a weak UV effect, this effect is not fully analogous to the original UV hypothesis. Instead, the subjective evaluation curve for face images resembles an uncanny slope more than a UV. Based on our results, we also argue that subjective affinity should be contrasted against subjective ratherthan objective measures of human-likeness when testing UV.


2021 ◽  
Vol 2 ◽  
Author(s):  
Mysore Narasimhamurthy Sharath ◽  
Babak Mehran

The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.


2021 ◽  
Vol 8 ◽  
Author(s):  
Hisashi Ishihara ◽  
Saneyuki Iwanaga ◽  
Minoru Asada

The behavior of an android robot face is difficult to predict because of the complicated interactions between many and various attributes (size, weight, and shape) of system components. Therefore, the system behavior should be analyzed after these components are assembled to improve their performance. In this study, the three-dimensional displacement distributions for the facial surfaces of two android robots were measured for the analysis. The faces of three adult males were also analyzed for comparison. The visualized displacement distributions indicated that the androids lacked two main deformation features observed in the human upper face: curved flow lines and surface undulation, where the upstream areas of the flow lines elevate. These features potentially characterize the human-likeness. These findings suggest that innovative composite motion mechanisms to control both the flow lines and surface undulations are required to develop advanced androids capable of exhibiting more realistic facial expressions. Our comparative approach between androids and humans will improve androids’ impressions in future real-life application scenes, e.g., receptionists in hotels and banks, and clerks in shops.


Author(s):  
Boyoung Kim ◽  
Elizabeth Phillips

Robots are entering various domains of human societies, potentially unfolding more opportunities for people to perceive robots as social agents. We expect that having robots in proximity would create unique social learning situations where humans spontaneously observe and imitate robots’ behaviors. At times, these occurrences of humans’ imitating robot behaviors may result in a spread of unsafe or unethical behaviors among humans. For responsible robot designing, therefore, we argue that it is essential to understand physical and psychological triggers of social learning in robot design. Grounded in the existing literature of social learning and the uncanny valley theories, we discuss the human-likeness of robot appearance and affective responses associated with robot appearance as likely factors that either facilitate or deter social learning. We propose practical considerations for social learning and robot design.


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