- Home
- e-Journals
- Interaction Studies
- Previous Issues
- Volume 23, Issue 3, 2022
Interaction Studies - Volume 23, Issue 3, 2022
Volume 23, Issue 3, 2022
-
Socially acceptable robot behavior
Author(s): Oliver Roesler, Elahe Bagheri, Amir Aly, Silvia Rossi and Rachid Alamipp.: 355–359 (5)More Less
-
Social appropriateness in HMI
Author(s): Ricarda Wullenkord, Jacqueline Bellon, Bruno Gransche, Sebastian Nähr-Wagener and Friederike Eysselpp.: 360–390 (31)More LessAbstractSocial appropriateness is an important topic – both in the human-human interaction (HHI), and in the human-machine interaction (HMI) context. As sociosensitive and socioactive assistance systems advance, the question arises whether a machine’s behavior should include considerations regarding social appropriateness. However, the concept of social appropriateness is difficult to define, as it is determined by multiple aspects. Thus, to date, a unified perspective, encompassing and combining multidisciplinary findings, is missing. When translating results from HHI to HMI, it remains unclear whether such insights into the dynamics of social appropriateness between humans may in fact apply to sociosensitive and socioactive assistance systems. To shed light on this matter, we propose the Five Factor Model of Social Appropriateness (FASA) which provides a multidisciplinary perspective on the notion of social appropriateness and its implementation into technical systems. Finally, we offer reflections on the applicability and ethics of the FASA Model, highlighting both strengths and limitations of the framework.
-
An epistemic logic for formalizing group dynamics of agents
Author(s): Stefania Costantini, Andrea Formisano and Valentina Pitonipp.: 391–426 (36)More LessAbstractIn the multi-agent setting, it is relevant to model group dynamics of agents, and logic has proved a good tool to do so. We propose an epistemic logic, L-DINF-E, that allows one to formalize what are the beliefs formed by a group of agents, where several groups exist and agents can pass from a group to another one. We introduce a new modality which allows an agent to reason about the beliefs of other agents. This allows us to model aspects of the “Theory of Mind”, understood as the set of social-cognitive skills involving the ability to attribute and reason about mental states, desires, beliefs, and knowledge of agents. In this paper, we present the logic L-DINF-E and illustrate how it can be used to solve “false-belief tasks”, i.e., tests in which an agent should understand that some other agent may develop, under some circumstances, false beliefs.
-
Learning social navigation from demonstrations with conditional neural processes
Author(s): Yigit Yildirim and Emre Ugurpp.: 427–468 (42)More LessAbstractSociability is essential for modern robots to increase their acceptability in human environments. Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation. However, social aspects of navigation are diverse, changing across different types of environments, societies, and population densities, making it unrealistic to use hand-crafted techniques in each domain. This paper presents a data-driven navigation architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn global and local controllers of the mobile robot from observations. Additionally, we leverage a state-of-the-art, deep prediction mechanism to detect situations not similar to the trained ones, where reactive controllers step in to ensure safe navigation. Our results demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data. Further, we showed that our system produces fewer personal-zone violations, causing less discomfort.
-
Towards socially-competent and culturally-adaptive artificial agents
Author(s): Chiara Bassetti, Enrico Blanzieri, Stefano Borgo and Sofia Marangonpp.: 469–512 (44)More LessAbstractThe development of artificial agents for social interaction pushes to enrich robots with social skills and knowledge about (local) social norms. One possibility is to distinguish the expressive and the functional orders during a human-robot interaction. The overarching aim of this work is to set a framework to make the artificial agent socially-competent beyond dyadic interaction – interaction in varying multi-party social situations – and beyond individual-based user personalization, thereby enlarging the current conception of “culturally-adaptive”. The core idea is to provide the artificial agent with the capability to handle different kinds of interactional disruptions, and associated recovery strategies, in microsociology. The result is obtained by classifying functional and social disruptions, and by investigating the requirements a robot’s architecture should satisfy to exploit such knowledge. The paper also highlights how this level of competence is achieved by focusing on just three dimensions: (i) social capability, (ii) relational role, and (iii) proximity, leaving aside the further complexity of full-fledged human-human interactions. Without going into technical aspects, End-to-end Data-driven Architectures and Modular Architectures are discussed to evaluate the degree to which they can exploit this new set of social and cultural knowledge. Finally, a list of general requirements for such agents is proposed.
-
Toward understanding the effects of socially aware robot behavior
Author(s): Oliver Roesler, Elahe Bagheri and Amir Alypp.: 513–552 (40)More LessAbstractA key factor for the acceptance of robots as regular partners in human-centered environments is the appropriateness and predictability of their behaviors, which depend partially on the robot behavior’s conformity to social norms. Previous experimental studies have shown that robots that follow social norms and the corresponding interactions are perceived more positively by humans than robots or interactions that do not adhere to social norms. However, the conducted studies only focused on the effects of social norm compliance in specific scenarios. Therefore, this paper aims to guide further research studies by compiling how researchers in relevant research fields think the perception of robots and the corresponding interactions are influenced independently of a specific scenario if a robot’s behavior conforms to social norms. Additionally, this study investigates what characteristics and metrics constitute a good general benchmark to objectively evaluate the behavior of social robots regarding its conformity to social norms according to researchers in relevant research communities. Finally, the paper summarizes how the obtained results can guide future research toward socially aware robot behavior.
Volumes & issues
-
Volume 25 (2024)
-
Volume 24 (2023)
-
Volume 23 (2022)
-
Volume 22 (2021)
-
Volume 21 (2020)
-
Volume 20 (2019)
-
Volume 19 (2018)
-
Volume 18 (2017)
-
Volume 17 (2016)
-
Volume 16 (2015)
-
Volume 15 (2014)
-
Volume 14 (2013)
-
Volume 13 (2012)
-
Volume 12 (2011)
-
Volume 11 (2010)
-
Volume 10 (2009)
-
Volume 9 (2008)
-
Volume 8 (2007)
-
Volume 7 (2006)
-
Volume 6 (2005)
-
Volume 5 (2004)
Most Read This Month
