Towards Social Identity in Socio-Cognitive Agents

• URL: http://arxiv.org/abs/2001.07142v1
• Date: Mon, 20 Jan 2020 15:27:26 GMT
• Title: Towards Social Identity in Socio-Cognitive Agents
• Authors: Diogo Rato, Samuel Mascarenhas, and Rui Prada
• Abstract summary: We propose a socio-cognitive agent model based on the concept of Cognitive Social Frames. Cognitive Social Frames can be built around social groups, and form the basis for social group dynamics mechanisms and construct of Social Identity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current architectures for social agents are designed around some specific units of social behaviour that address particular challenges. Although their performance might be adequate for controlled environments, deploying these agents in the wild is difficult. Moreover, the increasing demand for autonomous agents capable of living alongside humans calls for the design of more robust social agents that can cope with diverse social situations. We believe that to design such agents, their sociality and cognition should be conceived as one. This includes creating mechanisms for constructing social reality as an interpretation of the physical world with social meanings and selective deployment of cognitive resources adequate to the situation. We identify several design principles that should be considered while designing agent architectures for socio-cognitive systems. Taking these remarks into account, we propose a socio-cognitive agent model based on the concept of Cognitive Social Frames that allow the adaptation of an agent's cognition based on its interpretation of its surroundings, its Social Context. Our approach supports an agent's reasoning about other social actors and its relationship with them. Cognitive Social Frames can be built around social groups, and form the basis for social group dynamics mechanisms and construct of Social Identity.

Related papers

• Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
  Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal. We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
  arXiv  Detail & Related papers  (2024-04-17T02:57:42Z)
• Addressing Social Misattributions of Large Language Models: An HCXAI-based Approach [45.74830585715129]
  We suggest extending the Social Transparency (ST) framework to address the risks of social misattributions in Large Language Models (LLMs) LLMs may lead to mismatches between designers' intentions and users' perceptions of social attributes, risking to promote emotional manipulation and dangerous behaviors. We propose enhancing the ST framework with a fifth 'W-question' to clarify the specific social attributions assigned to LLMs by its designers and users.
  arXiv  Detail & Related papers  (2024-03-26T17:02:42Z)
• SocialBench: Sociality Evaluation of Role-Playing Conversational Agents [85.6641890712617]
  Large language models (LLMs) have advanced the development of various AI conversational agents. SocialBench is the first benchmark designed to evaluate the sociality of role-playing conversational agents at both individual and group levels. We find that agents excelling in individual level does not imply their proficiency in group level.
  arXiv  Detail & Related papers  (2024-03-20T15:38:36Z)
• SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents [107.4138224020773]
  We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and humans. In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals. We find that GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills.
  arXiv  Detail & Related papers  (2023-10-18T02:27:01Z)
• Towards socially-competent and culturally-adaptive artificial agents Expressive order, interactional disruptions and recovery strategies [0.0]
  The overarching aim of this work is to set a framework to make the artificial agent socially-competent beyond dyadic interaction-interaction. The paper highlights how this level of competence is achieved by focusing on just three dimensions: (i) social capability, (ii) relational role, and (iii) proximity.
  arXiv  Detail & Related papers  (2023-08-06T15:47:56Z)
• Social Practices: a Complete Formalization [1.370633147306388]
  We present a formalization of a social framework for agents based on the concept of Social Practices. Social practices facilitate the practical reasoning of agents in standard social interactions. They also come with a social context that gives handles for social planning and deliberation.
  arXiv  Detail & Related papers  (2022-05-22T09:58:42Z)
• Social Behaviour Understanding using Deep Neural Networks: Development of Social Intelligence Systems [2.107969466194361]
  Social computing has evolved beyond social informatics toward the birth of social intelligence systems. This paper takes initiatives to propose a social behaviour understanding framework with the use of deep neural networks for social and behavioural analysis. Three systems, including depression detection, activity recognition and cognitive impairment screening, are developed to evidently demonstrate the importance of social intelligence.
  arXiv  Detail & Related papers  (2021-05-20T03:19:55Z)
• Simulating Social Acceptability With Agent-based Modeling [28.727916976371265]
  We suggest to reframe the social space as a dynamic bundle of social practices. We outline possible research directions that focus on specific interactions among practices as well as regularities in emerging patterns.
  arXiv  Detail & Related papers  (2021-05-14T09:31:43Z)
• PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception [50.551003004553806]
  We create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions. Phase is validated with human experiments demonstrating that humans perceive rich interactions in the social events. As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE, which outperforms state-of-the-art feed-forward neural networks.
  arXiv  Detail & Related papers  (2021-03-02T18:44:57Z)
• Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents [60.27066549589362]
  Social language used by human agents is associated with greater users' responsiveness and task completion. The model uses a sequence-to-sequence deep learning architecture, extended with a social language understanding element. Evaluation in terms of content preservation and social language level using both human judgment and automatic linguistic measures shows that the model can generate responses that enable agents to address users' issues in a more socially appropriate way.
  arXiv  Detail & Related papers  (2020-12-29T08:22:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.