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.
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. This problem motivated many research directions on embodied language use. Current approaches focus on language as a communication tool in very simplified and non diverse social situations: the "naturalness" of language is reduced to the concept of high vocabulary size and variability. In this paper, we argue that aiming towards human-level AI requires a broader set of key social skills: 1) language use in complex and variable social contexts; 2) beyond language, …
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. Within the Deep Reinforcement Learning (DRL) field, this objective motivated multiple works on embodied language use. However, current approaches focus on language as a communication tool in very simplified and non-diverse social situations: the "naturalness" of language is reduced to the concept of high vocabulary size and variability. In this paper, we argue that aiming towards human-level AI requires a broader set of key social skills: 1) language use in compl…
AGH University of Science and Technology（AGH科技大学）；Tomas Bata University in Zlín（兹林托马斯·巴塔大学）
Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model. Besides giving the background and the details of the proposed algorithms we apply them to optimization of selected variants of the problem and discuss the results.
INRIA Bordeaux – Sud-Ouest（法国国家信息与自动化研究所波尔多-西南分部）；Université de Bordeaux（波尔多大学）；ENSTA Paris（巴黎高等先进技术学院）；Microsoft Research（微软研究院）
Building autonomous agents able to grow open-ended repertoires of skills across their lives is a fundamental goal of artificial intelligence (AI). A promising developmental approach recommends the design of intrinsically motivated agents that learn new skills by generating and pursuing their own goals - autotelic agents. But despite recent progress, existing algorithms still show serious limitations in terms of goal diversity, exploration, generalisation or skill composition. This perspective calls for the immersion of autotelic agents into rich socio-cultural worlds, an immensely important at…
Analyzing Cognitive Differences Among Large Language Models through the Lens of Social Worldview
Wangxuan Institute of Computer Technology, Peking University（北京大学计算机技术研究院）；Information Management Department, Peking University（北京大学信息管理系）；Renmin University of China（中国人民大学）
From Bias Mitigation to Bias Negotiation: Governing Identity and Sociocultural Reasoning in Generative AI
Institute for Social Science, University of Stuttgart（斯图加特大学社会科学研究所）；Department of Computer Science, Saarland University（萨尔兰大学计算机科学系）；Department of Linguistics, University of Texas Austin TX USA（德克萨斯大学语言学系）；Department of Linguistics, University of Texas（德克萨斯大学语言学系）
Spatial Cognition Research Center (SFB/TR 8)（空间认知研究中心（SFB/TR 8））；University of Bremen（不来梅大学）
We construe smart meeting cinematography with a focus on professional situations such as meetings and seminars, possibly conducted in a distributed manner across socio-spatially separated groups. The basic objective in smart meeting cinematography is to interpret professional interactions involving people, and automatically produce dynamic recordings of discussions, debates, presentations etc in the presence of multiple communication modalities. Typical modalities include gestures (e.g., raising one's hand for a question, applause), voice and interruption, electronic apparatus (e.g., pressing …
Causal Autoencoder-like Generation of Feedback Fuzzy Cognitive Maps with an LLM Agent
Department of Electrical and Computer Engineering（电气与计算机工程系）；University of Southern California（南加州大学）；School of Computing and Information Sciences（计算与信息科学学院）；Florida International University（佛罗里达国际大学）
Brown University（布朗大学）；Dalhousie University（达尔豪斯大学）
Cultural code-switching concerns how we adjust our overall behaviours, manners of speaking, and appearance in response to a perceived change in our social environment. We defend the need to investigate cultural code-switching capacities in artificial intelligence systems. We explore a series of ethical and epistemic issues that arise when bringing cultural code-switching to bear on artificial intelligence. Building upon Dotson's (2014) analysis of testimonial smothering, we discuss how emerging technologies in AI can give rise to epistemic oppression, and specifically, a form of self-silencing…
Arup（奥雅纳）；University of Kent（肯特大学）；University of Oxford（牛津大学）；University of Southampton（南安普顿大学）
The concept of social machines is increasingly being used to characterise various socio-cognitive spaces on the Web. Social machines are human collectives using networked digital technology which initiate real-world processes and activities including human communication, interactions and knowledge creation. As such, they continuously emerge and fade on the Web. The relationship between humans and machines is made more complex by the adoption of Internet of Things (IoT) sensors and devices. The scale, automation, continuous sensing, and actuation capabilities of these devices add an extra dimen…
