Social Artificial Intelligence Research Articles

Individual and team profiling to support theory of mind in artificial social intelligence

We describe an approach aimed at helping artificial intelligence develop theory of mind of their human teammates to support team interactions. We show how this can be supported through the provision of quantifiable, machine-readable, a priori information about the human team members to an agent. We first show how our profiling approach can capture individual team member characteristic profiles that can be constructed from sparse data and provided to agents to support the development of artificial theory of mind. We then show how it captures features of team composition that may influence team performance. We document this through an experiment examining factors influencing the performance of ad-hoc teams executing a complex team coordination task when paired with an artificial social intelligence (ASI) teammate. We report the relationship between the individual and team characteristics and measures related to task performance and self-reported perceptions of the ASI. The results show that individual and emergent team profiles were able to characterize features of the team that predicted behavior and explain differences in perceptions of ASI. Further, the features of these profiles may interact differently when teams work with human versus ASI advisors. Most strikingly, our analyses showed that ASI advisors had a strong positive impact on low potential teams such that they improved the performance of those teams across mission outcome measures. We discuss these findings in the context of developing intelligent technologies capable of social cognition and engage in collaborative behaviors that improve team effectiveness.

Understanding the Impact of Team Cognitive Load and Advice Compliance in Urban Search and Rescue Task

This research concentrates on the impact of Artificial Social Intelligence (ASI)'s advice intervention text messages and the compliance of three human team members, who communicate via voice to carry out victim rescue operations, on the team's performance. This experiment takes place in a simulated Urban Search and Rescue (USAR) task environment built using Minecraft. The ASI agent, utilizing its analytical components designed around interactive real-time measurements, oversees the team's behavior in the USAR task environment. The agent discerns and forecasts the human team members' actions, offering interventions via text messages to foster and sustain team efficacy. The study examines (1) the influence of the team's cognitive load on message compliance from the advisor, and (2) how these elements collectively affect team performance. The results show a positive association between cognitive load, compliance, and enhanced task performance, despite the lack of a significant correlation between cognitive load and compliance.

Transdisciplinary Team Science: Transcending Disciplines to Understand Artificial Social Intelligence in Human-Agent Teaming

We provide a transdisciplinary viewpoint on creating artificial social intelligence for human-agent teaming. We discuss theoretical, methodological, and technological insights, drawn from different disciplines, to more fully illuminate how cross-disciplinary research can inform research design and development. We unite ideas spanning human factors, cognitive and computer science, and organizational behavior. Grounding our ideas in real world challenges for human-AI teaming, and via a series of questions designed to facilitate synthesis across disciplines, we illustrate how transdisciplinary team science more effectively asks and answers complex questions on human-agent teaming. Our objective is to contribute to research and development in the field of human-AI and human-robot teaming by emphasizing a more human-centered perspective on AI.

Establishing Human Observer Criterion in Evaluating Artificial Social Intelligence Agents in a Search and Rescue Task.

Artificial social intelligence (ASI) agents have great potential to aid the success of individuals, human-human teams, and human-artificial intelligence teams. To develop helpful ASI agents, we created an urban search and rescue task environment in Minecraft to evaluate ASI agents' ability to infer participants' knowledge training conditions and predict participants' next victim type to be rescued. We evaluated ASI agents' capabilities in three ways: (a) comparison to ground truth-the actual knowledge training condition and participant actions; (b) comparison among different ASI agents; and (c) comparison to a human observer criterion, whose accuracy served as a reference point. The human observers and the ASI agents used video data and timestamped event messages from the testbed, respectively, to make inferences about the same participants and topic (knowledge training condition) and the same instances of participant actions (rescue of victims). Overall, ASI agents performed better than human observers in inferring knowledge training conditions and predicting actions. Refining the human criterion can guide the design and evaluation of ASI agents for complex task environments and team composition.

Artificial Social Intelligence: A Comparative and Holistic View

In addition to a physical comprehension of the world, humans possess a high social intelligence—the intelligence that senses social events, infers the goals and intents of others, and facilitates social interaction. Notably, humans are distinguished from their closest primate cousins by their social cognitive skills as opposed to their physical counterparts. We believe that artificial social intelligence (ASI) will play a crucial role in shaping the future of artificial intelligence (AI). This article begins with a review of ASI from a cognitive science standpoint, including social perception, theory of mind (ToM), and social interaction. Next, we examine the recently-emerged computational counterpart in the AI community. Finally, we provide an in-depth discussion on topics related to ASI.

DevianceNet: Learning to Predict Deviance from a Large-Scale Geo-Tagged Dataset

Understanding how a city’s physical appearance and environmental surroundings impact society traits, such as safety, is an essential issue in social artificial intelligence. To demonstrate the relationship, most existing studies utilize subjective human perceptual attributes, categorization only for a few violent crimes, and images taken from still shot images. These lead to difficulty in identifying location-specific characteristics for urban safety. In this work, to address this problem, we propose a large-scale dataset and a novel method by adopting a concept of “Deviance" which explains behaviors violating social norms, both formally (e.g. crime) and informally (e.g. civil complaints). We first collect a geo-tagged dataset consisting of incident report data for seven metropolitan cities, with corresponding sequential images around incident sites obtained from Google street view. We also design a convolutional neural network that learns spatio-temporal visual attributes of deviant streets. Experimental results show that our framework can reliably recognize real-world deviance in various cities. Furthermore, we analyze which visual attribute is important for deviance identification and severity estimation. We have released our dataset and source codes at our project page: https://deviance-project.github.io/DevianceNet/

Supporting Artificial Social Intelligence With Theory of Mind.

In this paper, we discuss the development of artificial theory of mind as foundational to an agent's ability to collaborate with human team members. Agents imbued with artificial social intelligence will require various capabilities to gather the social data needed to inform an artificial theory of mind of their human counterparts. We draw from social signals theorizing and discuss a framework to guide consideration of core features of artificial social intelligence. We discuss how human social intelligence, and the development of theory of mind, can contribute to the development of artificial social intelligence by forming a foundation on which to help agents model, interpret and predict the behaviors and mental states of humans to support human-agent interaction. Artificial social intelligence will need the processing capabilities to perceive, interpret, and generate combinations of social cues to operate within a human-agent team. Artificial Theory of Mind affords a structure by which a socially intelligent agent could be imbued with the ability to model their human counterparts and engage in effective human-agent interaction. Further, modeling Artificial Theory of Mind can be used by an ASI to support transparent communication with humans, improving trust in agents, so that they may better predict future system behavior based on their understanding of and support trust in artificial socially intelligent agents.

Nemesis in Mordor: The Possibility of Posthuman Savagery

Innovative computer games can sometimes serve as valid simulations of real sociocultural processes, exploring hypotheses about the possible impact of future technology on civilization. In February 2021, Warner Brothers was granted a patent for an artificial social intelligence system that it first used in Shadow of Mordor, a very popular computer game based on the legendarium of J.R.R. Tolkien, but reversing his humanistic and precautionary values. The main theme of Tolkien’s The Lord of the Rings was development of genuine friendship in a crusade to destroy a technology that gave its user superhuman powers at the cost of replacing sympathy with total selfishness. Shadow of Mordor and its successor Shadow of War promoted sadism and enslavement as tools for transcending human limitations, implicitly slandering transhumanism. This article surveys this troubling dynamic in four parts: (1) a conceptual introduction drawing upon a diverse literature about the human dimensions of current technological progress, (2) an overview of recent developments in the genre of Tolkien computer games, (3) a close examination of how the Nemesis multiagent system was designed, and (4) an initial assessment of public reactions to the Shadows expressed through videos and text comments on YouTube.

Interdisciplinary Models and Frameworks for the Study of Artificial Social Intelligence

This symposium provides a complementary set of papers exploring frameworks and models for developing artificial social intelligence (ASI) for teams. ASI consists of components of social cognition that support teamwork and more general interpersonal interactions. Although AI is rapidly evolving and fielded in a variety of operational settings, the implementation of such systems is vastly outpacing our ability to understand how to design and develop technologies appropriately. This symposium is meant to help redress this gap. Consisting of scholars representing the cognitive, computational, and organizational sciences, the papers discuss how they integrate theory and methods to inform development of agents capable of complex collaborative processes. Collectively, these papers synthesize perspectives across disciplines in support of an interdisciplinary research approach for ASL The goal is to contribute to research and development in the area of Human- AI- Robot Teaming effectiveness.

Building a Synthetic Task Environment to Support Artificial Social Intelligence Research

To support research on artificial social intelligence for successful teams (ASIST), an urban search and rescue task (USAR) was simulated within Minecraft to serve as a Synthetic Task Environment (STE). The goal for the development of the present STE was to create an environment that provides ample opportunities to allow ASI agents to demonstrate the theory of mind by making inferences and predictions of humans’ states and actions in the USAR task environment, and in the future to intervene to improve teamwork in real-time. This paper describes the STE design background, design potentials and considerations, rich data collection opportunities, and potential usage for more broad research.

Transdisciplinary Team Research to Develop Theory of Mind in Human-AI Teams Panelists

This panel will provide a transdisciplinary perspective on developing artificial social intelligence for teams. A panel with representatives from the cognitive, computational, and neural sciences will discuss theoretical, methodological, and technological insights derived from their respective disciplines. These perspectives will be integrated via a set of questions meant to guide synthesis across disciplines in support of a transdisciplinary team research approach. Through discussion across the panel and audience, our goal is to contribute to research and development in the area of Human-AI-Robot Teaming effectiveness.

IoT-Enabled Social Relationships Meet Artificial Social Intelligence

With the recent advances of the Internet of Things (IoT), and the increasing accessibility to ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as social relationships explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as artificial social intelligence (ASI) that has the potential to tackle the social relationships explosion problem. This article discusses the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

What’s the Difference? Reconciling Knowledge Structure Concepts to Aid AI Development for Human-Machine Teaming

A significant challenge for the development of artificial social intelligence for effective human-machine teams is defining the forms of artificial knowledge structures needed for machine agents to meaningfully engage in collaboration. Relevant to this, individual and shared knowledge structure concepts have been proposed across a variety of disciplines, resulting in a lack of conceptual clarity and impeding their operationalization for human-machine teaming. To reconcile conceptual differences across disciplines and enable the emergence of complex socio-cognitive abilities in machine agents, research is needed to integrate theory on the knowledge structures that underpin complex cognition. Toward this end, we survey research from the cognitive and computational sciences to develop a framework for the systematic application and evaluation of knowledge structure concepts for machine agents in teams. Our approach focuses on contextual factors, specifically the task environment structure and the situation temporality, that can help guide knowledge structure requirements for artificial social intelligence.

From Homo Sapiens to Robo Sapiens: The Evolution of Intelligence

In this paper, we present a review of recent developments in artificial intelligence (AI) towards the possibility of an artificial intelligence equal that of human intelligence. AI technology has always shown a stepwise increase in its capacity and complexity. The last step took place several years ago with the increased progress in deep neural network technology. Each such step goes hand in hand with our understanding of ourselves and our understanding of human cognition. Indeed, AI was always about the question of understanding human nature. AI percolates into our lives, changing our environment. We believe that the next few steps in AI technology, and in our understanding of human behavior, will bring about much more powerful machines that are flexible enough to resemble human behavior. In this context, there are two research fields: Artificial Social Intelligence (ASI) and General Artificial Intelligence (AGI). The authors also allude to one of the main challenges for AI, embodied cognition, and explain how it can be viewed as an opportunity for further progress in AI research.

Hybrid Avatar-Agent Technology – A Conceptual Step Towards Mediated “Social” Virtual Reality and its Respective Challenges

Abstract Driven by large industry investments, developments of Virtual Reality (VR) technologies including unobtrusive sensors, actuators and novel display devices are rapidly progressing. Realism and interactivity have been postulated as crucial aspects of immersive VR since the naissance of the concept. However, today’s VR still falls short from creating real life-like experiences in many regards. This holds particularly true when introducing the “social dimension” into the virtual worlds. Apparently, creating convincing virtual selves and virtual others and conveying meaningful and appropriate social behavior still is an open challenge for future VR. This challenge implies both, technical aspects, such as the real-time capacities of the systems, but also psychological aspects, such as the dynamics of human communication. Our knowledge of VR systems is still fragmented with regard to social cognition, although the social dimension is crucial when aiming at autonomous agents with a certain social background intelligence. It can be questioned though whether a perfect copy of real life interactions is a realistic or even meaningful goal of social VR development at this stage. Taking into consideration the specific strengths and weaknesses of humans and machines, we propose a conceptual turn in social VR which focuses on what we call “hybrid avatar-agent systems”. Such systems are required to generate i) avatar mediated interactions between real humans, taking advantage of their social intuitions and flexible communicative skills and ii) an artificial social intelligence (AIS) which monitors, and potentially moderates or transforms ongoing virtual interactions based on social signals, such as performing adaptive manipulations of behavior in intercultural conversations. The current article sketches a respective base architecture and discusses necessary research prospects and challenges as a starting point for future research and development.

Introduction. Social intelligence: from brain to culture

Humans are perhaps the most social animals. Although some eusocial insects, herd mammals and seabirds live in colonies comprising millions of individuals, no other species lives in such a variety of social groups as Homo sapiens . We live in many different sized societies, from small, nomadic hunter

Artificial social intelligence: a necessity for agent systems' developments

Often, the problems encountered by applications and developments of software agents require further investigation and improvements of agents' social capacities, that is, of the properties enabling them to act adaptively in multiagent domains. This is so for at least three reasons. First, quite often software agents are designed for reasoning upon and interacting with other agents (both human and artificial), as is the case in client-server and personal assistants. Secondly, to accomplish their primary task, these systems ought to reason upon, predict and eventually coordinate with, or at least prevent obstacles from, other systems accomplishing the same or different tasks in a concurrent way (think of electronic commerce, meeting agents, etc.). Thirdly, many applications are actually distributed over a set of agents with their specific roles and tasks, which are expected to participate in a joint activity, aimed at achieving a given global outcome.

Modelling social action for AI agents

In the new AI of the 90s an important stream is artificial social intelligence. In this work basic ontological categories for social action, structure, and mind are introduced. Sociality (social action, social structure) is let emerge from the action and intelligence of individual agents in a common world. Also some aspects of the way-down—how emergent collective phenomena shape the individual mind—are examined. First, interference and dependence are defined, and then different kinds of coordination (reactive versus anticipatory; unilateral versus bilateral; selfish versus collaborative) are characterised. “Weak social action”, based on beliefs about the mind of the other agents, and “strong social action”, based on goals about others' minds and their actions, are distinguished. Special attention is paid to Goal Delegation and Goal Adoption that are considered as the basic ingredients of social commitment and contract, and then of exchange, cooperation, group action, and organisation. Different levels of delegation and then of autonomy of the delegated agent are described; and different levels of goal-adoption are shown to characterise true collaboration. Social goals in the minds of the group members are argued to be the real glue of joint activity, and the notion of social commitment, as different from individual and from collective commitment, is underlined. The necessity for modelling social objective structures and constraints is emphasised and the “shared mind” view of groups and organisations is criticised. The spontaneous and unaware emergence of a dependence structure is explained, as well as its feedback on the participants' minds and behaviours. Critical observations are presented on current confusions such as that between “social” and “collective” action, or between communication and social action. The main claims of the paper are the following: (a) The real foundation of all sociality (cooperation, competition, groups, organisation, etc.) is the individual social action and mind. One cannot reduce or connect action at the collective level to action at the individual level unless one passes through the social character of the individual action, (b) Important levels of coordination and cooperation necessarily require minds and cognitive agents (beliefs, desires, intentions, etc.). (c) However, cognition, communication and agreement are not enough for modelling and implementing cooperation: emergent pre-cognitive structures and constraints should be formalised, and emergent forms of cooperation are needed also among planning and deliberative agents, (d) We are going towards a synthetic paradigm in AI and Cognitive Science, reconciling situatedness and plans, reactivity and mental representations, cognition, emergence and self-organisation.

Getting to know each other—Artificial social intelligence for autonomous robots

This paper proposes a research direction to study the development of ‘artificial social intelligence’ of autonomous robots which should result in ‘individualized robot societies’. The approach is highly inspired by the ‘social intelligence hypothesis’, derived from the investigation of primate societies, suggesting that primate intelligence originally evolved to solve social problems and was only later extended to problems outside the social domain. We suggest that it might be a general principle in the evolution of intelligence, applicable to both natural and artificial systems. Arguments are presented why the investigation of social intelligence for artifacts is not only an interesting research issue for the study of biological principles, but may be a necessary prerequisite for those scenarios in which autonomous robots are integrated into human societies, interacting and communicating both with humans and with each other. As a starting point to study experimentally the development of robots' ‘social relationships’, the investigation of collection and use of body images by means of imitation is proposed. A specific experimental setup which we use to test the theoretical considerations is described. The paper outlines in what kind of applications and for what kind of robot group structures social intelligence might be advantageous.

The Social Information Infrastructure

The Division of Social, Behavioral, and Economic Research of the National Science Foundation has explored aggressively the potential involvement of the social sciences in the National Information Infrastructure. We invision the NII as a global network of computer communications, which will evolve out of the Internet, linking all social scientists to massive digital libraries and to myriad smaller distributed data sources containing information of every imaginable sort. Five workshops have charted applications of high-performance computing in the social and behavioral sciences: cognitive science, computational geography, computational economics, artificial social intelligence, and electronic networks. A survey of SBER programs revealed that many are helping to create the information infrastructure, and substantial investment in six "flagship" digital library projects will develop the systems necessary for the NII of the 21st century. Keywords: information infrastructure, digital library, cognitive science, computational geography, computational economics, artificial intelligence, Internet.