Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions (2024)

Abstract: Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal that involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents (human or artificial). Progress towards Social-AI has accelerated in the past decade across several computing communities, including natural language processing, machine learning, robotics, human-machine interaction, computer vision, and speech. Natural language processing, in particular, has been prominent in Social-AI research, as language plays a key role in constructing the social world. In this position paper, we identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI. We anchor our discussion in the context of social intelligence concepts and prior progress in Social-AI research.

## introduction humans rely on social intelligence to interpret and respond to social phenomena such as empathy, rapport, collaboration, and group dynamics. social intelligence competencies that evolved over thousands of years in homo sapiens are hypothesized to have been core factors shaping human cognition and driving the emergence of language, culture, and societies @xcite @xcite @xcite . humans today continually navigate diverse social contexts, from short-term dyadic conversations to long-term relationships. virtual and embodied ai agents must have social intelligence competencies in order to function seamlessly alongside humans and other ai agents. the complexity of this vision and a set of core technical challenges for developing these agents are visualized in figure 1 . building socially-intelligent ai agents (social-ai) involves developing computational foundations for agents that can sense, perceive, reason about, learn from, and respond to affective, behavioral, and cognitive constructs of other agents (human or artificial). social-ai research interest has accelerated across computing communities in recent years, including natural language processing (nlp), machine learning (ml), robotics, human-machine interaction, computer vision, and speech (figure 2 ). we see social-ai beginning to support humans in real-world contexts. virtual text agents have stimulated empathic conversations between humans in online chatrooms @xcite , and affective signals from wearables have supported well-being @xcite . embodied social robots have supported geriatric care @xcite , motivated stroke patients @xcite , assisted youth with autism spectrum condition @xcite , improved student mental health @xcite , and collaborated with humans in manufacturing @xcite , among other prosocial applications 1 . is social-ai purely an application of ai to social contexts or do underlying technical challenges emerge that are particularly relevant to social-ai? we believe there are core technical challenges that must be addressed to advance the multidisciplinary, multimodal goal of building social-ai. our position paper is driven by the following question: what are core technical challenges, open questions, and opportunities for researchers to advance social-ai? we anchor our position in the context of social intelligence concepts, reviewed in section 2, and progress in social-ai, summarized in section 3. in section 4, we present 4 core technical challenges, along with opportunities and open questions to advance social-ai research. ## social intelligence concepts a shared understanding of social intelligence concepts is useful for researchers to contextualize progress and challenges in social-ai. we begin with the concept of social constructs -what makes entities social? social ontologists distinguish between social constructs (entities that exist by human construction) and natural kinds (entities that exist regardless of the interpretations of human minds) @xcite @xcite . for example, a person is a natural kind with physical properties, but a friend or stranger are social constructs unlinked to those physical properties. language plays a key role in forming social constructs (e.g., referring to someone as a close friend is an act that can make them a close friend) @xcite . social constructs are ontologically subjective, as their existence depends on perceptions of humans, referred to as being "perceiver-dependent" @xcite . this ontological subjectivity of constructs informs the social-ai challenges that we present in figure 1a and discuss in section 4. ## progress in social-ai research we examined social-ai research progress in 6 computing communities: nlp, ml, robotics, humanmachine interaction foot_1 , computer vision, and speech. interest in social-ai research has accelerated in the past decade, notably in nlp, ml, and robotics (figure 2 ). we synthesize key trends to provide readers with context for the discussion of core technical challenges and open questions in section 4. using the semantic scholar api @xcite , we found 3,257 relevant papers that spanned 1979-2023. for insights, we examined representative papers across communities, selected for citation count, recency, and relevance. details on search queries and filtering criteria are in appendix a. early social-ai research primarily envisioned rule-based approaches for building social intelligence competencies in agents. nlp and human-machine interaction researchers examined rule-based approaches for modeling goal-oriented communication @xcite , and dialogue @xcite . they also explored rule-based approaches for processing multimodal signals during human-machine interactions @xcite , animating embodied virtual humans as conversational agents @xcite @xcite , and interpreting potential effects of speech acts @xcite . ml and robotics researchers proposed multi-agent search and planning algorithms for social learning in groups of disembodied agents @xcite and behavior-based control for groups of robots @xcite . social robotics advanced systems for attending to social stimuli (e.g., faces) and communicating intent @xcite . in the past two decades, scientists have increasingly leveraged ml and deep learning in social-ai research. a common approach in these methods is to train models to predict social phenomena from observable human behavior, by using static datasets with ground truth labels computed as aggregations of annotator perspectives. a focus on ml for social signal processing also emerged during this era @xcite . this stimulated a focus on predicting social signals, such as laughter from speech @xcite , gesture and gait from visual cues @xcite , and engagement from visual, speech, and physiological data during human-robot interactions @xcite . there has been substantial research effort in predicting affective information, such as emotion and sentiment, from multimodal conversation signals @xcite @xcite @xcite , as well as efforts to predict social behaviors with affective information @xcite . multimodal interaction and computer vision research explored approaches for rendering virtual humans with social behavior @xcite and learning representations to model social interactions @xcite . computer vision and robotics researchers have studied ml approaches for predicting human intent to inform human-robot interactions @xcite and social navigation to improve robot navigation in spaces with humans @xcite @xcite . in parallel, researchers have focused on integrating game-theoretic and probabilistic approaches to model aspects of social intelligence, such as cooperation, competition, and theory-of-mind @xcite . approaches include stochastic games, inverse planning, and multi-agent reinforcement learning (marl) @xcite @xcite , studied through formalized games, such as public goods games @xcite , or grid-world simulations @xcite . in recent years, there have been efforts to probe the extent to which models, in particular large language models (llms), exhibit social intelligence competencies. scientists have identified strong performance by llms in procedurally-generated multi-agent gridworld interaction tasks @xcite , as well as limitations in llm social knowledge and social reasoning competencies @xcite when tested on static text benchmarks such as socialiqa @xcite , tomi @xcite , and epis-temic reasoning @xcite . these model limitations also emerge in static videoqa benchmarks such as social-iq 1.0 @xcite , social-iq 2.0 @xcite , and synthetic video benchmarks such as mmtom-qa @xcite . there have been efforts to study llm social interaction competency during goal-oriented dialogue @xcite and open-domain dialogue @xcite . recently, dynamic environments have been proposed to study social-ai agents in dyadic and multi-party settings, with sotopia @xcite , camel @xcite , and agentverse @xcite ) studying text agent interactions, coela @xcite studying embodied language agent interactions, and habitat 3.0 @xcite studying simulations of human-robot interactions. key takeaways social-ai research has made substantial progress in modeling social phenomena in static ungrounded data (abstracting away physical and social context), synthetic data, and lab-controlled social interactions. however, these data can abstract away the richness of multimodality @xcite in interactions, as well as the context-sensitivity and ambiguity inherent to social phenomena in-the-wild. we also found a focus on modeling temporally-localized phenomena (e.g., split-second communication, short-term interactions); longer-term phenomena were comparatively understudied (e.g., hours, days, years). ## core technical challenges and open questions social intelligence concepts (section 2) and prior research in social-ai foot_2 (section 3) informed our identification of 4 core technical challenges visualized in figure 1a : (c1) ambiguity in constructs, (c2) nuanced signals, (c3) multiple perspectives, (c4) agency and adaptation. we believe these challenges are particularly relevant to social-ai and must be addressed to advance social intelligence in ai agents situated across the landscape of social contexts in figure 1b 4 . in this section, we present these challenges, along with opportunities and open questions for social-ai research. ## conclusion in this position paper, we present a set of core technical challenges, along with opportunities and open questions, for advancing social-ai research across computing communities. the core technical challenges that we identify include the following: (c1) ambiguity in constructs, (c2) nuanced signals, (c3) multiple perspectives, and (c4) agency and adaptation in social agents. we believe these challenges are relevant for developing social-ai agents situated in diverse social contexts. for example, nlp researchers situating chatbots within social dialogue and robotics researchers building social robots will both encounter these 4 challenges. our research vision for building social-ai encompasses work to advance social agents with a variety of embodiments, social attributes, and social roles, with agents interacting in a range of social contexts. we conceptualize dimensions of social context and illustrate the core technical challenges in figure 1 , as a resource for readers across fields to visualize a holistic perspective of social-ai. we anchor our paper in key perspectives from the social intelligence literature (section 2) and progress in social-ai research across multiple computing communities and multiple decades (section 3). we contribute these summaries to stimulate a shared community understanding on social-ai. virtual and embodied ai systems with social intelligence have considerable potential to support human health and well-being in real-world contexts, such as homes, hospitals, and other shared spaces. for ai agents to function seamlessly alongside humans, these agents need to be endowed with social intelligence competencies. our paper is intended to inform and motivate research efforts towards ai agents with social intelligence.