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Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions (2404.11023v2)

Published 17 Apr 2024 in cs.HC, cs.CL, and cs.LG

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.

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Advancing Socially-Intelligent AI Agents Across Computing Disciplines

Introduction to Socially-Intelligent AI (Social-AI)

Social-AI seeks to emulate human social intelligence, endowing agents with abilities critical for sophisticated social interactions. These capabilities include sensing, perceiving, and responding to social cues within both human and artificial agents. Social-AI research represents a quintessential multidisciplinary effort, encompassing fields such as NLP, ML, robotics, and more. Recent years have seen a quickening pace in Social-AI advancements, indicating growing practical applications such as empathic virtual agents and social robots in elderly care.

Social Intelligence Framework

Fundamental to the paper is the elucidation of social constructs and social intelligence competencies. Social constructs—entities shaped by human interactions—are key in defining the agent's operational context. Social intelligence, initially conceptualized by early psychologists, revolves around competencies like social perception, knowledge, and reasoning. These abilities enable humans and should similarly empower AI agents to navigate complex social landscapes effectively.

The Nature of Social Constructs and Intelligence

  • Social Constructs: These are distinctions made within human social contexts, for instance, identifying relationships like friendships based solely on interpersonal interactions.
  • Social Intelligence Competencies: These include social perception (sensing relevant social cues), knowledge (understanding social norms), and reasoning (making inferences from social interactions).

Survey of Progress in Social-AI Research

The acceleration in Social-AI research is evident across various disciplines. Historical approaches were often rule-based, focusing on defining explicit patterns for agent behavior. Modern approaches, particularly with the advent of ML, leverage large datasets to predict and model social behaviors, shifting from rule-based systems to predictive analytics based on observed behavior. Notable research has involved emotion detection from multimodal signals and enhancing human-robot interactions through anticipatory algorithms.

Trends Observed:

  • Shift from Rule-based to Predictive Models: Earlier Social-AI systems relied heavily on predefined rules which have largely been supplanted by data-driven machine learning models.
  • Integration of Multimodal Data: Current research often combines data from multiple sources (visual, auditory, textual) to better predict human social signals.

Core Technical Challenges in Social-AI

The paper demarcates four principal technical challenges (C1 through C4) that need addressing to advance Social-AI:

  1. Ambiguity in Constructs (C1): Social constructs inherently display ambiguity which challenges their definition and operationalization in AI systems. Representing varying human perceptions about these constructs remains problematic.
  2. Nuanced Signals (C2): Social signals are subtle and often context-dependent. AI agents must recognize and interpret these minute variations to act appropriately within social contexts.
  3. Multiple Perspectives (C3): Social interactions incorporate multiple, often conflicting, individual perspectives. AI agents need mechanisms to understand and integrate these diverse viewpoints.
  4. Agency and Adaptation (C4): AI agents should demonstrate adaptive learning, modifying behaviors based on experiences within social interactions. This involves developing mechanisms for motivation and learning from both explicit and implicit social feedback.

Future Directions and Implications

The exploration of Social-AI, while rich with potential, is nascent and laden with complex, unresolved questions. These range from technical issues, like modeling ambiguous constructs and detecting nuanced social signals, to ethical concerns regarding privacy, bias, and trust. Continuous engagement with these challenges will be crucial as AI increasingly operates within intrinsically human domains.

Conclusion

This paper offers a comprehensive framework and identifies key challenges in Social-AI research. It elucidates the importance of interdisciplinary approaches and the integration of robust ethical practices to tackle the nuanced nature of social intelligence. As AI agents become more prevalent across various sectors, refining their social capabilities will be imperative for their success and acceptance in society.

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References (200)
  1. Social vision: Applying a social-functional approach to face and expression perception. Current Directions in Psychological Science, 26(3):243–248.
  2. Data-driven approaches in the investigation of social perception. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1693):20150367.
  3. Asif Agha. 2006. Language and social relations, volume 24. Cambridge University Press.
  4. Brenda J Allen. 2023. Difference matters: Communicating social identity. Waveland Press.
  5. Claudia Angelelli. 2000. Interpretation as a communicative event: A look through hymes’ lenses. Meta, 45(4):580–592.
  6. Sanjeev Arora and Anirudh Goyal. 2023. A theory for emergence of complex skills in language models. arXiv preprint arXiv:2307.15936.
  7. Lora Aroyo and Chris Welty. 2015. Truth is a lie: Crowd truth and the seven myths of human annotation. AI Magazine, 36(1):15–24.
  8. Janet Wilde Astington and Jennifer M Jenkins. 1995. Theory of mind development and social understanding. Cognition & Emotion, 9(2-3):151–165.
  9. Paul A Attewell and Katherine S Newman. 2010. Growing gaps: Educational inequality around the world. Oxford University Press.
  10. Robert Axelrod. 1997. Promoting Norms, pages 40–68. Princeton University Press.
  11. Power to the people? opportunities and challenges for participatory ai. Equity and Access in Algorithms, Mechanisms, and Optimization, pages 1–8.
  12. Language (technology) is power: A critical survey of" bias" in nlp. arXiv preprint arXiv:2005.14050.
  13. Envisioning communities: a participatory approach towards ai for social good. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pages 425–436.
  14. C. Breazeal and B. Scassellati. 1999a. How to build robots that make friends and influence people. In IEEE/RSJ International Conference on Intelligent Robots and Systems, volume 2, pages 858–863 vol.2.
  15. Cynthia Breazeal and Brian Scassellati. 1999b. A context-dependent attention system for a social robot. rn, 255(3).
  16. The social shapes test: A new measure of social intelligence, mentalizing, and theory of mind. Personality and Individual Differences, 143:107–117.
  17. Raymond Brueckner and Björn Schulter. 2014. Social signal classification using deep blstm recurrent neural networks. In IEEE International Conference on Acoustics, Speech and Signal processing (ICASSP), pages 4823–4827. IEEE.
  18. Measuring progress in fine-grained vision-and-language understanding. arXiv preprint arXiv:2305.07558.
  19. Nonverbal signals. Handbook of Interpersonal Communication, pages 239–280.
  20. Iemocap: Interactive emotional dyadic motion capture database. Language Resources and Evaluation, 42:335–359.
  21. Lynn Bye and Lee Jussim. 1993. A proposed model for the acquisition of social knowledge and social competence. Psychology in the Schools, 30(2):143–161.
  22. Toward a perspectivist turn in ground truthing for predictive computing. In AAAI Conference on Artificial Intelligence, volume 37, pages 6860–6868.
  23. Animated conversation: rule-based generation of facial expression, gesture & spoken intonation for multiple conversational agents. In Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, pages 413–420.
  24. Cristiano Castelfranchi. 1998. Modelling social action for ai agents. Artificial Intelligence, 103(1-2):157–182.
  25. Gaitset: Regarding gait as a set for cross-view gait recognition. In AAAI Conference on Artificial Intelligence, volume 33, pages 8126–8133.
  26. Exploring large language model based intelligent agents: Definitions, methods, and prospects. arXiv preprint arXiv:2401.03428.
  27. Intrinsically motivated reinforcement learning. Advances in Neural Information Processing Systems, 17.
  28. Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models. In IEEE/CVF International Conference on Computer Vision, pages 3043–3054.
  29. Herbert H Clark and Susan E Brennan. 1991. Grounding in communication.
  30. Michael Cohen. 2021. Exploring roberta’s theory of mind through textual entailment.
  31. New findings about social intelligence. Journal of Individual Differences.
  32. Gesture2path: Imitation learning for gesture-aware navigation. arXiv preprint arXiv:2209.09375.
  33. Can social interaction constitute social cognition? Trends in Cognitive Sciences, 14(10):441–447.
  34. Embodiment in socially interactive robots. Foundations and Trends in Robotics, 7(4):251–356.
  35. Using design metaphors to understand user expectations of socially interactive robot embodiments. arXiv preprint arXiv:2201.10671.
  36. John Dewey. 1909. Moral principles in education. Houghton Mifflin.
  37. Impact of annotator demographics on sentiment dataset labeling. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2):1–22.
  38. Cooperation structures. In International Joint Conference on Artificial Intelligence, pages 600–605. Morgan Kaufmann.
  39. Hanna Dumont and Douglas D Ready. 2023. On the promise of personalized learning for educational equity. Npj Science of Learning, 8(1):26.
  40. Social moments: a perspective on interaction for social robotics. Frontiers in Robotics and AI, 4:24.
  41. Shared reality: Experiencing commonality with others’ inner states about the world. Perspectives on Psychological Science, 4(5):496–521.
  42. Learning visual representations via language-guided sampling. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19208–19220.
  43. Clap learning audio concepts from natural language supervision. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE.
  44. Introduction. social intelligence: from brain to culture.
  45. Multi-agent planning as a dynamic search for social consensus. In IJCAI, volume 93, pages 423–429.
  46. The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing. IEEE Transactions on Affective Computing, 7(2):190–202.
  47. Ronit Feingold Polak and Shelly Levy Tzedek. 2020. Social robot for rehabilitation: expert clinicians and post-stroke patients’ evaluation following a long-term intervention. In ACM/IEEE International Conference on Human-Robot Interaction, pages 151–160.
  48. Towards transparency by design for artificial intelligence. Science and Engineering Ethics, 26(6):3333–3361.
  49. Caregiver and clinician shortages in an aging nation. In Mayo Clinic Proceedings, volume 78, pages 1026–1040. Elsevier.
  50. Noah E Friedkin and Eugene C Johnsen. 2011. Social influence network theory: A sociological examination of small group dynamics, volume 33. Cambridge University Press.
  51. Reasoning strategies explain individual differences in social reasoning. Journal of Experimental Psychology: General, 150(2):340.
  52. Xin Geng. 2016. Label distribution learning. IEEE Transactions on Knowledge and Data Engineering, 28(7):1734–1748.
  53. Characterizing sources of uncertainty to proxy calibration and disambiguate annotator and data bias. In IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pages 4202–4206. IEEE.
  54. Erving Goffman. 2016. The presentation of self in everyday life. In Social Theory Re-Wired, pages 482–493. Routledge.
  55. Erving Goffman et al. 2002. The presentation of self in everyday life. 1959. Garden City, NY, 259.
  56. Social robots in hospitals: a systematic review. Applied Sciences, 11(13):5976.
  57. Charles Goodwin. 2000. Action and embodiment within situated human interaction. Journal of Pragmatics, 32(10):1489–1522.
  58. Esther N Goody. 1995. Social intelligence and interaction: Expressions and implications of the social bias in human intelligence. Cambridge University Press.
  59. Andrew Gordon. 2016. Commonsense interpretation of triangle behavior. In AAAI Conference on Artificial Intelligence, volume 30.
  60. Ego-exo4d: Understanding skilled human activity from first-and third-person perspectives. arXiv preprint arXiv:2311.18259.
  61. Luke Guerdan and Hatice Gunes. 2022. Decentralized robot learning for personalization and privacy. arXiv preprint arXiv:2201.05527.
  62. Regiongpt: Towards region understanding vision language model. arXiv preprint arXiv:2403.02330.
  63. Don’t stop pretraining: Adapt language models to domains and tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8342–8360, Online. Association for Computational Linguistics.
  64. From hard to soft: Towards more human-like emotion recognition by modelling the perception uncertainty. In Proceedings of the 25th ACM international conference on Multimedia, pages 890–897.
  65. Michael A Hogg. 2016. Social identity theory. Springer.
  66. A simple language model for task-oriented dialogue. Advances in Neural Information Processing Systems, 33:20179–20191.
  67. Zhiting Hu. 2021. Towards Training AI Agents with All Types of Experiences: A Standardized ML Formalism. Ph.D. thesis, Carnegie Mellon University.
  68. Incorporating worker perspectives into mturk annotation practices for nlp. arXiv preprint arXiv:2311.02802.
  69. Claire Hughes and Rory T Devine. 2015. A social perspective on theory of mind. Handbook of Child Psychology and Developmental Science, pages 1–46.
  70. Social and emotional skills training with embodied moxie. arXiv preprint arXiv:2004.12962.
  71. Dell Hymes et al. 1972. Models of the interaction of language and social life.
  72. Social influence as intrinsic motivation for multi-agent deep reinforcement learning. In International Conference on Machine Learning, pages 3040–3049. PMLR.
  73. Human-centric dialog training via offline reinforcement learning. arXiv preprint arXiv:2010.05848.
  74. A robotic positive psychology coach to improve college students’ wellbeing. In IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pages 187–194. IEEE.
  75. Mmtom-qa: Multimodal theory of mind question answering. arXiv preprint arXiv:2401.08743.
  76. Averting robot eyes. Md. L. Rev., 76:983.
  77. Masahiro Kaneko and Danushka Bollegala. 2022. Unmasking the mask–evaluating social biases in masked language models. In AAAI Conference on Artificial Intelligence, volume 36, pages 11954–11962.
  78. David A Kenny and Thomas Ledermann. 2010. Detecting, measuring, and testing dyadic patterns in the actor–partner interdependence model. Journal of family psychology, 24(3):359.
  79. Muhammad Ali Khalidi. 2015. Three kinds of social kinds. Philosophy and Phenomenological Research, 90(1):96–112.
  80. Fantom: A benchmark for stress-testing machine theory of mind in interactions. arXiv preprint arXiv:2310.15421.
  81. The semantic scholar open data platform. arXiv preprint arXiv:2301.10140.
  82. Coordinate to cooperate or compete: abstract goals and joint intentions in social interaction. In CogSci.
  83. Caltech conte center, a multimodal data resource for exploring social cognition and decision-making. Scientific Data, 9(1):138.
  84. The evolutionary emergence of language: social function and the origins of linguistic form. Cambridge University Press.
  85. Hubert Knoblauch and René Tuma. 2019. Videography and video analysis. Sage research methods foundations. London: Sage.
  86. Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. Advances in Neural Information Processing Systems, 32.
  87. Sewa db: A rich database for audio-visual emotion and sentiment research in the wild. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3):1022–1040.
  88. The socialai school: Insights from developmental psychology towards artificial socio-cultural agents. arXiv preprint arXiv:2307.07871.
  89. Fine-grained recognition without part annotations. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5546–5555.
  90. Revisiting the evaluation of theory of mind through question answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5872–5877.
  91. Joint attention for multi-agent coordination and social learning. arXiv preprint arXiv:2104.07750.
  92. Survey of social bias in vision-language models. arXiv preprint arXiv:2309.14381.
  93. Modeling multimodal social interactions: New challenges and baselines with densely aligned representations.
  94. Quantifying & modeling multimodal interactions: An information decomposition framework. Advances in Neural Information Processing Systems, 36.
  95. Towards understanding and mitigating social biases in language models. In International Conference on Machine Learning, pages 6565–6576. PMLR.
  96. Hugo Liu and Push Singh. 2004. Commonsense reasoning in and over natural language. In Knowledge-Based Intelligent Information and Engineering Systems: 8th International Conference, pages 293–306. Springer.
  97. The emerging trends of multi-label learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):7955–7974.
  98. S2orc: The semantic scholar open research corpus. arXiv preprint arXiv:1911.02782.
  99. Stable bias: Evaluating societal representations in diffusion models. Advances in Neural Information Processing Systems, 36.
  100. Herbert G Lull. 1911. Moral instruction through social intelligence. American Journal of Sociology, 17(1):47–60.
  101. Dialoguernn: An attentive rnn for emotion detection in conversations. In AAAI Conference on Artificial Intelligence, volume 33, pages 6818–6825.
  102. Arthur B Markman. 1989. Lms rules and the inverse base-rate effect: Comment on gluck and bower (1988).
  103. Maja J Mataric. 1993. Designing emergent behaviors: From local interactions to collective intelligence.
  104. Maja J Mataric. 1994. Learning to behave socially. From animals to animats, 3:453–462.
  105. Socially assistive robotics for post-stroke rehabilitation. Journal of Neuroengineering and Rehabilitation, 4:1–9.
  106. George J McCall. 2003. Interaction. Handbook of Symbolic Interactionism, 322:327–348.
  107. Susan W McRoy and Graeme Hirst. 1993. Abductive explanation of dialogue misunderstandings. In Sixth Conference of the European Chapter of the Association for Computational Linguistics.
  108. Louis-Philippe Morency. 2010. Modeling human communication dynamics [social sciences]. IEEE Signal Processing Magazine, 27(5):112–116.
  109. Towards multimodal sentiment analysis: Harvesting opinions from the web. In International Conference on Multimodal Interfaces, pages 169–176.
  110. Latent-dynamic discriminative models for continuous gesture recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1–8. IEEE.
  111. Katashi Nagao and Akikazu Takeuchi. 1994. Social interaction: Multimodal conversation with social agents. In AAAI Conference on Artificial Intelligence, volume 94, pages 22–28.
  112. Clifford Nass and Youngme Moon. 2000. Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1):81–103.
  113. Language model transformers as evaluators for open-domain dialogues. In International Conference on Computational Linguistics, pages 6797–6808.
  114. David A Neequaye. 2023. Why rapport seems challenging to define and what to do about the challenge. Collabra: Psychology, 9(1).
  115. Katherine Nelson. 2003. Self and social functions: Individual autobiographical memory and collective narrative. Memory, 11(2):125–136.
  116. Learning to listen: Modeling non-deterministic dyadic facial motion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20395–20405.
  117. Dynamic probabilistic cca for analysis of affective behavior and fusion of continuous annotations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7):1299–1311.
  118. Does error-driven learning occur in the absence of cues? examination of the effects of updating connection weights to absent cues. In Proceedings of the Annual Meeting of the Cognitive Science Society, volume 44.
  119. Phases, transitions and interruptions: Modeling processes in multi-party negotiations. International Journal of Conflict Management, 14(3/4):191–211.
  120. A model-free affective reinforcement learning approach to personalization of an autonomous social robot companion for early literacy education. In AAAI Conference on Artificial Intelligence, volume 33, pages 687–694.
  121. Wellbeat: A framework for tracking daily well-being using smartwatches. IEEE Internet Computing, 24(5):10–17.
  122. Care gap: a comprehensive measure to quantify unmet needs in mental health. Epidemiology and Psychiatric Sciences, 27(5):463–467.
  123. David Pautler and Alex Quilici. 1998. A computational model of social perlocutions. In 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2, pages 1020–1026.
  124. The one where they reconstructed 3d humans and environments in tv shows. In European Conference on Computer Vision, pages 732–749. Springer.
  125. Linguistic issues in facial animation. In Computer Animation’91, pages 15–30. Springer.
  126. Generating facial expressions for speech. Cognitive science, 20(1):1–46.
  127. Improving data association by joint modeling of pedestrian trajectories and groupings. In European Conference on Computer Vision, pages 452–465. Springer.
  128. EL Pershina. 1986. Elementary contracts as a pragmatic basis of language interaction. In Coling 1986 Volume 1: The 11th International Conference on Computational Linguistics.
  129. Martin J Pickering and Simon Garrod. 2021. Understanding dialogue: Language use and social interaction. Cambridge University Press.
  130. Isabella Poggi and Francesca D’Errico. 2012. Social signals: a framework in terms of goals and beliefs. Cognitive Processing, 13:427–445.
  131. Verónica Policarpo. 2015. What is a friend? an exploratory typology of the meanings of friendship. Social Sciences, 4(1):171–191.
  132. Few-shot instruction prompts for pretrained language models to detect social biases. arXiv preprint arXiv:2112.07868.
  133. Habitat 3.0: A co-habitat for humans, avatars and robots. arXiv preprint arXiv:2310.13724.
  134. Jeffrey J Rachlinski. 1998. The limits of social norms. Chi.-Kent L. Rev., 74:1537.
  135. Constraint satisfaction processes in social reasoning. In Proceedings of the 25th Annual Cognitive Science Society, pages 964–969. Psychology Press.
  136. The aging of the baby boom and the growing care gap: A look at future declines in the availability of family caregivers, volume 12. AARP Public Policy Institute Washington, DC.
  137. Language-grounded indoor 3d semantic segmentation in the wild. In European Conference on Computer Vision, pages 125–141. Springer.
  138. Personalized machine learning for robot perception of affect and engagement in autism therapy. Science Robotics, 3(19):eaao6760.
  139. Akane Sano. 2016. Measuring college students’ sleep, stress, mental health and wellbeing with wearable sensors and mobile phones. Ph.D. thesis, Massachusetts Institute of Technology.
  140. Nlpositionality: Characterizing design biases of datasets and models. arXiv preprint arXiv:2306.01943.
  141. Social bias frames: Reasoning about social and power implications of language. arXiv preprint arXiv:1911.03891.
  142. Neural theory-of-mind? on the limits of social intelligence in large lms. arXiv preprint arXiv:2210.13312.
  143. Socialiqa: Commonsense reasoning about social interactions. arXiv preprint arXiv:1904.09728.
  144. Allison Sauppé and Bilge Mutlu. 2015. The social impact of a robot co-worker in industrial settings. In Proceedings of the 33rd annual ACM Conference on Human Factors in Computing Systems, pages 3613–3622.
  145. Robots for use in autism research. Annual Review of Viomedical Engineering, 14:275–294.
  146. Avec 2012: the continuous audio/visual emotion challenge. In ACM International Conference on Multimodal Interaction, pages 449–456.
  147. John Searle. 2010. Making the social world: The structure of human civilization. Oxford University Press.
  148. John R Searle. 1995. The construction of social reality. Simon and Schuster.
  149. John R Searle. 1998. Social ontology and the philosophy of society. Analyse & Kritik, 20(2):143–158.
  150. John R Searle. 2012. Human social reality and language. Phenomenology and Mind, (2):24–33.
  151. Clever hans or neural theory of mind? stress testing social reasoning in large language models. arXiv preprint arXiv:2305.14763.
  152. Human–ai collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nature Machine Intelligence, 5(1):46–57.
  153. Sensing, understanding, and shaping social behavior. IEEE Transactions on Computational Social Systems, 1(1):22–34.
  154. Theory of minds: Understanding behavior in groups through inverse planning. In AAAI Conference on Artificial Intelligence, volume 33, pages 6163–6170.
  155. Tokenization counts: the impact of tokenization on arithmetic in frontier llms. arXiv preprint arXiv:2402.14903.
  156. Social memory in everyday life: Recall of self-events and other-events. Journal of Personality and Social Psychology, 60(6):831.
  157. Mark Snyder and Nancy Cantor. 1980. Thinking about ourselves and others: Self-monitoring and social knowledge. Journal of Personality and Social Psychology, 39(2):222.
  158. Multimodal analysis and estimation of intimate self-disclosure. In ACM International Conference on Multimodal Interaction, pages 59–68.
  159. Samuel Lee Spaulding. 2022. Lifelong Personalization for Social Robot Learning Companions: Interactive Student Modeling Across Tasks and Over Time. Ph.D. thesis, Massachusetts Institute of Technology.
  160. Never trust anything that can think for itself, if you can’t control its privacy settings: The influence of a robot’s privacy settings on users’ attitudes and willingness to self-disclose. International Journal of Social Robotics, 15(9):1487–1505.
  161. Kim Sterelny. 2007. Social intelligence, human intelligence and niche construction. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1480):719–730.
  162. Learning the communication of intent prior to physical collaboration. In The 21st IEEE International Symposium on Robot and Human Interactive Communication, pages 968–973. IEEE.
  163. Ruth Strang. 1930. Measures of social intelligence. American Journal of Sociology, 36(2):263–269.
  164. Toward virtual humans. AI Magazine, 27(2):96–96.
  165. Beyond dirty, dangerous and dull: what everyday people think robots should do. In ACM/IEEE International Conference on Human-Robot Interaction, pages 25–32.
  166. Observer-aware legibility for social navigation. In IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pages 1115–1122. IEEE.
  167. Edward L Thorndike. 1920. Intelligence and its uses. Harper’s Magazine, 140:227–235.
  168. Robert L Thorndike and Saul Stein. 1937. An evaluation of the attempts to measure social intelligence. Psychological Bulletin, 34(5):275.
  169. Linda Tickle-Degnen and Robert Rosenthal. 1990. The nature of rapport and its nonverbal correlates. Psychological inquiry, 1(4):285–293.
  170. Sabine Trepte. 2013. Social identity theory. In Psychology of entertainment, pages 255–271. Routledge.
  171. Jonathan H Turner. 1988. A theory of social interaction. Stanford University Press.
  172. Social identity shapes social perception and evaluation. Neuroscience of prejudice and intergroup relations, 110.
  173. Linda J Van Hamme and Edward A Wasserman. 1994. Cue competition in causality judgments: The role of nonpresentation of compound stimulus elements. Learning and motivation, 25(2):127–151.
  174. Alessandro Vinciarelli and Anna Esposito. 2018. Multimodal analysis of social signals. In The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition-Volume 2, pages 203–226.
  175. Social signal processing: Survey of an emerging domain. Image and Vision Computing, 27(12):1743–1759.
  176. Bridging the gap between social animal and unsocial machine: A survey of social signal processing. IEEE Transactions on Affective Computing, 3(1):69–87.
  177. The role of physical embodiment in human-robot interaction. In IEEE International Symposium on Robot and Human Interactive Communication, pages 117–122. IEEE.
  178. Affective behavior learning for social robot haru with implicit evaluative feedback. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3881–3888. IEEE.
  179. Emergence of punishment in social dilemma with environmental feedback. In AAAI Conference on Artificial Intelligence, volume 37, pages 11708–11716.
  180. Pragmatics of human communication: A study of interactional patterns, pathologies and paradoxes. WW Norton & Company.
  181. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682.
  182. Susanne Weis. 2008. Theory and Measurement of Social Intelligence as a Cognitive Performance Construct. Ph.D. thesis.
  183. Susanne Weis and Heinz-Martin Süß. 2005. Social intelligence–a review and critical discussion of measurement concepts.
  184. Social-iq 2.0 challenge: Benchmarking multimodal social understanding.
  185. Edward O Wilson. 2012. The social conquest of earth. WW Norton & Company.
  186. Marc Wittmann. 2011. Moments in time. Frontiers in Integrative Neuroscience, 5:66.
  187. Too many cooks: Coordinating multi-agent collaboration through inverse planning. In CogSci.
  188. The ordinal nature of emotions. In International Conference on Affective Computing and Intelligent Interaction (ACII), pages 248–255. IEEE.
  189. Georgios N Yannakakis and Héctor P Martínez. 2015. Ratings are overrated! Frontiers in ICT, 2:13.
  190. Social-iq: A question answering benchmark for artificial social intelligence. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8807–8817.
  191. Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2236–2246.
  192. Leslie A Zebrowitz. 1990. Social perception. Thomson Brooks/Cole Publishing Co.
  193. Socratic models: Composing zero-shot multimodal reasoning with language. arXiv preprint arXiv:2204.00598.
  194. Language-guided human motion synthesis with atomic actions. In Proceedings of the 31st ACM International Conference on Multimedia, pages 5262–5271.
  195. Deliberating with ai: Improving decision-making for the future through participatory ai design and stakeholder deliberation. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1):1–32.
  196. Building cooperative embodied agents modularly with large language models. arXiv preprint arXiv:2307.02485.
  197. Motiongpt: Finetuned llms are general-purpose motion generators. arXiv preprint arXiv:2306.10900.
  198. Memorybank: Enhancing large language models with long-term memory. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19724–19731.
  199. Sotopia: Interactive evaluation for social intelligence in language agents. arXiv preprint arXiv:2310.11667.
  200. NormBank: A knowledge bank of situational social norms. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7756–7776, Toronto, Canada. Association for Computational Linguistics.
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Authors (3)
  1. Leena Mathur (13 papers)
  2. Paul Pu Liang (103 papers)
  3. Louis-Philippe Morency (123 papers)
Citations (3)
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