رؤى - Human-Computer Interaction - # Developing Socially-Intelligent AI Agents

Advancing Social Intelligence in AI Agents: Addressing Technical Challenges and Open Questions

Q: How can Social-AI models be designed to flexibly represent and reason about the ambiguity inherent in social constructs, rather than relying on predefined static labels?

In designing Social-AI models to handle the ambiguity in social constructs, a key approach is to leverage natural language as an expressive modality to capture and represent nuanced social phenomena. By allowing for richer natural language descriptions of social constructs, models can better capture the subjective and dynamic nature of social interactions. Instead of relying on predefined static labels, researchers can explore the use of flexible and dynamic label spaces that adjust during training and inference based on the ambiguity present in social constructs. This approach enables models to represent a wider range of interpretations and perceptions of social phenomena, aligning more closely with the inherently subjective nature of social constructs. Furthermore, incorporating uncertainty into the modeling process can help address ambiguity in social constructs. By acknowledging and modeling the uncertainty in annotator ratings and actor interpretations of social signals, models can better capture the diverse perspectives and nuances present in social interactions. This can involve exploring ordinal representations for emotion annotation, evaluating perception uncertainty across annotators, and developing frameworks to handle varied label distributions in social contexts.

Q: How might Social-AI agents be endowed with the capacity to dynamically update their understanding of other actors' evolving perspectives during an interaction, and how can this inform their own behavior adaptation?

To enable Social-AI agents to dynamically update their understanding of evolving perspectives during interactions, models can be designed to incorporate mechanisms for multi-perspective reasoning and social memory. By considering the interdependence of actors' perspectives and the influence they have on each other, agents can develop a more comprehensive understanding of social dynamics. This can involve creating joint models that represent the perspectives of all actors in an interaction, allowing agents to adapt their behavior based on the changing social context. One approach is to leverage theories of social influence and social identity to inform agent behavior. By modeling how actors influence and are influenced by each other's perspectives over time, agents can adapt their behavior to align with mutual social expectations and achieve long-term social goals. Shared social memory between agents and other actors can facilitate the creation of common ground and help agents operate in ways that are consistent with social norms and expectations.

Q: What are the limitations of current machine learning approaches in capturing the nuanced, interleaved, and cross-modal nature of social signals, and how can new frameworks address these limitations?

Current machine learning approaches face limitations in capturing the nuanced, interleaved, and cross-modal nature of social signals due to their reliance on predefined datasets with static labels and limited representations of social context. These approaches often struggle to capture the complexity and ambiguity inherent in social interactions, as well as the dynamic and context-dependent nature of social signals. New frameworks can address these limitations by incorporating more flexible and dynamic modeling techniques that can adapt to the evolving nature of social signals. This can involve exploring multi-modal models that operate on verbal and non-verbal information to interpret the nuances of cross-actor and cross-modal interaction patterns. By integrating natural language as an intermediate representation for processing nuanced social signals, models can better capture the richness and complexity of social interactions. Additionally, frameworks that focus on learning from implicit cues and the absence of signals can help agents perceive and interpret social signals more effectively. By developing mechanisms to recognize and learn from subtle cues, such as unsaid words, omitted gestures, and silences, models can enhance their understanding of social dynamics and adapt their behavior accordingly. Incorporating these aspects into new frameworks can improve the ability of Social-AI agents to capture the nuanced and interleaved nature of social signals.

المفاهيم الأساسية

Building AI agents with social intelligence competencies, including social perception, knowledge, memory, reasoning, creativity, and interaction, is a core technical challenge that requires addressing ambiguity in social constructs, nuanced social signals, multiple perspectives, and agent agency and adaptation.

الملخص

This position paper identifies four core technical challenges and open questions for advancing social intelligence in AI agents (Social-AI):

(C1) Ambiguity in Constructs: Social constructs have inherent ambiguity in their definition and interpretation. Researchers must explore methods to represent this ambiguity, such as using flexible natural language label spaces instead of predefined static labels.

(C2) Nuanced Signals: Social signals can be highly nuanced, with small changes leading to large shifts in meaning. Advancing the ability to process fine-grained multimodal social signals, including recognizing the absence of cues, is an open challenge.

(C3) Multiple Perspectives: Actors in social interactions bring their own evolving perspectives, experiences, and roles, which can interdependently influence each other. Developing models that can reason over these dynamic, concurrent multiple perspectives is crucial.

(C4) Agency and Adaptation: Social-AI agents must be goal-oriented, learning from both explicit and implicit social signals to adapt their behavior. Researchers must create mechanisms for agents to estimate success in achieving social goals and build shared social memory with other actors.

Addressing these challenges will require advances across computing communities, including natural language processing, machine learning, robotics, human-computer interaction, computer vision, and speech. Participatory AI frameworks, mitigating social biases, and preserving user privacy are also important considerations for developing ethical and trustworthy Social-AI systems.

تخصيص الملخص

إعادة الكتابة بالذكاء الاصطناعي

إنشاء الاستشهادات

ترجمة المصدر

إلى لغة أخرى

إنشاء خريطة ذهنية

من محتوى المصدر

زيارة المصدر

arxiv.org

الإحصائيات

"Social intelligence competencies that evolved in early Homo sapiens are hypothesized to have been core factors shaping human cognition and driving the emergence of language, culture, and societies."
"Virtual and embodied AI agents must have social intelligence competencies in order to function seamlessly alongside humans and other AI agents."
"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."

اقتباسات

"Social constructs have inherent ambiguity in their definition and interpretation in the social world."
"Social signals can be nuanced, often manifesting through different degrees of synchrony across actors and modalities."
"Actors bring their own perspectives, experiences, and roles; these factors can change over time and influence the perspectives of other actors during interactions."
"Social-AI agents must have the capacity to be goal-oriented, often targeting multiple goals simultaneously."

الرؤى الأساسية المستخلصة من

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

by Leena Mathur... في arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11023.pdf

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

استفسارات أعمق

How can Social-AI models be designed to flexibly represent and reason about the ambiguity inherent in social constructs, rather than relying on predefined static labels?

In designing Social-AI models to handle the ambiguity in social constructs, a key approach is to leverage natural language as an expressive modality to capture and represent nuanced social phenomena. By allowing for richer natural language descriptions of social constructs, models can better capture the subjective and dynamic nature of social interactions. Instead of relying on predefined static labels, researchers can explore the use of flexible and dynamic label spaces that adjust during training and inference based on the ambiguity present in social constructs. This approach enables models to represent a wider range of interpretations and perceptions of social phenomena, aligning more closely with the inherently subjective nature of social constructs.
Furthermore, incorporating uncertainty into the modeling process can help address ambiguity in social constructs. By acknowledging and modeling the uncertainty in annotator ratings and actor interpretations of social signals, models can better capture the diverse perspectives and nuances present in social interactions. This can involve exploring ordinal representations for emotion annotation, evaluating perception uncertainty across annotators, and developing frameworks to handle varied label distributions in social contexts.

How might Social-AI agents be endowed with the capacity to dynamically update their understanding of other actors' evolving perspectives during an interaction, and how can this inform their own behavior adaptation?

To enable Social-AI agents to dynamically update their understanding of evolving perspectives during interactions, models can be designed to incorporate mechanisms for multi-perspective reasoning and social memory. By considering the interdependence of actors' perspectives and the influence they have on each other, agents can develop a more comprehensive understanding of social dynamics. This can involve creating joint models that represent the perspectives of all actors in an interaction, allowing agents to adapt their behavior based on the changing social context.
One approach is to leverage theories of social influence and social identity to inform agent behavior. By modeling how actors influence and are influenced by each other's perspectives over time, agents can adapt their behavior to align with mutual social expectations and achieve long-term social goals. Shared social memory between agents and other actors can facilitate the creation of common ground and help agents operate in ways that are consistent with social norms and expectations.

What are the limitations of current machine learning approaches in capturing the nuanced, interleaved, and cross-modal nature of social signals, and how can new frameworks address these limitations?

Current machine learning approaches face limitations in capturing the nuanced, interleaved, and cross-modal nature of social signals due to their reliance on predefined datasets with static labels and limited representations of social context. These approaches often struggle to capture the complexity and ambiguity inherent in social interactions, as well as the dynamic and context-dependent nature of social signals.
New frameworks can address these limitations by incorporating more flexible and dynamic modeling techniques that can adapt to the evolving nature of social signals. This can involve exploring multi-modal models that operate on verbal and non-verbal information to interpret the nuances of cross-actor and cross-modal interaction patterns. By integrating natural language as an intermediate representation for processing nuanced social signals, models can better capture the richness and complexity of social interactions.
Additionally, frameworks that focus on learning from implicit cues and the absence of signals can help agents perceive and interpret social signals more effectively. By developing mechanisms to recognize and learn from subtle cues, such as unsaid words, omitted gestures, and silences, models can enhance their understanding of social dynamics and adapt their behavior accordingly. Incorporating these aspects into new frameworks can improve the ability of Social-AI agents to capture the nuanced and interleaved nature of social signals.