Neural Information Processing Systems http://nips.cc/

Here we adopt the hypothesis that when we are in an interactive social setting, our brains perform Bayesian inference of the intentions and cooperativeness of others using probabilistic representations.

The growing popularity of group activities increased the need to develop methods for providing recommendations to a group of users based on the collective preferences of the group members. Several group recommender systems have been proposed, but these methods often struggle due to sparsity and high-dimensionality of the available data, common in many real-world applications. In this paper, we propose a group recommender system called Group Soft-Impute SVD, which leverages soft-impute singular value decomposition to enhance group recommendations. This approach addresses the challenge of sparse high-dimensional data using low-rank matrix completion. We compared the performance of Group Soft-Impute SVD with Group MF based approaches and found that our method outperforms the baselines in recall for small user groups while achieving comparable results across all group sizes when tasked on Goodbooks, Movielens, and Synthetic datasets. Furthermore, our method recovers lower matrix ranks than the baselines, demonstrating its effectiveness in handling high-dimensional data.

The theory describes four physical zones (or territories) defined by growing distances around each person, as can be seen in Figure 3 (top left). With those hidden unwritten rules for spaces around a person, only socially close people are welcome within the intimate zone, while generally close people can enter the personal zone, followed by generally familiar people who are allowed in the social space. Otherwise, general public are only permitted within the public space. The concept of group proxemics has been investigated in literature with most attention being paid to detailing the classical proxemics theory. For instance, the authors of [14] explored proxemics and their impact on shape of group formation, the authors of [2] explored proxemics dispersion as average distances people maintain between each other as they walk in group, and in [18], [19] focus was given to studying the effect of proxemics on crowd and its traffic flow dynamics. Within robot-human interactions, the authors of [20]-[22] studied appropriate (safety, comfort, acceptability, etc) distance robots are expected to maintain from people (as individuals). It could be noticed that proxemics are structured around interactions between individuals and details are specified in terms of social relationships between them. In what follows, we explore the situation when an individual is part of a bigger and more complex social entity such as a group. We study the nature of such interactions and and explore associated proxemics.

This article investigates the pedestrian group as an emergent agent. The article explores empirical data to derive emergent agency and formation state spaces and outline recurring patterns of walking behavior. In this analysis, pedestrian trajectories extracted from surveillance videos are used along with manually annotated pedestrian group memberships. We conducted manual expert evaluation of observed groups, produced new manual annotations for relevant events pertaining to group behavior and extracted metrics relevant group formation. This information along with quantitative analysis was used to model the life-cycle and formation of the group agent. Those models give structure to expectations around walking behavior of groups; from pedestrian walking independently to the emergence of a collective intention where group members tended to maintain bounded distance between each other. Disturbances to this bounded distance often happened in association with changes in either their agency or their formation states. We summarized the patterns of behavior along with the sequences of state transitions into abstract patterns, which can aid in the development of more detailed group agents in simulation and in the design of engineering systems to interact with such groups.

The inference of others' intentions is called "theory of mind"

This study investigated whether robotic agents that deal with social hierarchical relationships can reduce the dominance of superiors and equalize participation among participants in discussions with hierarchical structures. Thirty doctors and students having hierarchical relationship were gathered as participants, and an intervention experiment was conducted using a robot that can encourage participants to speak depending on social hierarchy. These were compared with strategies that intervened equally for all participants without considering hierarchy and with a no-action. The robots performed follow actions, showing backchanneling to speech, and encourage actions, prompting speech from members with less speaking time, on the basis of the hierarchical relationships among group members to equalize participation. The experimental results revealed that the robot's actions could potentially influence the speaking time among members, but it could not be conclusively stated that there were significant differences between the robot's action conditions. However, the results suggested that it might be possible to influence speaking time without decreasing the satisfaction of superiors. This indicates that in discussion scenarios where experienced superiors are likely to dominate, controlling the robot's backchanneling behavior could potentially suppress dominance and equalize participation among group members.

Generative agents have made significant progress in simulating human behavior, but existing frameworks often simplify emotional modeling and focus primarily on specific tasks, limiting the authenticity of the simulation. Our work proposes the Psychological-mechanism Agent (PSYA) framework, based on the Cognitive Triangle (Feeling-Thought-Action), designed to more accurately simulate human behavior. The PSYA consists of three core modules: the Feeling module (using a layer model of affect to simulate changes in short-term, medium-term, and long-term emotions), the Thought module (based on the Triple Network Model to support goal-directed and spontaneous thinking), and the Action module (optimizing agent behavior through the integration of emotions, needs and plans). To evaluate the framework's effectiveness, we conducted daily life simulations and extended the evaluation metrics to self-influence, one-influence, and group-influence, selection five classic psychological experiments for simulation. The results show that the PSYA framework generates more natural, consistent, diverse, and credible behaviors, successfully replicating human experimental outcomes. Our work provides a richer and more accurate emotional and cognitive modeling approach for generative agents and offers an alternative to human participants in psychological experiments.

In contrast to single-user recommender systems, group recommender systems are designed to generate and explain recommendations for groups. This group-oriented setting introduces additional complexities, as several factors - absent in individual contexts - must be addressed. These include understanding group dynamics (e.g., social dependencies within the group), defining effective decision-making processes, ensuring that recommendations are suitable for all group members, and providing group-level explanations as well as explanations for individual users. In this paper, we analyze in which way large language models (LLMs) can support these aspects and help to increase the overall decision support quality and applicability of group recommender systems.