AIMC Topic: Cooperative Behavior

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Understanding dimensions of trust in AI through quantitative cognition: Implications for human-AI collaboration.

PloS one
Human-AI collaborative innovation relies on effective and clearly defined role allocation, yet empirical research in this area remains limited. To address this gap, we construct a cognitive taxonomy trust in AI framework to describe and explain its i...

Mapping the landscape: A bibliometric analysis of AI and teacher collaboration in educational research.

F1000Research
BACKGROUND: This study intends to investigate the relationship between artificial intelligence and teachers' collaboration in educational research in response to the growing use of technologies and the current status of the field.

Human-generative AI collaboration enhances task performance but undermines human's intrinsic motivation.

Scientific reports
In a series of four online experimental studies (total N = 3,562), we investigated the performance augmentation effect and psychological deprivation effect of human-generative AI (GenAI) collaboration in professional settings. Our findings consistent...

CoHet4Rec: A recommendation for collaborative heterogeneous information networks.

PloS one
Recommender Systems (RS) aim to predict users' latent interests in items by learning embeddings from user-item graphs. Graph Neural Networks (GNNs) have significantly advanced RS by enabling the embedding of graph-structured data. However, relying so...

Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem.

Nature communications
A canonical social dilemma arises when resources are allocated to people, who can either reciprocate with interest or keep the proceeds. The right resource allocation mechanisms can encourage levels of reciprocation that sustain the commons. Here, in...

Hierarchical task network-enhanced multi-agent reinforcement learning: Toward efficient cooperative strategies.

Neural networks : the official journal of the International Neural Network Society
Navigating multi-agent reinforcement learning (MARL) environments with sparse rewards is notoriously difficult, particularly in suboptimal settings where exploration can be prematurely halted. To tackle these challenges, we introduce Hierarchical Sym...

Advances in the application of human-machine collaboration in healthcare: insights from China.

Frontiers in public health
In the context of the technological revolution and the digital intelligence era, the contradiction between the rising incidence of diseases and the uneven distribution of quality medical resources is highlighted, and the diagnosis and prevention of d...

QTypeMix: Enhancing multi-agent cooperative strategies through heterogeneous and homogeneous value decomposition.

Neural networks : the official journal of the International Neural Network Society
In multi-agent cooperative tasks, the presence of heterogeneous agents is familiar. Compared to cooperation among homogeneous agents, collaboration requires considering the best-suited sub-tasks for each agent. However, the operation of multi-agent s...

A fully value distributional deep reinforcement learning framework for multi-agent cooperation.

Neural networks : the official journal of the International Neural Network Society
Distributional Reinforcement Learning (RL) extends beyond estimating the expected value of future returns by modeling its entire distribution, offering greater expressiveness and capturing deeper insights of the value function. To leverage this advan...

Learning performance and physiological feedback-based evaluation for human-robot collaboration.

Applied ergonomics
The development of Industry 4.0 has resulted in tremendous transformations in the manufacturing sector to supplement the human workforce through collaboration with robots. This emphasis on a human-centered approach is a vital aspect in promoting resi...