This workshop focuses on the role of decision-making and learning in human-multi-agent cooperation, viewed through the lens of cognitive modeling. AI technologies, particularly in human-robot interaction, are increasingly focused on cognitive modeling, encompassing everything from visual processing to symbolic reasoning and action recognition. These capabilities support human-agent cooperation in complex tasks.

Natural agents, like humans, often make decisions based on a blend of biological, social, and cognitive motivations, as elucidated by combined motivations' model like Maslow’s Hierarchy of Needs and Alderfer’s Existence-Relatedness-Growth (ERG) theory. On the other hand, the AI agent can be regarded as a self-organizing system that also presents various needs and motivations in its evolution through decision-making and learning to adapt to different scenarios and satisfy their needs. Combined with AI agent capacity to aid decision-making, it opens up new horizons in human-multi-agent collaboration. This potential is crucially essential in the context of interactions between human agents and intelligent agents, when considering to establish stable and reliable relationships in their cooperation, particularly in adversarial and rescue mission environments.

This workshop will bring together researchers from multi-agent systems (MAS) and human-robot interaction (HRI) and aims to advance the field by exploring how cognitive science, mathematical modeling, statistical analysis, software simulations, and hardware demonstrations can help answer critical questions about decision-making and learning in these cooperative environments. The goal is to understand better how cognitive modeling can enhance cooperation between humans and AI agents, especially in complex, high-stakes scenarios.

Topics

We solicit contributions from topics including but not limited to:

• Human-multi-agent cognitive modeling
• Human-multi-agent trust networks
• Trustworthy AI agents in Human-robot interaction
• Trust based Human-MAS decision-making and learning
• Consensus in Human-MAS collaboration
• Intrinsically motivated AI agent modeling in Human-MAS
• Innate-values-driven reinforcement learning
• Multi-Objective MAS decision-making and learning
• Adaptive learning with social rewards
• Cognitive models in swarm intelligence and robotics
• Game-theoretic approaches in MAS decision-making
• Cognitive model application in intelligent social systems