Board, card or video games have been played by virtually every individual in the world.
Games are popular because they are intuitive and fun. These distinctive qualities of games …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL),
covering MARL's models, solution concepts, algorithmic ideas, technical challenges, and …

Collaborating with humans requires rapidly adapting to their individual strengths,
weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement …

Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess
generalization to novel situations as their primary objective (unlike supervised learning …

Scaling high-quality tutoring remains a major challenge in education. Due to growing
demand, many platforms employ novice tutors who, unlike experienced educators, struggle …

Social interaction is both ubiquitous and central to understanding human behavior. Such
interactions depend, we argue, on shared intentionality: the parties must form a common …

Synchronizing decisions across multiple agents in realistic settings is problematic since it
requires agents to wait for other agents to terminate and communicate about termination …

Given the popular presupposition of human reasoning as the standard for learning and
decision making, there have been significant efforts and a growing trend in research to …

With the recent development of deep learning, AI models are widely used in various
domains. AI models show good performance for definite tasks such as image classification …