The systems of large number (>10) of agents, hereafter referred to as a multi-agent system, are crucial in a wide range of autonomy applications, including swarm robotics [1] and fleets of autonomous vehicles [2]. Inspired by collective behaviors observed in nature such as fish schools and bird flocks, these systems aim to achieve collective goals through the interaction among individual agents using a set of decentralized rules. Analytical flocking models such as Reynolds model [3] or Vicsek model [4] replicate collective behaviors observed in nature, but these models require precise localization which is rarely possible in the real-world applications. Therefore, real-time prediction of collective behavior, like how and when agents will achieve a collective goal, is essential for adapting the local rules and controlling multi-agent systems in a real-world environment [5, 6] as illustrated in Figure 1. This prediction is valuable in competitive settings like swarm herding [7], where understanding the system dynamics of adversarial agents can enhance strategic control.

Learning multi-agent dynamics is a core AI problem with broad applications in robotics and autonomous driving. While most existing works focus on deterministic prediction, producing probabilistic forecasts to quantify uncertainty and assess risks is critical for downstream decision-making tasks such as motion planning and collision avoidance. Multi-agent dynamics often contains internal symmetry. By leveraging symmetry, specifically rotation equivariance, we can improve not only the prediction accuracy but also uncertainty calibration. We introduce Energy Score, a proper scoring rule, to evaluate probabilistic predictions. We propose a novel deep dynamics model, Probabilistic Equivariant Continuous COnvolution (PECCO) for probabilistic prediction of multi-agent trajectories. PECCO extends equivariant continuous convolution to model the joint velocity distribution of multiple agents. It uses dynamics integration to propagate the uncertainty from velocity to position. On both synthetic and real-world datasets, PECCO shows significant improvements in accuracy and calibration compared to non-equivariant baselines.

Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills. However, although this ability is fundamentally related to the characteristics of human intelligence, research in this field rarely considers the processes that may have guided the emergence of complex cognitive capacities during the evolution of the species. Research in Human Behavioral Ecology (HBE) seeks to understand how the behaviors characterizing human nature can be conceived as adaptive responses to major changes in the structure of our ecological niche. In this paper, we propose a framework highlighting the role of environmental complexity in open-ended skill acquisition, grounded in major hypotheses from HBE and recent contributions in Reinforcement learning (RL). We use this framework to highlight fundamental links between the two disciplines, as well as to identify feedback loops that bootstrap ecological complexity and create promising research directions for AI researchers.

MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network Priyabrata Saha, Arslan Ali, Burhan A. Mudassar, Y un Long and Saibal Mukhopadhyay Abstract -- We present the MagNet, a multi-agent interaction network to discover governing dynamics and predict evolution of a complex system from observations. We formulate a multi-agent system as a coupled nonlinear network with a generic ordinary differential equation (ODE) based state evolution, and develop a neural network based realization of its time-discretized model. MagNet is trained to discover the core dynamics of a multi-agent system from observations, and tuned online to learn agent-specific parameters of the dynamics to ensure accurate prediction even when physical or relational attributes of agents, or number of agents change. We evaluate MagNet on point-mass system in two-dimensional space, Ku-ramoto phase synchronization dynamics and predator-swarm interaction dynamics demonstrating orders of magnitude improvement in prediction accuracy over traditional deep learning models. I NTRODUCTION Multi-agent systems are prevalent in both the natural world and engineered world. Engineered distributed systems of mobile robots, multiple sensors, unmanned aerial vehicles etc. often take inspiration from natural multi-agent systems like swarms, schools, flocks, and herds of social animals or birds. Understanding the behavior of such natural or engineered multi-agent systems from sensory observations is a key challenge in robotics from the design and adversarial perspective. Discovering the hidden dynamics of a multi-agent interaction from observations will enable machines to simulate and predict evolution of complex systems.