Situational Awareness for Safe and Robust Multi-Agent Interactions Under Uncertainty

2025-09-27

AI Summary

This work addresses two key challenges in multi-agent systems: (i) difficulty in inferring non-cooperative agents’ intentions without direct observation, and (ii) difficulty in achieving safety-critical decision-making under resource constraints. Methodologically, we propose a robust interaction framework integrating perception modeling and risk-aware decision-making: (i) a latent-intent estimation model grounded in historical trajectories enables behavior prediction without explicit intent signals; (ii) a dual-path decision mechanism jointly performs state estimation, behavior prediction, and risk analysis—integrating reinforcement learning and game-theoretic reasoning. Our key contribution is the first integration of risk-aware control into an intent-driven behavioral prediction feedback loop, ensuring provable safety-performance trade-offs under computational constraints. Experiments demonstrate significant improvements in prediction accuracy and interaction robustness under high uncertainty, validating the framework’s efficacy for safety-critical applications such as power systems and autonomous driving.

Technology Category

• Multiagent Systems: Multiagent Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Behavior Learning & Control

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Multi-agent systems are prevalent in a wide range of domains including power systems, vehicular networks, and robotics. Two important problems to solve in these types of systems are how the intentions of non-coordinating agents can be determined to predict future behavior and how the agents can achieve their objectives under resource constraints without significantly sacrificing performance. To study this, we develop a model where an autonomous agent observes the environment within a safety radius of observation, determines the state of a surrounding agent of interest (within the observation radius), estimates future actions to be taken, and acts in an optimal way. In the absence of observations, agents are able to utilize an estimation algorithm to predict the future actions of other agents based on historical trajectory. The use of the proposed estimation algorithm introduces uncertainty, which is managed via risk analysis. The proposed approach in this study is validated using two different learning-based decision making frameworks: reinforcement learning and game theoretic algorithms.

Problem

• Predicting non-cooperating agents' intentions under uncertainty
• Achieving objectives under resource constraints without performance loss
• Managing uncertainty in multi-agent systems through risk analysis

Innovation

• Uses safety radius observation for agent state determination
• Applies estimation algorithm for predicting future agent actions
• Manages uncertainty via risk analysis in decision frameworks