Inferring Short-Sightedness in Dynamic Noncooperative Games

2024-12-02

arXiv.org

AI Summary

In dynamic non-cooperative games involving multi-agent systems (e.g., pedestrians, vehicles, robots), heterogeneous foresight capabilities among agents introduce significant biases in behavioral prediction. Method: This paper proposes an inverse dynamic game framework that, for the first time, models agent foresight as a learnable continuous parameter. We formulate a parametricized mixed complementarity problem (MCP) and design an efficient, directionally differentiable solver—thereby overcoming the limitations of conventional fixed-foresight assumptions. Contribution/Results: Our approach introduces three key innovations: (i) parametric modeling of foresight, (ii) a directionally differentiable MCP-based inverse solving paradigm, and (iii) online inference capability tailored to foresight heterogeneity. Evaluated on real-world intersection trajectory data, the method achieves a 33% improvement in behavioral prediction accuracy over fixed-foresight baselines, substantially enhancing safety and decision efficiency in human–machine interaction.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Dynamic game theory is an increasingly popular tool for modeling multi-agent, e.g. human-robot, interactions. Game-theoretic models presume that each agent wishes to minimize a private cost function that depends on others' actions. These games typically evolve over a fixed time horizon, specifying how far into the future each agent plans. In practical settings, however, decision-makers may vary in foresightedness. We conjecture that quantifying and estimating each agent's foresightedness from online data will enable safer and more efficient interactions with other agents. To this end, we frame this inference problem as an emph{inverse} dynamic game. We consider a specific parametrization of each agent's objective function that smoothly interpolates myopic and farsighted planning. Games of this form are readily transformed into parametric mixed complementarity problems; we exploit the directional differentiability of solutions to these problems with respect to their hidden parameters to solve for agents' foresightedness. We conduct two types of experiments: one with synthetically generated pedestrian motion at a crosswalk and the other with real-world intersection data involving people walking, biking, and driving vehicles. The results of these experiments demonstrate that explicitly inferring agents' foresightedness enables game-theoretic models to more accurately model agents' behavior. Specifically, our results show 33% more accurate prediction of foresighted behavior on average compared to the baseline in real-world scenarios.

Problem

• Estimating agents' foresightedness in dynamic games
• Improving accuracy of game-theoretic behavior models
• Enhancing human-robot interaction safety and efficiency

Innovation

• Inverse dynamic game for foresightedness inference
• Parametric mixed complementarity problem transformation
• Directional differentiability for solution analysis