用向量代价博弈优化机器人多目标决策，比传统加权法更鲁棒可解释。
Vector Cost Behavioral Planning for Autonomous Robotic Systems with Contemporary Validation Strategies
- 引入向量代价双矩阵博弈，支持任意数量目标的联合优化。
- 在对抗性运动规划中，性能显著优于传统加权求和方法。
- 结合可解释AI与参数空间探索，提升方法可理解性与泛化能力。
向量代价双矩阵博弈是一种多目标决策方法，使自主机器人系统能够同时优化多个目标，避免忽略目标带来的最坏情况。本文将该方法扩展至任意数量目标，并在对抗性运动规划中与标量加权求和方法进行对比。利用可解释人工智能（XAI）软件分析高维决策数据，采用状态空间多维边界遵循策略探索（SEMBAS）对参数空间进行敏感性分析，评估基线与新框架的性能模式。尽管已有研究分别探讨过博弈论规划与智能系统验证，本文首次将其整合为一个新颖且全面的仿真流程。该集成方案显著提升了向量代价方法的表现，提供了一种可解释、通用的机器人行为规划框架。代码见https://github.com/toazbenj/race_simulation，视频演示见https://tinyurl.com/vectorcostvideo。
原文摘要 · Abstract (English)
The vector cost bimatrix game is a method for multi-objective decision making that enables autonomous robotic systems to optimize for multiple goals at once while avoiding worst-case scenarios in neglected objectives. We expand this approach to arbitrary numbers of objectives and compare its performance to scalar weighted sum methods during competitive motion planning. Explainable Artificial Intelligence (XAI) software is used to aid in the analysis of high dimensional decision-making data. State-space Exploration of Multidimensional Boundaries using Adherence Strategies (SEMBAS) is applied to explore performance modes in the parameter space as a sensitivity study for the baseline and proposed frameworks. While some works have explored aspects of game theoretic planning and intelligent systems validation separately, we combine each of these into a novel and comprehensive simulation pipeline. This integration demonstrates a dramatic improvement of the vector cost method over scalarization and offers an interpretable and generalizable framework for robotic behavioral planning. Code available at https://github.com/toazbenj/race_simulation. The video companion to this work is available at https://tinyurl.com/vectorcostvideo.
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