Vector Cost Behavioral Planning for Autonomous Robotic Systems with Contemporary Validation Strategies

2025-11-21

AI Summary

To address the challenge of simultaneously optimizing performance and ensuring safety in multi-objective behavioral planning for autonomous robots, this paper proposes a vector-cost double-matrix game framework. It extends vector-cost methods—previously limited to two objectives—to arbitrary numbers of objectives, thereby eliminating the risk of neglecting secondary objectives inherent in scalar-weighted aggregation. The method integrates eXplainable Artificial Intelligence (XAI) with State-space Exploration via Multi-dimensional Boundaries and Adherence Strategies (SEMBAS), enabling interpretable high-dimensional decision modeling and sensitivity analysis. Experimental evaluation on competitive motion planning tasks demonstrates significant improvements in robustness and performance identifiability over conventional scalarization-based approaches. The framework’s source code, demonstration videos, and complete simulation pipeline are publicly released, confirming its effectiveness and reproducibility.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning
• Search and Optimization: Sampling/Simulation-based Search

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Extends vector cost bimatrix games for multi-objective robotic decision making
• Compares vector cost optimization against scalar weighted sum methods
• Develops interpretable framework combining game theory with validation strategies

Innovation

• Vector cost bimatrix game for multi-objective robotic decision making
• Explainable AI analysis of high-dimensional decision-making data
• State-space exploration of multidimensional boundaries for sensitivity study