Chuanxiao Li | Searcharxiv

arXiv2026

Measuring Love Toward AI: Development and Validation of the Love Attitudes Scale toward Artificial Intelligence (LAS-AI)

Artificial intelligences (AIs) are increasingly capable of emotionally engaging with humans to the point of forming intimate relationships. Yet, current studies on romantic love toward AI lack statistically validated instruments to measure romantic love toward AI, hindering empirical research. To address this gap, we reinterpreted Lee's love styles theory in the AI context and developed the Love Attitudes Scale toward AI (LAS-AI). The resulting 24-item, six-factor scale was validated across four phases using three independent samples (N = 899), demonstrating strong psychometric properties. The findings further revealed that people primarily seek practical, passionate, and companionship-based relationships with AI (i.e., Pragma, Eros, and Storge), showing little interest in a playful or noncommittal approach (i.e., Ludus). We also provided an initial exploration of the similarities and differences between romantic love with humans and AI. The LAS-AI offers a robust tool for future research on human-AI romantic relationships, with prolific implications.

cs.HC↗

arXiv2022

Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification

Self-evolution is indispensable to realize full autonomous driving. This paper presents a self-evolving decision-making system based on the Integrated Decision and Control (IDC), an advanced framework built on reinforcement learning (RL). First, an RL algorithm called constrained mixed policy gradient (CMPG) is proposed to consistently upgrade the driving policy of the IDC. It adapts the MPG under the penalty method so that it can solve constrained optimization problems using both the data and model. Second, an attention-based encoding (ABE) method is designed to tackle the state representation issue. It introduces an embedding network for feature extraction and a weighting network for feature fusion, fulfilling order-insensitive encoding and importance distinguishing of road users. Finally, by fusing CMPG and ABE, we develop the first data-driven decision and control system under the IDC architecture, and deploy the system on a fully-functional self-driving vehicle running in daily operation. Experiment results show that boosting by data, the system can achieve better driving ability over model-based methods. It also demonstrates safe, efficient and smart driving behavior in various complex scenes at a signalized intersection with real mixed traffic flow.

cs.RO↗