arXiv subjects

Mohmmad Naim Rastgoo

Publications and source records attributed to Mohmmad Naim Rastgoo.

3 recordsLinked to original sources

arXiv2025

BCR-DRL: Behavior- and Context-aware Reward for Deep Reinforcement Learning in Human-AI Coordination

Deep reinforcement Learning (DRL) offers a powerful framework for training AI agents to coordinate with human partners. However, DRL faces two critical challenges in human-AI coordination (HAIC): sparse rewards and unpredictable human behaviors. These challenges significantly limit DRL to identify effective coordination policies, due to its impaired capability of optimizing exploration and exploitation. To address these limitations, we propose an innovative behavior- and context-aware reward (BCR) for DRL, which optimizes exploration and exploitation by leveraging human behaviors and contextual information in HAIC. Our BCR consists of two components: (i) A novel dual intrinsic rewarding scheme to enhance exploration. This scheme composes an AI self-motivated intrinsic reward and a human-motivated intrinsic reward, which are designed to increase the capture of sparse rewards by a logarithmic-based strategy; and (ii) A new context-aware weighting mechanism for the designed rewards to improve exploitation. This mechanism helps the AI agent prioritize actions that better coordinate with the human partner by utilizing contextual information that can reflect the evolution of learning. Extensive simulations in the Overcooked environment demonstrate that our approach can increase the cumulative sparse rewards by approximately 20%, and improve the sample efficiency by around 38% compared to state-of-the-art baselines.

cs.AI↗

arXiv2025

Affective-CARA: A Knowledge Graph Driven Framework for Culturally Adaptive Emotional Intelligence in HCI

Culturally adaptive emotional responses remain a critical challenge in affective computing. This paper introduces Affective-CARA, an agentic framework designed to enhance user-agent interactions by integrating a Cultural Emotion Knowledge Graph (derived from StereoKG) with Valence, Arousal, and Dominance annotations, culture-specific data, and cross-cultural checks to minimize bias. A Gradient-Based Reward Policy Optimization mechanism further refines responses according to cultural alignment, affective appropriateness, and iterative user feedback. A Cultural-Aware Response Mediator coordinates knowledge retrieval, reinforcement learning updates, and historical data fusion. By merging real-time user input with past emotional states and cultural insights, Affective-CARA delivers narratives that are deeply personalized and sensitive to diverse cultural norms. Evaluations on AffectNet, SEMAINE DB, and MERD confirm that the framework consistently outperforms baseline models in sentiment alignment, cultural adaptation, and narrative quality. Affective-CARA achieved a Cultural Semantic Density of 9.32 out of 10 and lowered cultural representation bias by 61% (KL-Divergence: 0.28), demonstrating robust performance in generating ethical, adaptive responses. These findings suggest the potential for more inclusive and empathetic interactions, making Affective-CARA an avenue for fostering culturally grounded user experiences across domains such as cross-cultural communication, mental health support, and education.

cs.HC↗

arXiv2024

A BERT-Based Summarization approach for depression detection

Depression is a globally prevalent mental disorder with potentially severe repercussions if not addressed, especially in individuals with recurrent episodes. Prior research has shown that early intervention has the potential to mitigate or alleviate symptoms of depression. However, implementing such interventions in a real-world setting may pose considerable challenges. A promising strategy involves leveraging machine learning and artificial intelligence to autonomously detect depression indicators from diverse data sources. One of the most widely available and informative data sources is text, which can reveal a person's mood, thoughts, and feelings. In this context, virtual agents programmed to conduct interviews using clinically validated questionnaires, such as those found in the DAIC-WOZ dataset, offer a robust means for depression detection through linguistic analysis. Utilizing BERT-based models, which are powerful and versatile yet use fewer resources than contemporary large language models, to convert text into numerical representations significantly enhances the precision of depression diagnosis. These models adeptly capture complex semantic and syntactic nuances, improving the detection accuracy of depressive symptoms. Given the inherent limitations of these models concerning text length, our study proposes text summarization as a preprocessing technique to diminish the length and intricacies of input texts. Implementing this method within our uniquely developed framework for feature extraction and classification yielded an F1-score of 0.67 on the test set surpassing all prior benchmarks and 0.81 on the validation set exceeding most previous results on the DAIC-WOZ dataset. Furthermore, we have devised a depression lexicon to assess summary quality and relevance. This lexicon constitutes a valuable asset for ongoing research in depression detection.

cs.CL↗