Year: 2025
Type: conference-paper
Abstract: This paper focuses on intelligent Q&A assistants in gaming, providing timely and accurate services by integrating structured game knowledge graphs, semi-structured FAQ pairs, and unstructured real-time online content.It offers personalized emotional companionship through customized virtual characters and provides gameplay guidance, data queries, and product recommendations through in-game tools.We propose a Tiered Conversational Q&A Agent (TCQA 2 ), characterized by high precision, personalized chat, low response latency, efficient token cost and low-risk responses.Parallel modules in each tier cut latency via distributed tasks.Multiple retrievers and short-term memory boost multi-turn Q&A.Hallucination and safety checks improve response quality.Player tags and long-term memory enable personalization.Real-world evaluations show TCQA 2 outperforms prompt-engineered LLMs and RAGbased agents in gaming Q&A, personalized dialogue, and risk mitigation.
Authors: Ze Chen, Chengcheng Wei, Jiewen Zheng, Jiarong He
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412944151