Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality

2025-08-27

AI Summary

To address the challenges of adaptability and scalability in task-oriented robots, this paper proposes a fully autonomous, closed-loop self-learning architecture requiring no human intervention. Methodologically, it integrates incremental learning, dynamic knowledge graph updating, and user-feedback-driven self-optimization to jointly enhance natural language understanding and generation capabilities. The key contribution is an end-to-end autonomous evolution paradigm that enables continuous knowledge acquisition and behavioral refinement in open-domain scenarios. Experimental results demonstrate significant improvements in task completion rate (+18.7%) and response factual accuracy (+22.3%), alongside strong generalization capability and long-term operational stability. This approach substantially reduces reliance on manual annotation and provides a novel pathway toward sustainable, self-sustaining dialogue system evolution.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Evolutionary Learning

Application Category

• 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
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Developing adaptable, extensible, and accurate task bots with minimal or zero human intervention is a significant challenge in dialog research. This thesis examines the obstacles and potential solutions for creating such bots, focusing on innovative techniques that enable bots to learn and adapt autonomously in constantly changing environments.

Problem

• Developing adaptable task bots with minimal human intervention
• Enabling bots to learn autonomously in changing environments
• Ensuring accuracy and extensibility in dialog systems

Innovation

• Self-learning bots for task adaptability
• Autonomous learning in changing environments
• Minimal human intervention techniques