Dialogue Management

Dialogue management (DM) focuses on guiding conversations in interactive systems, such as chatbots and robots, towards desired goals. Current research emphasizes improving DM performance through advanced machine learning models, including reinforcement learning algorithms (like DQN and DDQN), transformer-based architectures (like Llama 2), and mixture-of-experts approaches, often pre-trained on large datasets encompassing diverse dialogue tasks. These advancements aim to create more natural, efficient, and safe interactions, impacting fields like customer service, human-robot interaction, and collaborative AI systems. A key challenge remains the need for high-quality training data to overcome limitations in model performance and ensure robust, reliable dialogue systems.