A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning-based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings

A Survey of Customer Service System Based on Learning

With the rapid development of artificial intelligence, people have moved from manual customer service to handling affairs, and now they are more inclined to use intelligent customer service systems. The intelligent customer service system is generally a chat robot based on natural language processing, and it is a dialogue system. Therefore, it plays a vital role in many fields, especially in the field of e-commerce. In this article, to help researchers further study the customer service system for e-commerce, we survey the learning-based methods in dialogue understanding, dialogue management and dialogue response generation in the customer service system. In particular, we compare the advantages and disadvantages of these methods and pointed out further research directions