Graph-based Self-Adaptive Conversational Agent

Conversational agents have been widely adopted in dialogue systems for various business purposes. Many existing conversational agents are rule-based and require significant human intervention to adapt the knowledge and conversational flow. In this paper, we propose a graph-based adaptive conversational agent model which is capable of learning knowledge from human beings and adapting the knowledge-base according to human-agent interactions. Studies to evaluate the proposed model are conducted and presented, which compare the responses from the proposed adaptive agent model and a conventional agent.

Authors

• Lan Zhang University of Science and Technology of China
• Weihua Li
• Quan Bai
• Edmund Lai Auckland University of Technology

Keywords

• self-adaptive
• conversational agents
• knowledge graph