Reliable Collaborative Conversational Agent System Based on LLMs and Answer Set Programming
As the Large-Language-Model-driven (LLM-driven) Artificial Intelligence (AI) bots became popular, people realized their strong potential in Task-Oriented Dialogue (TOD). However, bots relying wholly on LLMs are unreliable in their knowledge, and whether they can finally produce a correct result for the task is not guaranteed. The collaboration among these agents also remains a challenge, since the necessary information to convey is unclear, and the information transfer is by prompts -- unreliable, and malicious knowledge is easy to inject. With the help of logic programming tools such as Answer Set Programming (ASP), conversational agents can be built safely and reliably, and communication among the agents made more efficient and secure. We proposed an Administrator-Assistant Dual-Agent paradigm, where the two ASP-driven bots share the same knowledge base and complete their tasks independently, while the information can be passed by a Collaborative Rule Set (CRS). The knowledge and information conveyed are encapsulated and invisible to the users, ensuring the security of information transmission. We have constructed AutoManager, a dual-agent system for managing the drive-through window of a fast-food restaurant such as Taco Bell in the US. In AutoManager, the assistant bot takes the customer's order while the administrator bot manages the menu and food supply. We evaluated our AutoManager and compared it with the real-world Taco Bell Drive-Thru AI Order Taker, and the results show that our method is more reliable.
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