Central Agent Orchestrating Specialized Chatbots for Social Knowledge
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Solution Overview
Problem
Current chatbot systems lack effective coordination and real-time access to up-to-date information, particularly from social media, which limits their ability to provide accurate and timely responses to user queries in natural language conversations.
Innovation Solution
A system and method that utilize a central agent to parse natural-language user queries, leveraging a local database for immediate answers and invoking artificial conversational entities (ACEs) for topical expertise, with the option to create answers using social knowledge crawled from social media services when local knowledge is insufficient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple chatbots are integrated into a natural dialogue system, then the system can provide diverse topical expertise and handle various user queries, but the chatbots lack coordination and cannot stay up to date with latest information
Solution Approach 1:
A central agent is introduced as an intermediary component that coordinates between multiple specialized chatbots and social media data sources. The central agent receives user queries, determines which specialized chatbot should handle each query based on topical field, and ensures they receive updated information from social media, thereby providing coordination and information currency without requiring direct integration between all chatbot components.
Solution Approach 2:
The system is divided into specialized chatbots, each focused on a specific topical field (e.g., sports, entertainment, news). This segmentation allows each chatbot to develop deep expertise in its domain while the central agent manages the overall coordination and information distribution, resolving the contradiction between specialized versatility and coordinated reliability.
2Ease of operation
If chatbots are configured to converse using natural dialogue with information from multiple data sources, then user interaction quality improves, but the system complexity increases due to limited support for building and improving chatbot behavior
Solution Approach 1:
The central agent serves as a universal component that handles multiple functions: receiving user queries, parsing natural language, determining topical fields, selecting appropriate specialized chatbots, and managing information flow from social media sources. This multi-functional design reduces overall system complexity by consolidating coordination logic in a single component rather than requiring complex integration between all chatbot pairs.
3Speed
If chatbots rely on local databases for answers, then response speed is fast, but the information becomes outdated and lacks real-time social media data
Solution Approach 1:
The system performs preliminary actions by having the central agent proactively crawl and collect information from social media sources before user queries arrive. This pre-gathering of information ensures that when users ask questions, the specialized chatbots already have access to current social media data, eliminating the need for slow real-time searches while maintaining information currency.
Solution Approach 2:
The central agent continuously crawls social media sources for new information, maintaining an ongoing flow of updated data to the specialized chatbots. This continuous information refresh ensures that chatbots always have access to current social media content while maintaining fast response times, as the information is already available in their knowledge base when queried.
Data Source
AI summary
A method for answering a natural-language user query includes receiving a query from a user by a central agent, parsing the query, and determining if an answer is known to the central agent as being within a local database. If the answer is known, the query is answered with a known natural-language answer using the local database. If not, artificial conversational entity (ACE) invocation is performed. The ACE invocation includes determining a topical field for the query, determining an ACE, of a plurality of ACEs, to send the query to, based on the topical field, and sending the query to the ACE. If the ACE is capable of answering, a natural-language answer is received from the ACE and presented to the user. If not, a natural-language answer is created using social knowledge and presented to the user. Social knowledge includes data crawled from subscriber-provided content of social media services.