Federated Virtual Agent System for Chat Bot Response Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional chat bot systems require expensive human-labeling efforts and often have incomplete knowledge, leading to inconsistent performance across different domains, as they rely on hand-crafted rules and manually labeled training data.
Innovation Solution
A virtual agent system that integrates responses from multiple virtual agents using a dialog manager with a natural language processing module, allowing for adaptive selection and switching of resources to provide optimized responses based on current circumstances, leveraging unsupervised, semi-supervised, and fully supervised machine learning approaches to derive high-quality automatic responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hand-crafted rules and manually labeled training data are used for chat bot systems, then the system can learn communication rules for specific domains, but expensive human-labeling efforts and high costs are incurred
Solution Approach 1:
The system enables virtual agents to automatically learn and improve communication rules through machine learning models, eliminating the need for expensive manual labeling. The agents self-train using available data and continuously refine their performance without human intervention in the labeling process.
Solution Approach 2:
The federated architecture creates a universal learning system where multiple domain-specific virtual agents contribute to a shared knowledge base. The system serves multiple domains simultaneously through a common infrastructure that aggregates learning across all agents, reducing overall costs while maintaining domain-specific performance.
2Adaptability or versatility
If hand-crafted rules and manually labeled training data are used for chat bot systems, then the system can operate in specific domains, but knowledge in each specific domain is often incomplete and performance varies across different circumstances
Solution Approach 1:
The system merges knowledge from multiple domain-specific virtual agents into a unified federated learning framework. By combining the strengths of different agents across various domains, the system achieves more complete and reliable knowledge coverage than any single agent could achieve alone.
Solution Approach 2:
The system implements feedback loops where virtual agents continuously learn from their interactions and performance outcomes. The federated architecture allows agents to receive feedback from multiple sources and continuously refine their knowledge, improving both completeness and adaptability over time.
3Adaptability or versatility
If multiple virtual agents are integrated to provide optimized responses, then the system can adapt to changing conversation contexts and improve performance, but the device complexity increases
Solution Approach 1:
The system segments the virtual agent ecosystem into independent, modular agents that each handle specific domains or functions. This segmentation allows for easier management and integration of multiple agents while maintaining individual agent simplicity and enabling selective activation based on conversation context.
Data Source
AI summary
A method and a virtual agent system services a user request from a user. The virtual agent system includes: (a) a conversational user interface receiving the user request and communicating with two or more virtual agents; and (b) a dialog manager including a natural language processing module, that directs operations of the conversational user interface, wherein the dialog manager (i) receives and analyzes the user request from the conversation user interface using the natural language processing module, (ii) causes the conversational user interface to request and to receive a response to the user request from each of the virtual agents, and (iii) integrates the received responses to the user request into an integrated response based on the natural language processing module and causes the conversational user interface to provide the integrated response to the user.