Communicative Discourse Trees Detect Distributed Incompetence
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Solution Overview
Problem
Current systems fail to accurately detect distributed incompetence in text, leading to ineffective communication and problem-solving in multi-agent systems, where agents refer users to others without resolving the issue, indicating a lack of coherent and relevant responses.
Innovation Solution
The use of communicative discourse trees, which represent rhetorical relationships between text fragments, allows for the analysis and identification of distributed incompetence by applying a predictive model to determine the presence of such incompetence and generate appropriate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If autonomous agents use simple referral mechanisms to handle user queries, then device complexity is reduced, but communication effectiveness and problem-solving capability deteriorate due to distributed incompetence
Solution Approach 1:
The system implements feedback mechanisms by analyzing discourse trees to detect distributed incompetence patterns in multi-agent conversations. The predictive model continuously monitors agent interactions, identifies when referrals fail to resolve issues, and adjusts agent behavior accordingly to improve communication effectiveness while maintaining system complexity at acceptable levels.
Solution Approach 2:
The patent replaces simple mechanical referral mechanisms with an intelligent system that uses discourse tree analysis and predictive modeling. Instead of straightforward agent-to-agent referrals, the system employs natural language processing and machine learning to detect incompetence patterns and generate appropriate responses, substituting mechanical operations with intelligent processing.
2Reliability
If autonomous agents engage in extensive analysis to detect distributed incompetence, then communication effectiveness improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing text into discourse trees and pre-training predictive models on discourse patterns. This preparation work is done in advance, allowing the system to quickly detect distributed incompetence during actual agent interactions without requiring extensive real-time analysis, thus reducing processing time while maintaining detection accuracy.
Solution Approach 2:
The system applies partial analysis by focusing discourse tree analysis on specific rhetorical relationships and communicative actions most indicative of distributed incompetence. Rather than analyzing every aspect of agent conversations in depth, the system targets key patterns and behaviors, achieving sufficient detection accuracy with reduced computational effort and time.
Data Source
AI summary
Techniques are disclosed for detecting distributed incompetence in text of a conversation using communicative discourse trees and then inserting an automatic response from an autonomous agent (chatbot) or other entity. For example, a computing system generates a communicative discourse tree from utterances from multiple agents to a user. The computing system obtains a prediction of whether the text includes distributed incompetence by applying a trained predictive model to the communicative discourse tree. Based on the detection, the computing system generates an updated response to a user device.