Discourse Trees for Autonomous Agent Explanation Soundness
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Solution Overview
Problem
Current technologies fail to effectively differentiate between good and bad explanations in text, leading to unconvincing explanations from autonomous agents, which can erode user trust and faith in their responses.
Innovation Solution
The use of discourse trees to assess the soundness of explanations by identifying logical and rhetorical connections between text statements, with complete discourse trees incorporating both actual and imaginary relationships to enhance explanation validity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If compositional semantics technologies are used to support automated agents, then the agents can handle simple queries and replies, but the agents fail to provide convincing explanations for complex discourse
Solution Approach 1:
The patent segments discourse into elementary discourse units (EDUs) and organizes them into hierarchical discourse trees with nucleus and satellite relationships. This segmentation allows the system to analyze complex explanations by breaking them down into manageable logical units while preserving the overall explanatory structure, thereby improving both query handling capability and explanation credibility.
Solution Approach 2:
The patent introduces a new dimension of discourse structure analysis by creating discourse trees that add hierarchical and relational dimensions to traditional linear text processing. This dimensional enhancement enables the system to evaluate explanations from multiple perspectives (logical connection, rhetorical structure, completeness), resolving the contradiction between handling simple queries and providing convincing complex explanations.
2Measurement precision
If traditional discourse trees are used to analyze text, then logical connections between statements can be identified, but rhetorical connections and completeness cannot be fully assessed
Solution Approach 1:
The patent merges multiple analysis dimensions into a unified discourse tree structure that simultaneously captures logical connections, rhetorical relationships, and explanatory completeness. By combining nucleus-satellite relationships with rhetorical role annotations (explanans/explanandum), the system preserves both logical precision and rhetorical information without loss, resolving the contradiction between measurement precision and information retention.
3Speed
If autonomous agents provide explanations without discourse analysis, then response speed is maintained, but user trust and satisfaction decrease
Solution Approach 1:
The patent implements preliminary discourse tree construction and validation as a preparatory step before generating agent responses. By pre-analyzing the discourse structure, identifying nucleus-satellite relationships, and validating logical connections in advance, the system ensures explanation quality without significantly impacting response speed, thus resolving the contradiction between speed and user trust.
Data Source
AI summary
Systems, devices, and methods discussed herein provide improved autonomous agent applications that are configured to provide explanations in response to user-submitted questions. Training data comprising a question, and an explanation pair may be accessed. A discourse tree and an explanation chain can be constructed from the explanation. The explanation chain may identify logical relationships between two entities of elementary discourse units identified from the discourse tree. A query may be submitted for the two entities, and a set of search results can be mined to identify text linking the two entities. An additional discourse tree can be generated from the text of a search result. The additional discourse tree can be combined with the original discourse tree to generate a complete discourse tree. A model may be trained using this augmented data (e.g., the complete discourse tree) to improve the quality of explanations provided by the autonomous agent application.