Deep Reinforcement Learning Interpretability via Sequential Pattern Mining
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Solution Overview
Problem
Reinforcement learning models in dialog management systems lack interpretability and debuggability, making it difficult to deploy them in consumer-facing applications where user experience and understanding of policy decisions are critical.
Innovation Solution
The approach involves extracting representative dialog policy rules using sequential pattern mining and refining the models by incorporating human knowledge through user simulation, allowing for improved interpretability and debuggability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning models are used in dialog management systems, then the system can provide optimized policy decisions and improved user experience, but the models lack interpretability and debuggability
Solution Approach 1:
The patent introduces sequential pattern mining as an intermediary technique that bridges the gap between complex reinforcement learning models and human-understandable dialog policies. The SPM process extracts meaningful patterns from the RL model's decision-making process, creating an intermediate representation that maintains the optimization benefits of RL while providing the interpretability needed for debugging and deployment.
Solution Approach 2:
The patent extracts representative dialog policies from the reinforcement learning model by applying sequential pattern mining. This extraction process isolates the most important decision-making patterns from the complex RL model, creating a simplified set of rules that can be interpreted and debugged while preserving the essential optimization capabilities.
2Adaptability or versatility
If reinforcement learning models are deployed in consumer-facing applications, then advanced capabilities are achieved, but user understanding of policy decisions becomes difficult
Solution Approach 1:
Sequential pattern mining serves as a mediator that translates complex reinforcement learning decisions into understandable dialog policies. The SPM process identifies and extracts meaningful sequential patterns from the RL model's behavior, creating an intermediate layer that preserves advanced capabilities while improving user comprehension through interpretable rule representations.
Solution Approach 2:
The patent transforms the 'color' or representation of the model's decision-making process from opaque mathematical transformations to visible, interpretable sequential patterns. By changing the representation format from hidden RL weights to explicit dialog policies with identifiable patterns, the system maintains advanced capabilities while making the decision logic visible and understandable to users.
3Productivity
If rule-based implementations are replaced with reinforcement learning, then scalability and forward-looking optimization improve, but interpretability and debuggability are lost
Solution Approach 1:
The patent introduces sequential pattern mining as a mediator layer between reinforcement learning models and the dialog management system. This intermediary technique extracts interpretable sequential patterns from the RL model, enabling both the scalability of RL and the debuggability needed for maintenance, effectively bridging the gap between these two opposing requirements.
Solution Approach 2:
The patent segments the complex reinforcement learning decision-making process into distinct sequential patterns that can be individually analyzed and debugged. By dividing the monolithic RL model into extractable sequential patterns, the system maintains the overall scalability of RL while enabling granular debugging and repair of specific policy components.
Data Source
AI summary
In one embodiment, a method includes training a target machine-learning model iteratively by accessing training data of content objects, training an intermediate machine-learning model that outputs contextual evaluation measurements based on the training data, generating state-indications associated with the training data, wherein the state-indications comprise user-intents, system actions, and user actions, training the target machine-learning model based on the contextual evaluation measurements, the state-indications, and an action set comprising possible system actions, extracting rules based on the target machine-learning model by a sequential pattern-mining model, generating synthetic training data based on the rules, updating the training data by adding the synthetic training data to the training data, determining if a completion condition is reached for the training, and if the completion condition is reached returning the target machine-learning model, else repeating the iterative training of the target machine-learning model.