Interpretable Decision Rules via Pruning and Simulation
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Solution Overview
Problem
Current decision-making methods in machine learning and reinforcement learning face challenges in maintaining interpretability over time, with post-hoc methods providing limited guarantees of operational rules and intrinsic methods like decision trees suffering from low interpretability and degradation in recommendation quality.
Innovation Solution
A method combining a machine learning module, reinforcement learning module, pruning module, and network module to generate and prune dominant variables, producing an interpretable set of rules with no significant degradation in quality, using techniques like Proximal Policy Optimization (PPO) to provide neuro-symbolic outputs understandable by humans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If post-hoc methods such as LIME, SHAP, and query-based explanations are used to provide relations between input features and model outputs, then interpretability is improved for a reduced number of variables, but the rules may not remain operational in the source environment
Solution Approach 1:
The system performs preliminary actions by generating synthetic training data through simulation before the actual decision-making process. This pre-generated data captures operational rules and relationships in advance, allowing the ML model to learn patterns that are both interpretable and operationally valid in the source environment, rather than relying on post-hoc explanations that may not hold true.
2Device complexity
If intrinsic methods like decision trees are used to provide high interpretability, then the model structure becomes simpler and more explainable, but the decision trees may become deep and embody low interpretability over time
Solution Approach 1:
The system dynamically adapts the model structure through continuous learning from synthetic data generated by the simulation environment. Rather than using static decision trees that become deep and difficult to interpret, the model evolves its representations over time, maintaining simplicity and interpretability while capturing complex operational relationships through the dynamic simulation-training loop.
3Loss of information
If hybrid methods such as attention-based saliency maps are used to produce explanations along with recommendations, then interpretability is improved, but the quality of recommendations degrades over time
Solution Approach 1:
The system implements feedback through the simulation environment that continuously evaluates and refines the synthetic training data. This feedback loop ensures that the explanations and recommendations remain aligned with actual operational effectiveness, preventing degradation in recommendation quality over time while maintaining interpretability through the attention-based mechanisms.
4Productivity
If machine learning models are used to improve performance of tasksets, then decision accuracy is improved, but the interpretability and trust of the decisions deteriorates
Solution Approach 1:
The simulation environment acts as an intermediary between the complex ML model and the decision-making process. It generates synthetic training data that bridges the gap between raw data and model outputs, providing interpretable representations of operational rules while maintaining the high accuracy of the underlying ML model. This intermediary layer makes the decision-making process transparent and trustworthy.
Data Source
AI summary
A method and system of increasing interpretability of decision making methods include a network module providing raw data from an environment to a machine learning (ML) module. In response to the raw data being delivered to the ML module, the ML module generates a trained classifier using the raw data. A pruning module then prunes a plurality of dominant variables, in the sense of being most relevant for the decision made with respect to the classifier, using the trained classifier. The network module then provides the sub-optimal policy to a reinforcement learning (RL) module, where a generated sub-optimal policy is applied to the environment to obtain a dataset by applying the sub-optimal policy and generating a trajectory. The ML module then generates an interpretable set of rules using the generated trajectory.