Logical CMDP Policy Representation for Interpretable Control
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Solution Overview
Problem
Constrained Markov decision process (CMDP) models used in control systems lack interpretability, making it difficult for operators to understand the reasoning behind control actions and the impact of input variables, which hinders optimization and modification of controlled application systems.
Innovation Solution
A computer-implemented method for generating logically-represented policies based on CMDP models using dual linear programming, allowing for the automatic transformation of complex policies into interpretable logical forms, such as disjunctive or conjunctive normal form, enabling operators to modify control systems effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CMDP models are used to direct control actions in control systems, then the system optimization and constraint compliance are improved, but the interpretability and understandability of control reasoning deteriorate
Solution Approach 1:
The patent introduces an intermediary component that translates the internal occupation measures of the CMDP model into externally visible logical rules. This mediator layer preserves the optimization capabilities of CMDP while providing human-understandable explanations through logical representations that show the reasoning behind control actions.
Solution Approach 2:
The patent creates a logical copy or representation of the CMDP policy in a human-interpretable format. By generating logical rules that mirror the decision-making logic of the CMDP model, operators can understand and verify the control reasoning without sacrificing the model's optimization performance.
2Reliability
If complex CMDP models are implemented for constrained control, then the constraint compliance and operational efficiency are improved, but the ease of operation and modification by operators deteriorates
Solution Approach 1:
The logical rule generation acts as an intermediary between the complex CMDP model and operators. This intermediary provides a simplified interface that maintains constraint compliance while enabling operators to easily understand, verify, and modify control policies through intuitive logical representations.
Solution Approach 2:
The patent transforms the parameters of the CMDP model (occupation measures) into logical rule parameters that are more accessible to operators. By changing the representation format from mathematical vectors to logical rules, the system maintains its constraint-compliant behavior while improving operability.
3Productivity
If occupation measures are used as decision variables in dual linear programming, then the policy optimization is improved, but the difficulty of detecting and measuring the reasoning logic increases
Solution Approach 1:
The patent creates a logical copy of the occupation measure-based policy. By generating logical rules that represent the same decision logic as the occupation measures, the system maintains optimization performance while making the reasoning logic detectable and measurable through standard logical analysis methods.
Solution Approach 2:
The patent substitutes the mathematical/mechanical representation (occupation measures in linear programming) with a logical representation. This substitution replaces the difficult-to-interpret mathematical formalism with intuitive logical rules that are easier to detect, measure, and analyze.
Data Source
AI summary
A control system, computer program product, and method for generating a logically-represented policy for a control system operating based on a CMDP model are provided. The control system directs the operation of a controlled application system that is subject to a constraint. The method includes receiving, at the control system, data corresponding to control action variables and system state variables relating to the controlled application system, data corresponding to a cost/reward, and data corresponding to the constraint, and automatically training a CMDP model for the operation of the controlled application system based on the received data, where the CMDP model is formulated using dual linear programming, and where the CMDP model includes a policy corresponding to occupation measures that are decision variables of the dual linear programming formulation. The method also includes automatically generating a logically-represented policy for the control system based on the policy of the CMDP model.