Automated Sequential Decision Calibration Using Historical Data
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Solution Overview
Problem
Current systems for calibrating and defining inputs to sequential decision problems rely heavily on user experience and heuristic methods, which become impractical for large state or action spaces, unclear spaces, or when data-driven approaches are needed, often resulting in biased and tedious tasks.
Innovation Solution
A computer-aided system that uses historical data to automatically define and calibrate parameters for sequential decision problems, including action sets, state dimensions, reward sets, transition matrices, and discount factors, forming a functional equation to generate an optimal policy through statistical techniques and error-checking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If heuristic methods are used to define inputs, then the process is simple for small problems, but it becomes biased and impractical for large state or action spaces
Solution Approach 1:
The patent replaces the mechanical/heuristic approach of manually defining state spaces, action spaces, and transition matrices with an automated computational system that uses statistical methods and historical data to estimate these parameters objectively, eliminating user bias and manual effort
Solution Approach 2:
The system enables the historical data to speak for itself by automatically estimating transition probabilities and reward structures through statistical analysis, allowing the data to define the decision problem parameters without requiring user interpretation or heuristic judgments
2Productivity
If user experience is relied upon to calibrate parameters, then the process is quick for familiar problems, but it introduces bias and fails when the state space is not immediately apparent
Solution Approach 1:
The system uses historical data as feedback to objectively estimate transition probabilities and reward structures, allowing the actual observed behavior and outcomes to calibrate the decision model parameters rather than relying on user assumptions
Solution Approach 2:
The patent transforms the calibration process from subjective parameter specification to objective parameter estimation by changing how parameters are determined - using statistical methods to estimate transition matrices and reward structures from historical data rather than user input
3Adaptability or versatility
If manual definition of state and action spaces is performed, then the user has control over the problem structure, but the task becomes tedious and difficult for complex problems
Solution Approach 1:
The system performs preliminary analysis of historical data to pre-identify potential state spaces, action spaces, and transition structures before the user needs to solve the decision problem, reducing the setup time and effort required
Solution Approach 2:
The patent creates a universal framework that can handle various types of decision problems by using general statistical methods to estimate parameters from historical data, making the system adaptable to different problem structures without requiring manual reconfiguration
Data Source
AI summary
A system and method for defining and calibrating the inputs to a sequential decision problem using historical data, where the user provides historical data and the system and method forms the historical data (along with other inputs) into at least one of the states, actions, rewards or transitions used in composing and solving the sequential decision problem.