Prescriptor Model Distillation for Evolutionary Decision Optimization
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Solution Overview
Problem
Existing methods struggle to effectively combine and refine diverse knowledge from multiple experts to generate optimized prescriptor models for strategic decision-making, as conventional machine learning approaches fail to account for non-linear interactions and unknown optimal outcomes.
Innovation Solution
A computer-implemented method using an evolutionary algorithm framework to distill and evolve prescriptor models, incorporating diverse expert inputs into a neural network format, allowing for optimized decision-making through a predictor surrogate model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning approaches (gradient descent, linear programming) are used for predictive modeling, then the modeling process is simple and straightforward, but the models fail to capture non-linear interactions and cannot determine optimal outcomes in partially observable domains
Solution Approach 1:
The patent replaces conventional machine learning optimization methods (gradient descent, linear programming) with an evolutionary algorithm framework. This substitution enables the system to handle non-linear interactions and partially observable domains by using population-based search and selection mechanisms that can explore complex solution spaces without requiring differentiable or convex objective functions.
Solution Approach 2:
The patent transforms prescriptor models into a functional form with evolvable parameters that can be optimized through evolutionary algorithms. By representing models as neural networks with adjustable weights and architectures, the system enables continuous parameter optimization across generations, allowing capture of non-linear relationships while maintaining model interpretability.
2Adaptability or versatility
If diverse expert knowledge is collected and combined, then the likelihood of revealing improved policy opportunities increases, but the difficulty of effectively extracting, combining, and refining complementary information increases
Solution Approach 1:
The patent merges multiple diverse prescriptor models into a unified evolutionary framework where different expert knowledge representations can coexist and interact. The system combines various model types (neural networks, rule-based systems, statistical models) into a single population that evolves together, allowing complementary strengths to emerge through evolutionary operations like crossover and selection.
Solution Approach 2:
The patent introduces a surrogate predictive model as an intermediary that evaluates and compares diverse prescriptor outputs. This mediator translates different expert knowledge formats into a common evaluation metric, enabling effective combination and refinement of complementary information while managing the complexity of integrating heterogeneous knowledge sources.
3Reliability
If Evolutionary Surrogate-assisted Prescription (ESP) is used with randomly generated neural networks as initial population, then the framework can handle non-linear interactions, but low quality random solutions are generated requiring extensive evolution time
Solution Approach 1:
The patent applies preliminary distillation actions to transform existing prescriptor models into the functional form required by evolutionary algorithms before initiating the evolution process. This pre-processing step converts diverse expert knowledge into standardized neural network representations with meaningful initial weights, eliminating the need to start from random initialization and significantly reducing the time required to reach high-quality solutions.
Solution Approach 2:
The patent creates copies of existing high-quality prescriptor models and transforms them into the evolutionary framework's required format. Instead of generating random neural networks, the system replicates and adapts proven prescriptor structures, preserving their knowledge content while making them compatible with evolutionary optimization. This copying approach provides high-quality starting points that require minimal evolution time.
Data Source
AI summary
A system and method of combining and improving sets of diverse prescriptors for Evolutionary Surrogate-assisted Prescription (ESP) model is described. The prescriptors are distilled into neural networks and evolved further using ESP. The system and method can handle diverse sets of prescriptors in that it makes no assumptions about the form of the input (i.e., contexts) of the initial prescriptors; it relies only on the prescriptions made in order to distill each prescriptor to a neural network with a fixed form. The resulting set of high performing prescriptors provides a practical way for ESP to incorporate external human and machine knowledge and generate more accurate and fitting set of solutions.