Neural Network Path Projection for Sequential Decision Problems
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Solution Overview
Problem
Current systems and methods for solving sequential decision problems fail to accurately project a likely future path, neglecting real options, asymmetric risks, and user preferences, relying on arbitrary rules and simplistic simulations that do not account for the complexity of decision-making under uncertainty.
Innovation Solution
A computer-aided system and method that forms and solves functional equations to generate an optimal policy, identifying the most likely transitions and actions for each state, allowing for the visualization of future paths and incorporating user preferences and personality traits, while handling stochastic elements and large numbers of possible paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If Monte Carlo simulations or simple event trees are used to approximate future paths, then computational simplicity is maintained, but accuracy in representing real options and asymmetric risks deteriorates
Solution Approach 1:
The patent replaces traditional mechanical simulation methods (Monte Carlo simulations, event trees) with a neural network-based system that learns optimal paths through training on decision problem data, substituting statistical sampling with intelligent pattern recognition to achieve both accuracy and computational efficiency
Solution Approach 2:
The patent changes the fundamental parameters of the simulation approach by using learned transition probabilities and value functions from neural networks instead of random sampling, allowing the system to capture asymmetric risks and real options while maintaining computational tractability
2Measurement precision
If the number of possible paths is increased to capture complexity of decision-making, then accuracy improves, but computational complexity increases
Solution Approach 1:
The patent extracts the essential features of decision-making from the full space of possible paths by using neural networks to learn value functions and optimal policies, separating the critical decision points from the exhaustive enumeration of all possible paths
Solution Approach 2:
The patent segments the decision problem into manageable components by using neural networks to process different aspects of the decision space independently, allowing complex problems to be solved through modular computation rather than exhaustive path analysis
Data Source
AI summary
A system and method for projecting the likely future path of the subject of a sequential decision problem. The subject of the sequential decision problem takes an action beginning with the starting state of affairs and probabilistically transitions into other states according to the structure of the decision problem, the solution to the decision problem, possibly random events, and the decisions of the subject. The likely future path consists of a sequence of actions taken by the subject, the states the subject will likely be in after taking the projected actions, and the rewards the subject is likely to receive along the future path.