Doubly-Exponentially Accelerated Particle Methods for Nonlinear Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current artificial intelligence methods, such as Deep Reinforcement Learning, fail to effectively address nonlinear control problems under uncertainty, leading to sub-optimal performance in novel and complex environments due to their inability to generalize beyond training data and handle exponential computational complexity.
Innovation Solution
The development of doubly-exponentially accelerated particle methods that simulate biological intelligence by formulating Markov Decision Processes on a smaller world state space, incorporating emotional and multi-valued functions, and using oracle bootstrapping to devise optimal, contingent strategies for action, allowing for efficient computation and abstraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional AI methods (Deep Reinforcement Learning) are used to solve nonlinear control problems, then training data can be utilized, but the system fails to generalize to novel scenarios and cannot handle exponential computational complexity
Solution Approach 1:
The patent segments the computational problem by separating the state space into manageable components through the formulation of Markov Decision Processes on a reduced state space. This allows the system to handle complexity by dividing the overall computational task into smaller, more manageable sub-problems that can be solved efficiently.
Solution Approach 2:
The patent transforms the computational problem by changing the dimensionality of the state space representation. By formulating MDPs on a smaller world state space rather than using the full state space, the system achieves more efficient computation while maintaining generalization capability to novel scenarios.
2Reliability
If the state space is expanded to include all probability distributions over world states to handle uncertainty, then uncertainty can be modeled accurately, but the computational cost becomes exponentially larger
Solution Approach 1:
The patent extracts only the essential information needed for decision-making under uncertainty by formulating MDPs on a reduced state space. This allows the system to handle uncertainty accurately by focusing on the critical state components rather than processing the entire probability distribution space.
Solution Approach 2:
The patent changes the parameters of the state space representation to achieve a more efficient formulation. By modifying how the state space is parameterized and represented, the system maintains accurate uncertainty modeling while significantly reducing computational requirements.
3Manufacturing precision
If backward induction is performed on the space of all probability distributions to find optimal action strategies, then globally optimal strategies can be found, but the computation becomes infeasible for practical problems
Solution Approach 1:
The patent segments the backward induction process by performing it on a reduced state space rather than the full probability distribution space. This maintains the ability to find globally optimal strategies while reducing computational time by working with a smaller, more manageable state representation.
Data Source
AI summary
Aspects herein describe new methods of determining optimal actions to achieve high-level objectives based on an optimized chosen statistic. At least one high-level objective, along with various observational data about the world, is identified by a computational unit. The computational unit determines, through a particle method, an optimal course of action. The particle method is doubly-exponentially accelerated based on one or more acceleration methods. The doubly-exponentially accelerated particle method comprises alternating backward and forward sweeps of a coupled induction loop to optimize a selection policy and test for convergence to determine said optimal course of action. In one embodiment a user inputs a high-level objective into a cell phone which senses observational data. The cell phone communicates with a server that provides instructions. The server determines an optimal course of action via the doubly-exponentially accelerated particle method, and the cell phone then displays the instructions to the user.