Dynamic Physical Model Control Logic for Low-Latency Decisions
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Solution Overview
Problem
Complex engineered systems, such as self-driving cars and planes, face challenges in generating effective control decision logic due to the complexity of dynamic environments, leading to high computational costs and latency issues.
Innovation Solution
The automatic generation of control decision logic using a dynamic physical model, refined through discretization, pipeline staging, and pruning, enables efficient control by reducing precision requirements and optimizing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional control logic is used for complex engineered systems, then control accuracy can be maintained, but computational cost increases and response time decreases
Solution Approach 1:
The patent transforms continuous physical parameters into discrete symbolic representations, changing the parameter space from continuous to discrete. This allows control logic to operate on simplified symbolic states rather than continuous values, reducing computational complexity while maintaining control accuracy through the preservation of critical system behavior patterns.
Solution Approach 2:
The patent replaces traditional computational control mechanisms with a physics-based symbolic reasoning system. Instead of using complex algorithms to compute control decisions, the system uses symbolic representations of physical laws and principles to directly derive control actions, substituting computational mechanics with physical reasoning.
2Measurement precision
If traditional control logic is used for complex engineered systems, then control accuracy can be maintained, but response time increases due to processing latency
Solution Approach 1:
By changing continuous parameters to discrete symbolic states, the system reduces the computational burden of real-time processing. Symbolic state transitions can be evaluated much faster than continuous numerical computations, enabling rapid response times while preserving control accuracy through the use of physics-based reasoning rules.
Solution Approach 2:
The patent performs preliminary discretization and symbolic representation of system states offline, before real-time control is needed. This pre-processing creates a simplified symbolic model that can be rapidly queried during operation, eliminating the need for complex real-time computations while maintaining accurate control decisions.
3Measurement precision
If high precision control is implemented, then system performance improves, but computational resource requirements increase
Solution Approach 1:
The patent reduces computational resource requirements by transforming continuous control parameters into discrete symbolic representations. This parameter transformation allows the system to achieve high control precision through symbolic reasoning about physical principles rather than through computationally intensive numerical calculations, significantly reducing memory, processing power, and storage requirements.
Data Source
AI summary
Possible input value combinations of a prediction of an engineered system are iterated over, comprising, for a possible input value combination: selecting an action to perform on the engineered system for the possible input value combination, comprising: performing a plurality of predictions of the engineered system scored by evaluating an objective function associated with the engineered system and using the possible input value combination and a corresponding plurality of actions. The action is selected from the corresponding plurality of actions, the selection being based at least in part on scores of the plurality of predictions. A rule specifying a corresponding set of one or more rule conditions that is met when the possible input value combination is matched and a corresponding action associated with the rule as a selected action is generated. The generated set of rules to be stored or further processed is output.