Probability Tree Node Structural Value Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated decision-making systems, such as those in autonomous vehicles, face challenges in efficiently analyzing and updating probability trees to make optimal decisions due to the complexity and size of these trees, which can lead to increased computational requirements and storage needs.
Innovation Solution
A method and system for calculating and assigning node structural values to each node in a probability tree, which quantify the entropy of subtrees, allowing for the analysis and updating of probability trees, and guiding the construction of enhanced trees by adjusting branch expansion based on these values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the probability tree is fully expanded to ensure comprehensive decision analysis, then the decision-making accuracy is improved, but the computational load and storage requirements increase significantly
Solution Approach 1:
The patent segments the probability tree into multiple layers and identifies specific nodes for analysis rather than processing the entire tree uniformly. By calculating node structural values for selected nodes and using them to guide selective expansion, the system divides the computational task into manageable segments, reducing overall computational load while maintaining decision accuracy.
Solution Approach 2:
The patent applies local quality by calculating node structural values (entropy measurements) specifically for certain nodes to determine their importance, rather than treating all nodes equally. This allows the system to focus computational resources on locally important regions of the tree that contribute most to decision quality, while pruning or skipping less critical areas.
2Adaptability or versatility
If the probability tree is fully expanded to ensure comprehensive decision analysis, then the exploration of action spaces is improved, but the storage needs increase significantly
Solution Approach 1:
The patent segments the probability tree structure and processes it layer by layer, storing only essential node structural values rather than the complete tree structure. By calculating entropy values for selected nodes and using them to guide selective expansion, the system maintains adequate exploration of action spaces while storing only the minimum necessary data.
Solution Approach 2:
The patent extracts only the critical node structural values (entropy measurements) from the probability tree rather than storing the entire tree structure. This extraction approach captures the essential information needed for decision-making and tree optimization while dramatically reducing storage requirements.
3Reliability
If node structural values are calculated for all nodes to provide comprehensive analysis, then the reliability of decision-making is improved, but the computational time increases
Solution Approach 1:
The patent calculates node structural values selectively for important nodes based on their position in the tree and their potential impact on decision-making, rather than uniformly processing all nodes. This localized approach maintains reliability by focusing computational effort on nodes that most influence the final decision, while reducing overall computational time.
Solution Approach 2:
The patent performs partial action by calculating node structural values for a subset of nodes rather than all nodes. By selecting nodes strategically (e.g., those at critical decision points or with high potential impact), the system achieves sufficient reliability for practical decision-making without the excessive computational cost of complete analysis.
Data Source
AI summary
A method of performing a probability tree analysis. The method includes identifying a plurality of nodes in a probability tree with each of the plurality of nodes having a probability vector. At least one node structural value is calculated for each of the plurality of nodes. The at least one node structural value quantifies an entropy of a subtree extending from a corresponding one of the plurality of nodes. The at least one node structural value is assigned to a corresponding one of the plurality of nodes. An analyzed probability tree is output including the at least one node structural value assigned to the corresponding one of the plurality of nodes.