Computer Architecture for Causal Plan Prediction and Consistency Checking
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Solution Overview
Problem
Existing computer-implemented plan recognition techniques face challenges in accurately predicting future actions and completing partially observed plans due to the complexity of causal structures and hierarchical relationships, leading to inefficiencies in various domains such as cyber security, control systems, and personal assistants.
Innovation Solution
A computer architecture that utilizes machine learning and neural networks to generate observation data structures, extend them with predicted actions, and enforce consistency rules to improve plan recognition accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing plan recognition techniques are used to handle complexity of causal structures and hierarchical relationships, then plan recognition can be performed, but prediction accuracy and efficiency deteriorate
Solution Approach 1:
The patent segments the plan recognition task into multiple independent components: a neural network component for capturing causal structures and hierarchical relationships, and a rule-based consistency checking component for enforcing domain constraints. This segmentation allows each component to specialize in specific aspects, improving both accuracy and efficiency by avoiding the need to handle all complexity within a single system.
Solution Approach 2:
The patent introduces an intermediary representation layer consisting of structured observations and predicted actions that serve as mediators between raw input data and final plan recognition outputs. This intermediary structure organizes information in a way that facilitates both neural network processing of patterns and rule-based validation, thereby improving prediction accuracy while maintaining efficiency through structured data flow.
2Reliability
If consistency rules are enforced to improve prediction accuracy, then plan recognition reliability improves, but computational complexity increases
Solution Approach 1:
The system segments complexity management by separating consistency rule enforcement from the main neural network inference process. Rules are applied independently to structured predictions after the neural network generates initial results, allowing reliability to be improved through validation without increasing the core computational complexity of the prediction model itself.
Solution Approach 2:
The patent applies consistency rules selectively rather than exhaustively - only to the specific structured predictions that are generated during the planning process. This partial application of rules maintains reliability for the critical prediction paths while avoiding the computational burden of applying all possible rules to all possible data, thus managing system complexity.
Data Source
AI summary
A computer receives a plurality of observations. The computer generates an observation data structure, where the observation data structure represents at least one of causal structures or hierarchical relationships between the plurality of observations. The computer extends, in accordance with the at least one of the causal structures or the hierarchical relationships, the observation data structure to include predicted actions that have not yet occurred and are not from the plurality of observations while maintaining a format and a structure of the observation data structure. The computer reduces, in accordance with a set of consistency rules, the extended observation data structure. The computer provides an output associated with the reduced observation data structure.