Autonomous Agent Prediction Error Modelling via ARMA and Regression
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Solution Overview
Problem
Existing self-learning predictive systems, such as Hierarchical Temporal Memory (HTM), face inaccuracies in learning and prediction as the number of iterations increases, failing to adapt effectively in unstructured and noisy environments, and lack a mathematical framework to model prediction behavior across path-learning iterations.
Innovation Solution
A two-stage modelling technique using Hierarchical Temporal Memory (HTM) combined with Autoregressive moving average (ARMA) and linear regression to capture and estimate prediction errors in path-learning of autonomous agents, integrating time series models across path-learning iterations to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional HTM modelling technique is used for path-learning, then the system can learn sequences of actions, but prediction accuracy deteriorates with increase in iterations
Solution Approach 1:
The patent implements a feedback mechanism by calculating prediction errors between HTM predicted actions and actual actions, then using these errors to update the HTM model parameters. This continuous feedback loop allows the system to correct accumulating errors and maintain prediction accuracy over multiple iterations through the error correction process.
Solution Approach 2:
The patent replaces the traditional HTM learning mechanism with an enhanced version that incorporates statistical error modeling. By substituting the pure HTM approach with a hybrid system that includes error calculation, ARIMA time series analysis, and parameter updating based on prediction errors, the system achieves sustained accuracy over iterations.
2Measurement precision
If deep learning and reinforcement learning techniques are used for prediction, then prediction capability is improved, but computational resources and training data requirements increase
Solution Approach 1:
The patent segments the prediction problem into two distinct stages: first, HTM generates predicted actions based on temporal patterns, and second, a statistical error model corrects these predictions. This segmentation allows each component to be simpler and more efficient, avoiding the need for complex deep learning architectures while achieving comparable or superior prediction capability.
Solution Approach 2:
The patent introduces an intermediary error correction mechanism that sits between the HTM prediction and the final output. Instead of using computationally intensive deep learning models, the system uses this intermediary statistical model to adjust predictions based on historical error patterns, reducing computational requirements while maintaining accuracy.
3Reliability
If traditional HTM system is used in controlled environments, then learning performance is satisfactory, but adaptability to unstructured and noisy environments deteriorates
Solution Approach 1:
The patent makes the system dynamic by enabling continuous adaptation to changing environments. The error correction model is updated online as new data arrives, allowing the system to adapt to unstructured and noisy environments. This dynamic updating mechanism enables the system to maintain reliable learning performance across diverse and changing conditions.
Solution Approach 2:
The patent implements parameter changes by adjusting HTM model parameters based on prediction errors from actual actions. This parameter adaptation allows the system to transition from controlled environment performance to unstructured environment adaptability, as the parameters are continuously tuned to match the specific characteristics of the operating environment.
Data Source
AI summary
Systems and methods for modelling prediction errors in path-learning of an autonomous learning agent are provided. The traditional systems and methods provide for machine learning techniques, wherein estimation of errors in prediction is reduced with an increase in the number of path-iterations of the autonomous learning agent. Embodiments of the present disclosure provide for a two-stage modelling technique to model the prediction errors in the path-learning of the autonomous learning agent, wherein the two-stage modelling technique comprises extracting a plurality of fitted error values corresponding to a plurality of predicted actions and actual actions by implementing an Autoregressive moving average (ARMA) technique on a set of prediction error values; and estimating, by implementing a linear regression technique on the plurality of fitted error values, a probable deviation of the autonomous learning agent from each of an actual action amongst a plurality of predicted and actual actions.