Central Modelling System for Autonomous Agents
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Solution Overview
Problem
Autonomous agents face challenges in updating their models efficiently due to high computational costs and reliability concerns, particularly with power consumption and communication bandwidth, and there is a risk of incorrect behavior that can compromise their environment.
Innovation Solution
A computer-implemented system with a central modelling system that exposes data storage and behaviorally secure interfaces, allowing autonomous agents to interact and share data in a way that reduces training costs and ensures reliable behavior by using feature extraction, data storage, and behavioral default prevention mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous agents perform model fitting using available data, then the agents can update their models to improve performance, but the computational power and time costs increase significantly
Solution Approach 1:
The system divides the model fitting task into two segments: (1) a centralized server performs computationally intensive model training on aggregated data from multiple agents, and (2) individual agents receive pre-trained models or model updates. This segmentation allows distributed model updating without each agent bearing the full computational burden, thus improving model accuracy while reducing individual agent power consumption.
Solution Approach 2:
The patent merges data collection and model training operations by having multiple agents contribute data to a centralized server that performs unified model training. This combining of resources enables more efficient use of computational power through shared infrastructure and aggregated data processing, rather than redundant independent training on each agent.
2Reliability
If autonomous agents transfer data samples to learn from each other, then knowledge sharing improves model performance, but communication bandwidth consumption increases
Solution Approach 1:
Instead of transferring raw data samples between agents, the system creates and transfers copied model parameters or compressed model representations. This copying approach allows knowledge sharing while dramatically reducing communication bandwidth requirements compared to transferring original datasets, as model parameters are much smaller than raw data samples.
3Productivity
If autonomous agents automatically adapt their interaction with the environment, then the agents can optimize their behavior, but the risk of overriding security constraints increases
Solution Approach 1:
The patent introduces a centralized server as an intermediary that mediates between autonomous agents and the environment. The server acts as a security gateway that can validate and filter agent actions before they affect the environment, allowing behavior optimization through automatic adaptation while maintaining security constraint compliance through centralized oversight and validation.
4Reliability
If the amount of training data for a model doubles, then the model learns more comprehensively, but the training time increases by a factor of 4 due to quadratic dependence
Solution Approach 1:
The system segments the large training dataset into smaller batches that are processed in parallel across multiple agents contributing to a centralized training process. This segmentation enables more efficient training time scaling by utilizing distributed computational resources, avoiding the quadratic time increase that would occur with sequential processing of the entire dataset.
Data Source
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AI summary
A system of autonomous agents in communication with a central modelling system, wherein each agent operates in an environment in accordance with a computational model sends agent data to the central monitoring system. The central modelling system comprises: a data storage device for storing computational models and a plurality of data records; a feature extractor for extracting features from said agent data, and a modelling engine for training the computational model using the data records. Upon receipt of agent data from any one of the agents, a corresponding data record is created and stored by the central modelling system, the data record including features extracted from the agent data by the feature extractor. In response to a request from any one of the agents for a new computational model, the central modelling system sends to the requesting agent a trained computational model, the trained computational model comprising any one of the computational models trained by the modelling engine on data records received from any of the agents and stored by the central modelling system.