Agent Generator for Automatic Creation of Agent-Based Systems
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Solution Overview
Problem
The development of agent-based systems is often tedious and time-consuming, requiring specialized expertise, making it difficult for typical users to create systems for data processing and analysis tasks without the assistance of domain experts.
Innovation Solution
An agent generator, or meta-agent, is used to autonomously generate agent-based systems by receiving user specifications, mapping tasks to ontological concepts, selecting transforms, and iteratively refining them to achieve optimal performance, allowing non-expert users to create effective data processing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If agent-based systems are developed using traditional methods requiring specialized expertise, then system performance and reliability are improved, but development time and complexity increase significantly
Solution Approach 1:
The system enables non-expert users to automatically generate agent-based systems through a user interface and specification process, eliminating the need for domain experts to manually develop the system. The computer automatically performs the complex tasks of creating the agent model, selecting transforms, and configuring the system based on user specifications alone.
Solution Approach 2:
A computer acts as an intermediary between the non-expert user and the complex agent-based system development process. The computer translates high-level user specifications into detailed system configurations, bridging the gap between simple user input and complex system implementation without requiring user expertise.
2Manufacturing precision
If specialized domain experts are involved in system development, then system quality and accuracy are improved, but ease of operation deteriorates
Solution Approach 1:
The system allows non-expert users to independently create agent-based systems without requiring assistance from domain experts. The automated process handles all complex technical decisions, enabling users with basic skills to achieve professional-quality results alone.
Solution Approach 2:
The system is designed to serve multiple user types (both experts and non-experts) through a unified interface that automatically adapts to the user's expertise level. The same user interface and specification process works for all users, eliminating the need for separate expert-only development paths.
3Ease of operation
If automated generation methods are used to reduce development effort, then ease of operation is improved, but manufacturing precision may deteriorate
Solution Approach 1:
The system incorporates iterative refinement where the computer generates initial system configurations based on user specifications, evaluates their performance, and automatically adjusts parameters to optimize quality. This feedback loop ensures that automated generation produces high-quality results by continuously improving the system based on performance metrics.
Solution Approach 2:
The system pre-configures transform parameters and system architecture based on the user specification before actual system execution. By performing preliminary analysis and configuration, the system ensures that the generated agent-based system is optimized for its intended purpose from the start, maintaining high quality through automated preparatory work.
Data Source
AI summary
An agent-based system may be automatically generated from a specification provided by a user or third-party process. An agent generator may map the specification to a canonical model identifying one or more tasks to be performed by the agent-based system as ontological concepts. The agent generator may generate one or more candidate agents using the canonical model. The candidate agents may comprise one or more interconnected data transforms, which may comprise data access transforms, preprocessing transforms, machine learning transforms, and/or structural transforms. The agent generator iteratively modifies the agent-based system until a termination criteria is satisfied. The termination criteria may provide a selection mechanism whereby a performance of the plurality of candidate agents may be evaluated. An optimal agent may be selected using, inter alia, the performance of the agent-based system.