Hierarchical Agent Control for Flexible Machine Task Processing
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Solution Overview
Problem
Existing machine learning systems for processing user tasks are resource-intensive, lack flexibility in handling complex tasks, and struggle with explaining responses due to static complexity ties, leading to inefficient hardware and software resource usage.
Innovation Solution
A computer-implemented method that processes tasks through a first agent selecting suitable solution operators based on natural language descriptions, allowing for a structured approach with variable hierarchy, enabling efficient task completion by inter- and intra-machine instructions, and integrating human intelligence when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a machine learning model processes tasks based on statistical relationships, then user-friendliness and natural language processing capability are improved, but the system cannot handle tasks outside its trained statistical patterns
Solution Approach 1:
The patent introduces a hierarchical agent system with multiple levels of intelligence acting as intermediaries between user input and task execution. The high-level agent plans complex tasks while low-level agents execute specific actions, enabling the system to handle diverse tasks beyond single-model statistical patterns while maintaining natural language interfaces
2Device complexity
If existing architectures process user input in a non-hierarchical environment, then system complexity is reduced, but the system cannot effectively manage large state and action spaces in Markov Decision Processes
Solution Approach 1:
The patent segments the control architecture into hierarchical levels with distinct responsibilities. The high-level agent handles strategic planning and decision-making for complex tasks, while low-level agents manage specific actions and immediate responses. This segmentation enables effective management of large state and action spaces by distributing computational complexity across multiple specialized agents rather than requiring a single monolithic system
3Extent of automation
If the system performs resource-intensive self-management, then autonomous task execution is improved, but hardware and software resources are unnecessarily tied up
Solution Approach 1:
The patent implements dynamic resource allocation where the hierarchical agent system activates only the computational resources necessary for each specific task. The high-level agent plans tasks and delegates to appropriate low-level agents, avoiding continuous full-system operation. This dynamic activation pattern maintains autonomous task execution while significantly reducing overall resource consumption compared to continuously active monolithic systems
Data Source
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AI summary
A computer-implemented method comprising at least the following steps performed by one or more servers: (a) receiving a task specification from a client; (b) processing the task specification by a first agent trained as a machine learning model to generate a query result, wherein the processing comprises: (i) selecting a solution operator for at least one processing step suitable for processing the task specification; wherein the selection is based at least partially on a textual description of the solution operator's capability; (ii) causing the selected solution operator to process the at least one processing step into intermediate results;(iii) Applying a status indicator comprising: (i) First agent receiving back the intermediate results generated by the solution operator and evaluating the intermediate results to determine whether the task has been solved and, if not, repeating steps (i) to (iii), or (ii) assuming that the task has been solved; (iv) First agent generating the query result; and (c) providing the query result to the client.