Distributed Autonomous Agent Discovery With Cryptographic Bloom Trees
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Solution Overview
Problem
Existing distributed computing systems face inefficiencies in managing autonomous agents, leading to heightened hardware costs and reduced responsiveness due to storage space demands and hash collisions in probabilistic data structures, resulting in false positives.
Innovation Solution
A distributed computer system utilizing a bloom tree with cryptographic hash values to manage autonomous agents, combined with a software framework and machine-learning models for precise task allocation, ensuring efficient resource utilization and accurate agent selection, while minimizing storage needs through invertible bloom filters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems use search and discovery databases to identify and deploy autonomous agents, then the system can fulfill service requests, but the storage space demands increase and hardware costs heighten
Solution Approach 1:
The patent uses cryptographic hash values as compact representations (copies) of autonomous agent functionalities. Instead of storing complete agent profiles or extensive database records, the system stores only hash values that uniquely identify agent capabilities, dramatically reducing storage requirements while maintaining the ability to retrieve and deploy appropriate agents for service requests
Solution Approach 2:
The patent transforms the storage representation from full agent data structures to condensed cryptographic hash values. This parameter change in data representation format reduces the quantity of stored information from potentially megabytes or gigabytes per agent to mere bytes per agent, solving the storage space problem while preserving agent identification and deployment functionality
2Quantity of substance
If probabilistic data structures such as bloom filters are used for searching and managing autonomous agent functionality, then storage space is reduced, but hash collisions occur leading to false positives
Solution Approach 1:
The patent introduces cryptographic hash values as an intermediary layer between the bloom filter structure and the actual autonomous agent functionalities. When a service request comes in, the system hashes the required functionality and queries the bloom filter with this hash value. The hash acts as a unique intermediary representation that minimizes collision probability while maintaining the space efficiency of probabilistic data structures
Solution Approach 2:
The patent changes the parameter being hashed from raw functionality descriptions to cryptographic hash values of those descriptions. This transformation creates fixed-length, uniformly distributed values that are ideal for probabilistic data structures, reducing hash collisions compared to using variable-length functionality strings directly in the bloom filter
3Adaptability or versatility
If existing systems expand memory to autonomous agents, then the agents can handle more tasks, but hardware costs increase
Solution Approach 1:
The patent creates a universal agent registry system where a single centralized database stores cryptographic hash values representing multiple autonomous agent functionalities. This universal structure serves all service requests across the distributed system, eliminating the need for each agent to have expanded local memory. The system achieves multi-functionality through centralized resource sharing rather than individual agent expansion
Solution Approach 2:
The system enables autonomous agents to self-identify and self-deploy based on service request requirements. When a service request arrives, the system automatically queries the agent registry, identifies matching agents via hash value comparison, and deploys appropriate agents without human intervention. This self-service mechanism eliminates the need for expanded memory in agents, as the matching logic resides in the centralized registry
Data Source
AI summary
The present disclosure provides a distributed computer system that comprises worker nodes that are coupled together via a data communication network to exchange data therebetween, wherein the worker nodes include computing arrangements and local databases to process and store data therein. The worker nodes are autonomous agents (AAs), wherein the distributed computer system is configured to use the worker nodes for fulfilling a service request. The distributed computer system also comprises a processing arrangement that generates a bloom tree, driven by cryptographic hash values, ensuring accurate autonomous agent functionality identification, while proof-based decision-making enhances task execution.