Agent Data Packaging via Canonical Rule Segmentation
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Solution Overview
Problem
Existing rule-based systems for software agents face inefficiencies in handling large rule sets, particularly in terms of memory consumption and computational speed, especially when agents move between execution environments with varying resources, leading to the need for a method to utilize data narrowing rules effectively.
Innovation Solution
A system and method for determining data packaging requirements of agents using a rule engine with canonical data narrowing rules, allowing agents to collect, execute, and adapt data narrowing rules within execution environments, including discarding unnecessary rules and optimizing network bandwidth usage based on environment-specific conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a complete rule set is carried by the agent, then the agent can operate in any execution environment, but memory consumption increases and performance degrades
Solution Approach 1:
The rule set is segmented into two parts: a compact canonical rule set carried by the agent, and environment-specific data narrowing rules stored externally. The agent collects only the necessary data narrowing rules from the target execution environment, avoiding the need to carry the complete rule set while maintaining full operational capability.
Solution Approach 2:
The canonical rule set is prepared in advance with placeholders for data narrowing rules. When the agent moves to a new execution environment, the rule engine automatically collects the appropriate data narrowing rules from the environment's rule repository, preparing the complete rule set just in time for operation.
2Loss of information
If data is transmitted in full detail, then data completeness is maintained, but network bandwidth consumption increases
Solution Approach 1:
Data transmission is optimized based on the specific execution environment's characteristics. Data narrowing rules are applied locally to filter and compress data according to environment-specific requirements, ensuring that only necessary data details are transmitted over the network while maintaining completeness of essential information.
Solution Approach 2:
The data transmission parameters are dynamically changed based on environment conditions. The rule engine collects data narrowing rules that modify data packaging parameters such as compression level, filtering criteria, and prioritization schemes, allowing the same data to be transmitted efficiently under different network conditions.
3Adaptability or versatility
If the agent carries a large rule set, then it can handle complex tasks, but computational speed decreases due to rule matching overhead
Solution Approach 1:
Environment-specific data narrowing rules are extracted from the target execution environment's rule repository and integrated into the agent's rule engine. This allows the agent to access complex task-handling rules without carrying them all, as rules are collected on-demand from the external environment, reducing the immediate rule matching overhead while maintaining full task handling capability.
Data Source
AI summary
A method for determining a data package requirement of an agent in an execution environment, utilizing a rule engine and a set of canonical data narrowing rules, comprising collecting the data narrowing rule, asserting narrowing data into a working memory and executing the rule engine with the data narrowing rules and the working memory.