Compiled Rule Sets for Software Agent Adaptability
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Solution Overview
Problem
Existing rule-based systems for software agents are inefficient due to the need for static logic compilation and high CPU usage, particularly in environments with limited resources, making it challenging to construct and execute agents with complex rule sets.
Innovation Solution
A system and method for constructing software agents using a set of compiled, canonical rules that can be executed in a rule-based system, allowing for local or remote construction and movement, with the ability to request and compile rules from a repository or server, and manage working memory, optimizing performance by reducing memory consumption and CPU usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If procedural programming is used to implement agent logic, then the agent can be constructed with explicit control flow, but the logic becomes static and tightly coupled with the agent software, reducing adaptability
Solution Approach 1:
The patent introduces a rule engine as an intermediary component that separates agent logic from agent software. The rule engine loads and executes rules independently, allowing the agent to adapt its behavior by loading different rule sets without modifying the agent's core software structure. This mediator layer enables dynamic adaptability while maintaining clean separation of concerns.
Solution Approach 2:
The patent segments agent logic into independent, loadable rule modules that can be constructed and executed separately from the agent software. Each rule is a discrete unit that can be individually managed, loaded, and unloaded, allowing the agent to adapt by combining different rule segments rather than relying on monolithic procedural code.
2Adaptability or versatility
If complex rule sets are loaded into the agent, then the agent can handle sophisticated tasks, but memory consumption increases, particularly in resource-constrained environments
Solution Approach 1:
The patent implements preliminary compilation of rule sets into optimized intermediate representations before they are loaded into the agent. This pre-processing step transforms complex rule sets into more compact forms that consume less memory during execution, while still maintaining full functionality for handling sophisticated tasks.
Solution Approach 2:
The patent changes the representation parameters of rules by transforming them from human-readable text formats into compiled binary or intermediate formats. This parameter transformation reduces the memory footprint of rule sets while preserving their logical structure and execution capabilities, enabling complex rule sets to run in resource-constrained environments.
3Speed
If rules are compiled and executed in the agent's execution environment, then execution speed improves, but CPU usage increases, making it challenging for environments with limited computational resources
Solution Approach 1:
The patent implements dynamic rule execution by allowing the agent to load, unload, and switch between different rule sets based on current task requirements. Instead of continuously executing all possible rules, the system dynamically activates only the necessary rules for the current context, reducing CPU usage while maintaining execution speed for active rules.
Solution Approach 2:
The patent applies partial action by executing only the subset of rules relevant to the current agent task rather than compiling and executing the entire rule set. This selective execution approach reduces CPU consumption by focusing computational resources on necessary rules while maintaining fast execution for those specific rules.
Data Source
AI summary
A method for constructing an agent in a first execution environment that utilizes a set of compiled, canonical rules, comprising requesting a canonical rule set for the agent, compiling the rule set and supplying the agent with the compiled rule set.