Software Agent Rule Merging for Resource-Constrained Environments
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Solution Overview
Problem
Existing rule-based systems for software agents are limited by their static logic and inefficiencies in handling large rule sets, leading to performance issues on devices with limited computational resources, and there is a need to construct agents that can merge and utilize both supplied and environment-specific rules effectively.
Innovation Solution
A system and method for constructing software agents that retrieve and merge canonical rules from both a supplied rule set and an execution environment's rule repository, compiling these rules into a merged set for use in a rule engine, allowing agents to adapt and operate efficiently across varying environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If agents are programmed with static logic compiled into agent software, then the agent structure is simple, but the agent cannot adapt to different environments and complex goals become intractable
Solution Approach 1:
The patent segments the agent into distinct components: a rule engine that executes rules and a working memory that stores data. This separation allows the agent to maintain a simple core structure while achieving adaptability through external rule sets that can be dynamically loaded and modified without changing the agent's fundamental architecture.
Solution Approach 2:
The patent introduces a rule set as an intermediary between the agent's simple execution engine and the complex requirements of different environments. The rule set acts as a configurable layer that translates environmental requirements into executable instructions, allowing the same agent structure to adapt to various contexts by swapping rule sets.
2Adaptability or versatility
If a large set of rules is provided to the agent, then the agent can handle complex goals, but performance issues occur on devices with limited computational resources
Solution Approach 1:
The patent implements dynamic rule set management where the agent can load, unload, and modify rule sets based on current needs. Instead of maintaining a static large rule set, the agent dynamically activates only the rules necessary for the current task, reducing computational overhead on resource-constrained devices while maintaining the capability to handle complex goals when needed.
Solution Approach 2:
The patent changes the parameter of rule set size from fixed to variable. The agent can adjust the number and complexity of active rules based on available computational resources and task requirements, allowing optimal performance across different device configurations while maintaining the ability to handle complex goals when resources permit.
3Ease of manufacture
If procedural programming is used to program agents, then the implementation is straightforward, but the logic is tightly coupled and cannot be easily modified
Solution Approach 1:
The patent segments the agent's logic into a separate rule set that is distinct from the execution engine. This segmentation maintains implementation ease by providing a clear, structured approach to agent creation while simultaneously improving modularity, as the rule set can be independently modified, updated, or swapped without affecting the core execution logic.
Solution Approach 2:
The patent introduces a rule set as an intermediary layer between the simple execution engine and the complex domain logic. This intermediary maintains ease of implementation by providing a standardized rule format while enabling high modularity, as the rule set can be independently developed, tested, and modified to adapt to different requirements without changing the engine itself.
4Adaptability or versatility
If the agent carries all necessary rules when moving between execution environments, then the agent is self-sufficient, but the burden on devices with limited resources increases
Solution Approach 1:
The patent makes the rule engine universal by designing it to execute rules from various sources (embedded rules, remotely loaded rules, environment-specific rules). This universality allows the agent to maintain a small, lightweight core that can adapt to different environments by loading appropriate rule sets, reducing the weight of moving the agent while maintaining environment independence.
Solution Approach 2:
The patent uses the rule set as an intermediary that can be selectively loaded from different sources depending on the execution environment. Instead of embedding all possible rules in the agent, the rule set acts as a mediator that can be supplied from the environment or loaded remotely, reducing agent size while maintaining the ability to operate independently in various contexts.
Data Source
AI summary
A method for constructing an agent in an execution environment that utilizes a set of supplied canonical rules and a set of environment resident canonical rules comprising retrieving a first canonical rule set for the agent, retrieving a second canonical rule set for the agent and merging the two rule sets.