Continual Learning Blocks for Modular Skill Layering in AI Agents
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Solution Overview
Problem
Conventional systems for training artificial agents lack control over learned skills and knowledge, fail to prioritize important skills, and are unable to layer skills and knowledge modularly for higher-level learning, as well as learn specific knowledge during skill execution.
Innovation Solution
A method for training artificial agents through continual learning blocks, where each block defines a skill to achieve a goal configuration with a prediction of distance, duration, or vector-valued functions, and relies on previous learned blocks to achieve modular and efficient skill development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional training systems are used, then the agent can learn skills and knowledge, but the user lacks control over which skills and knowledge are learned
Solution Approach 1:
The training system is segmented into discrete continual learning blocks, where each block teaches a specific skill or knowledge component. This segmentation enables users to control and select which skills are learned by choosing specific blocks, while maintaining manageable system complexity through modular organization.
2Adaptability or versatility
If conventional training systems are used, then the agent learns skills, but the skills cannot be layered modularly for higher-level learning
Solution Approach 1:
Skills are segmented into independent continual learning blocks that can be selectively combined and layered. Each block represents a discrete skill unit that can be stacked modularly to build higher-level competencies, enabling flexible skill composition without complex organizational structures.
Solution Approach 2:
Continual learning blocks are designed to be nested and compositional, where simpler skill blocks can be contained within and combined to form more complex skill structures. This nesting enables modular layering of skills from basic to advanced levels.
3Loss of information
If conventional training systems are used, then the agent executes skills, but cannot learn specific knowledge predictions during execution
Solution Approach 1:
The system merges skill execution with knowledge prediction learning within a unified continual learning block structure. Each block simultaneously handles skill performance and teaches predictive knowledge about task outcomes, eliminating information loss without requiring separate complex learning mechanisms.
Data Source
AI summary
A method and system for teaching an artificial intelligent agent includes giving the agent several examples where it can learn to identify what is important about these example states. Once the agent has the ability to recognize a goal configuration, it can use that information to then learn how to achieve the goal states on its own. An agent may be provided with positive and negative examples to demonstrate a goal configuration. Once the agent has learned certain goal configurations, the agent can learn an option to achieve the goal configuration and a distance function that predicts at least one of a distance and a duration to the goal configuration under the learned option. This distance function prediction may be incorporated as a state feature of the agent.