Artificial Agent Goal Learning Using Feature Extraction and Feedback
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Solution Overview
Problem
Current intelligent control systems for artificial agents suffer from brittleness and poor generalization, necessitating improvements in training and control systems.
Innovation Solution
A method for training artificial agents by defining goal configurations, providing positive and negative examples, extracting key state features, and using a distance function as an intermediate reward to enhance learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional intelligent control systems are used for training artificial agents, then the agents can learn basic tasks, but the agents exhibit brittleness and poor generalization
Solution Approach 1:
The patent segments the training process into distinct phases: demonstrating goal configurations with positive examples, providing negative examples of failed attempts, and extracting key state features from successful demonstrations. This segmentation allows the agent to learn systematically from structured feedback, improving both reliability and generalization by breaking down complex learning into manageable components
Solution Approach 2:
The system implements a feedback mechanism where the agent receives positive examples when goal configurations are achieved and negative examples when they fail. This feedback loop enables the agent to iteratively improve its policies by learning from both successes and failures, thereby enhancing reliability while maintaining adaptability through continuous refinement
2Reliability
If more training examples are provided to improve agent learning, then the agent's performance improves, but the training time and computational resources increase
Solution Approach 1:
The patent extracts only the key state features from training examples rather than processing entire state sequences. By identifying and extracting the most relevant features from positive and negative examples, the system reduces the computational burden and training time while maintaining learning effectiveness, as the agent focuses on critical discriminative features rather than all possible state variables
Solution Approach 2:
The system uses a distance function as an intermediate reward that provides partial guidance toward the goal configuration rather than requiring complete goal achievement. This partial action approach allows the agent to receive feedback at intermediate stages, accelerating learning by providing more frequent training signals without requiring full task completion for each learning step
Data Source
AI summary
A method and system for teaching an artificial intelligent agent where the agent can be placed in a state that it would like it to learn how to achieve. By giving the agent several examples, 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 policies and skills that achieve the learned goal configuration. The agent may create a collection of these policies and skills from which to select based on a particular command or state.