Shared Machine Learning Model With Expertise Weighting
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Solution Overview
Problem
Current machine learning technologies are cumbersome for independently building agents that learn and adapt to specific events, and creating a shared learning environment remains elusive, making it difficult to develop a generic, non-task-specific learning system that can be accessed and contributed to by other agents or systems.
Innovation Solution
A method and system for shared machine learning that provides a model with attributes and attribute value data types to multiple agents, allowing them to receive and integrate inputs, determine expertise weights based on ground-truth values, and use adaptive mixtures to estimate attribute values, enabling a shared learning paradigm where agents can collectively leverage each other's insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If agents independently build separate learning systems for specific tasks, then task-specific learning capability is improved, but system complexity and difficulty of integration increases
Solution Approach 1:
The patent combines multiple independent learning systems into a unified framework where agents can share learning experiences and models. The system integrates task-specific learning capabilities while maintaining a common architecture that reduces complexity through standardized interfaces and shared knowledge representation.
Solution Approach 2:
The learning system is designed with universal components that can serve multiple tasks and agents simultaneously. The framework provides multi-functional capabilities allowing the same infrastructure to support different learning scenarios, reducing the need for separate specialized systems.
2Adaptability or versatility
If a shared learning environment is created for generic learning, then reusability and collaboration between agents is improved, but difficulty in implementing learning systems increases
Solution Approach 1:
The shared learning environment is segmented into modular components including experience storage, model training modules, and evaluation systems. This segmentation allows independent development and implementation of each component, reducing overall implementation difficulty while maintaining shared learning capabilities.
Solution Approach 2:
The patent introduces intermediary layers and standardized protocols that facilitate communication and data exchange between different learning systems. These intermediaries simplify integration by providing uniform interfaces, reducing the complexity of implementing shared learning environments.
3Ease of operation
If machine learning technology is made more accessible and easier to use, then ease of building learning systems is improved, but loss of specialized learning capabilities may occur
Solution Approach 1:
The system implements local quality by allowing different agents and tasks to have customized learning configurations and parameters while operating within a unified framework. Each agent can maintain specialized learning capabilities tailored to its specific needs without requiring complex independent system development.
Data Source
AI summary
A system and method are provided for shared machine learning. The method includes providing a model to a plurality of agents included in a machine learning system. The model specifies attributes and attribute value data types for an event in which the agents act. The method further includes receiving agent-provided inputs during an instance of the event. The agent-provided inputs include estimated attribute values that are consistent with the attribute value data types. The method also includes determining expertise weights for at least some agents in response to at least one ground-truth which is learned from the estimated attribute values. The method additionally includes determining an estimate value for one or more of the attributes using respective adaptive mixtures of the estimated attribute values.