Reinforcement Learning for Dynamic Service Request Construction
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Solution Overview
Problem
Existing approaches to constructing service requests and providing configuration items are limited by the need for manual organization of labels and sufficient training examples, making them labor-intensive and inflexible to handle new request types and exceptions.
Innovation Solution
A reinforcement learning algorithm is applied to detect unhealthy system states, determine possible actions, and construct service requests, allowing for adaptive and exploratory path determination without relying on fixed training data or user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a hierarchical multi-label classification approach is used to construct service requests, then the system can provide structured service catalogs, but it requires sufficient training examples and prior organization of labels into a hierarchy, making it labor-intensive and inflexible to new request types
Solution Approach 1:
The patent transitions from a static hierarchical classification system to a dynamic reinforcement learning system that adapts to new service request types and attributes without requiring manual reorganization of label hierarchies. The system learns optimal service request constructions through continuous interaction and feedback, enabling it to dynamically adjust to changing requirements while maintaining construction accuracy.
Solution Approach 2:
The reinforcement learning system enables the service catalog to self-organize and self-improve without human intervention. The system automatically learns from interactions, identifies patterns, and optimizes service request constructions autonomously, eliminating the labor-intensive manual label organization while maintaining or improving construction reliability.
2Stability of the object's composition
If manual service catalog creation is used ahead of time, then a structured service catalog can be established, but it is not easily adaptable to new request types and their attributes
Solution Approach 1:
The patent replaces the static manual service catalog with a dynamic reinforcement learning-based system that maintains structural integrity while adapting to new request types. The system continuously learns from new data and interactions, allowing the service catalog structure to evolve and accommodate emerging service types without requiring complete manual recreation.
Solution Approach 2:
The reinforcement learning system creates a universal service catalog framework that can handle diverse service request types and attributes through a single adaptive system. Rather than creating separate manual structures for different service types, the system learns universal patterns and relationships that apply across multiple service domains, enabling versatile adaptation to new request types while maintaining overall structural stability.
3Measurement precision
If a supervised learning approach is used to learn service request patterns, then the system can achieve accurate predictions, but it requires sufficient training examples (xi, yi) to learn a best possible hypothesis h(x)
Solution Approach 1:
The patent implements a feedback-driven reinforcement learning system where the service catalog continuously learns from the outcomes of service request constructions. Rather than requiring extensive pre-collected training data, the system learns through feedback loops where each service request and its result contribute to improving future predictions. This allows the system to achieve high prediction accuracy with progressively fewer additional training examples as it learns from real-world interactions.
Solution Approach 2:
The system performs preliminary exploration and learning through reinforcement learning before full deployment, gradually building up prediction accuracy through iterative interactions. Rather than requiring all training data to be available upfront, the system performs preliminary actions to gather necessary learning data in a controlled manner, reducing the immediate quantity of training data needed while achieving accurate predictions over time.
Data Source
AI summary
An embodiment includes a method for use in managing a system comprising one or more computers, each computer comprising at least one hardware processor coupled to at least one memory. The method comprises a computer-implemented manager: detecting that the system is in an unhealthy state; determining a set of one or more possible actions to remedy the unhealthy state of the system; selecting at least one action of the set of one or more possible actions; and constructing a service request implementing the selected at least one action; wherein at least one of the detecting, determining, selecting, and constructing is based at least in part on applying a reinforcement learning algorithm.