Safeguard Module for AI Network Control Stability
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Solution Overview
Problem
Current AI-based network control systems lack effective safeguards, particularly in closed-loop Reinforcement Learning systems, leading to potential negative actions, chaotic behavior, and ineffective reward functions, with multiple AI systems potentially agreeing on inappropriate actions or learning inappropriate behaviors.
Innovation Solution
An AI-based network control system with a safeguard module that independently obtains network data, develops deterministic decisions, and compares them with ML algorithm actions, allowing, blocking, or modifying actions based on these decisions, and can interact with other safeguard modules and receive operator input for feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI systems are used for autonomous network control, then productivity and adaptability are improved, but reliability and stability deteriorate due to potential negative actions and chaotic behavior
Solution Approach 1:
A safeguard module is introduced as an intermediary component between the AI system and the network control plane. This safeguard module monitors AI decisions, compares them against deterministic policies, and can block or modify actions that violate safety constraints, thereby maintaining reliability while allowing AI-driven productivity improvements
Solution Approach 2:
Deterministic safety policies are pre-configured in the safeguard module to prevent negative actions before they occur. These policies establish predetermined boundaries and constraints that the AI system must not violate, proactively preventing chaotic behavior and instability
2Measurement precision
If multiple AI systems are used to check actions against one another, then decision accuracy is improved, but device complexity increases
Solution Approach 1:
The control system is segmented into distinct functional components: the AI system for optimization decisions and the safeguard module for safety verification. This segmentation allows each component to specialize in its function rather than requiring multiple redundant AI systems, reducing overall complexity while maintaining decision accuracy
3Ease of operation
If AI systems operate independently without safeguards, then ease of operation is improved, but harmful factors increase due to potential negative actions
Solution Approach 1:
The safeguard module implements continuous feedback monitoring of AI decisions against predetermined safety constraints. When the AI system generates actions that could lead to harmful outcomes, the feedback mechanism detects these violations and triggers corrective actions to prevent negative effects
Data Source
AI summary
Artificial Intelligence (AI)-based network control includes obtaining data from a network having a plurality of network elements; analyzing the data with one or more Machine Learning (ML) algorithms to determine one or more actions for network control; analyzing the determined one or more actions to determine any risks associated therewith; and one of allowing, modifying, and blocking the determined one or more actions based on the determined risks to safeguard the network. The risks can be based on one or more of (1) non-deterministic behavior AI inference which is statistical in nature, (2) unbounded uncertainty of the AI inference that can result in arbitrarily large inaccuracy on rare occasions, (3) unpredictable behavior of the AI inference in presence of input data that is different than data in training and testing datasets, and (4) malicious input data.