# Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics

arXiv: https://arxiv.org/abs/2204.10308 · HTML: https://mlsanity.com/paper/2204.10308/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Recurrent Neural Networks for Time Series Forecasting](https://mlsanity.com/paper/1901.00069/) (2019)
- [Neuroevolution in Games: State of the Art and Open Challenges](https://mlsanity.com/paper/1410.7326/) (2014)
- [Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution](https://mlsanity.com/paper/1902.02390/) (2019)
- [Low-Cost Recurrent Neural Network Expected Performance Evaluation](https://mlsanity.com/paper/1805.07159/) (2018)
- [Cyber-Physical Security and Safety of Autonomous Connected Vehicles: Optimal Control Meets Multi-Armed Bandit Learning](https://mlsanity.com/paper/1812.05298/) (2018)
- [Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility](https://mlsanity.com/paper/2004.11302/) (2020)
- [Accelerating the Evolution of Convolutional Neural Networks with Node-Level Mutations and Epigenetic Weight Initialization](https://mlsanity.com/paper/1811.08286/) (2018)
- [Random Error Sampling-based Recurrent Neural Network Architecture Optimization](https://mlsanity.com/paper/1909.02425/) (2019)

## Abstract (arXiv)

Self-adaptive systems frequently use tactics to perform adaptations. Tactic examples include the implementation of additional security measures when an intrusion is detected, or activating a cooling mechanism when temperature thresholds are surpassed. Tactic volatility occurs in real-world systems and is defined as variable behavior in the attributes of a tactic, such as its latency or cost. A system's inability to effectively account for tactic volatility adversely impacts its efficiency and resiliency against the dynamics of real-world environments. To enable systems' efficiency against tactic volatility, we propose a Tactic Volatility Aware (TVA-E) process utilizing evolved Recurrent Neural Networks (eRNN) to provide accurate tactic predictions. TVA-E is also the first known process to take advantage of uncertainty reduction tactics to provide additional information to the decision-making process and reduce uncertainty. TVA-E easily integrates into popular adaptation processes enabling it to immediately benefit a large number of existing self-adaptive systems. Simulations using 52,106 tactic records demonstrate that: I) eRNN is an effective prediction mechanism, II) TVA-E represents an improvement over existing state-of-the-art processes in accounting for tactic volatility, and III) Uncertainty reduction tactics are beneficial in accounting for tactic volatility. The developed dataset and tool can be found at https://tacticvolatility.github.io/