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

no code implementations • 21 Apr 2022 • Aizaz Ul Haq, Niranjana Deshpande, AbdElRahman ElSaid, Travis Desell, Daniel E. Krutz

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.

Decision Making

Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data

no code implementations • 16 Nov 2021 • Yuansheng Zhu, Weishi Shi, Deep Shankar Pandey, Yang Liu, Xiaofan Que, Daniel E. Krutz, Qi Yu

We propose a novel framework to classify large-scale time series data with long duration.

Multiple Instance Learning Time Series +1

Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility

no code implementations • 23 Apr 2020 • Jeffrey Palmerino, Qi Yu, Travis Desell, Daniel E. Krutz

Unfortunately, current self-adaptive approaches do not account for tactic volatility in their decision-making processes, and merely assume that tactics do not experience volatility.

Decision Making Time Series +1