DeepAI AI Chat
Log In Sign Up

Forming Ensembles at Runtime: A Machine Learning Approach

04/30/2021
by   Tomáš Bureš, et al.
0

Smart system applications (SSAs) built on top of cyber-physical and socio-technical systems are increasingly composed of components that can work both autonomously and by cooperating with each other. Cooperating robots, fleets of cars and fleets of drones, emergency coordination systems are examples of SSAs. One approach to enable cooperation of SSAs is to form dynamic cooperation groups-ensembles-between components at runtime. Ensembles can be formed based on predefined rules that determine which components should be part of an ensemble based on their current state and the state of the environment (e.g., "group together 3 robots that are closer to the obstacle, their battery is sufficient and they would not be better used in another ensemble"). This is a computationally hard problem since all components are potential members of all possible ensembles at runtime. In our experience working with ensembles in several case studies the past years, using constraint programming to decide which ensembles should be formed does not scale for more than a limited number of components and ensembles. Also, the strict formulation in terms of hard/soft constraints does not easily permit for runtime self-adaptation via learning. This poses a serious limitation to the use of ensembles in large-scale and partially uncertain SSAs. To tackle this problem, in this paper we propose to recast the ensemble formation problem as a classification problem and use machine learning to efficiently form ensembles at scale.

READ FULL TEXT
05/21/2012

Soft Rule Ensembles for Statistical Learning

In this article supervised learning problems are solved using soft rule ...
04/17/2020

A stochastic approach to handle knapsack problems in the creation of ensembles

Ensemble-based methods are highly popular approaches that increase the a...
04/12/2023

Boosted Prompt Ensembles for Large Language Models

Methods such as chain-of-thought prompting and self-consistency have pus...
06/22/2021

Repulsive Deep Ensembles are Bayesian

Deep ensembles have recently gained popularity in the deep learning comm...
12/10/2020

Ensemble Squared: A Meta AutoML System

The continuing rise in the number of problems amenable to machine learni...
05/02/2023

Cubature rules for unitary Jacobi ensembles

We present Chebyshev type cubature rules for the exact integration of ra...
08/27/2022

The Ghost of Performance Reproducibility Past

The importance of ensemble computing is well established. However, execu...