IPAC2022 - List of Authors (Boese, M.)

Paper	Title	Page
TUPOST058	Badger: The Missing Optimizer in ACR	999
	Z. Zhang, A.L. Edelen, J.R. Garrahan, C.E. Mayes, S.A. Miskovich, D.F. Ratner, R.J. Roussel, J. Shtalenkova SLAC, Menlo Park, California, USA M. Böse, S. Tomin DESY, Hamburg, Germany Y. Hidaka, G.M. Wang BNL, Upton, New York, USA
	Badger is an optimizer specifically designed for Accelerator Control Room (ACR). It’s the spiritual successor of Ocelot optimizer. Badger abstracts an optimization run as an optimization algorithm interacts with an environment, by following some pre-defined rules. The environment is controlled by the algorithm and tunes/observes the control system/machine through an interface, while the users control/monitor the optimization flow through a graphical user interface (GUI) or a command line interface (CLI). This paper would introduce the design principles and applications of Badger.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST058
About •	Received ※ 08 June 2022 — Revised ※ 10 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 17 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPOMS016	A Pipeline for Orchestrating Machine Learning and Controls Applications	1439
	I.V. Agapov, M. Böse, L. Malina DESY, Hamburg, Germany
	Machine learning and artificial intelligence are becoming widespread paradigms in control of complex processes. Operation of accelerator facilities is not an exception, with a number of advances having happened over the last years. In the domain of intelligent control of accelerator facilities, the research has mostly been focused on feasibility demonstration of ML-based agents, or application of ML-based agents to a well-defined problem such as parameter tuning. The main challenge on the way to a more holistic AI-based operation, in our opinion, is of engineering nature and is related to the need of significant reduction of the amount of human intervention. The areas where such intervention is still significant are: training and tuning of ML models; scheduling and orchestrating of multiple intelligent agents; data stream handling; configuration management; and software testing and verification requiring advanced simulation environment. We have developed a software framework which attempts to address all these issues. The design and implementation of this system will be presented, together with application examples for the PETRA III storage ring.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOMS016
About •	Received ※ 09 June 2022 — Revised ※ 15 June 2022 — Accepted ※ 17 June 2022 — Issue date ※ 25 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Badger: The Missing Optimizer in ACR

999

Z. Zhang, A.L. Edelen, J.R. Garrahan, C.E. Mayes, S.A. Miskovich, D.F. Ratner, R.J. Roussel, J. Shtalenkova
SLAC, Menlo Park, California, USA
M. Böse, S. Tomin
DESY, Hamburg, Germany
Y. Hidaka, G.M. Wang
BNL, Upton, New York, USA

Badger is an optimizer specifically designed for Accelerator Control Room (ACR). It’s the spiritual successor of Ocelot optimizer. Badger abstracts an optimization run as an optimization algorithm interacts with an environment, by following some pre-defined rules. The environment is controlled by the algorithm and tunes/observes the control system/machine through an interface, while the users control/monitor the optimization flow through a graphical user interface (GUI) or a command line interface (CLI). This paper would introduce the design principles and applications of Badger.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST058

About •

Received ※ 08 June 2022 — Revised ※ 10 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 17 June 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPOMS016

A Pipeline for Orchestrating Machine Learning and Controls Applications

1439

I.V. Agapov, M. Böse, L. Malina
DESY, Hamburg, Germany

Machine learning and artificial intelligence are becoming widespread paradigms in control of complex processes. Operation of accelerator facilities is not an exception, with a number of advances having happened over the last years. In the domain of intelligent control of accelerator facilities, the research has mostly been focused on feasibility demonstration of ML-based agents, or application of ML-based agents to a well-defined problem such as parameter tuning. The main challenge on the way to a more holistic AI-based operation, in our opinion, is of engineering nature and is related to the need of significant reduction of the amount of human intervention. The areas where such intervention is still significant are: training and tuning of ML models; scheduling and orchestrating of multiple intelligent agents; data stream handling; configuration management; and software testing and verification requiring advanced simulation environment. We have developed a software framework which attempts to address all these issues. The design and implementation of this system will be presented, together with application examples for the PETRA III storage ring.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOMS016

About •

Received ※ 09 June 2022 — Revised ※ 15 June 2022 — Accepted ※ 17 June 2022 — Issue date ※ 25 June 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)