A Paradigm for Safe Adaptation of Collaborating Robots

Emilia Cioroaica; Barbora Buhnova; Emrah Tomur

doi:10.1145/3524844.3528061

A Paradigm for Safe Adaptation of Collaborating Robots

Emilia Cioroaica, Barbora Buhnova, Emrah Tomur

School of Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

17 Downloads (Pure)

Abstract

The dynamic forces that transit back and forth traditional boundaries of system development have led to the emergence of digital ecosystems. Within these, business gains are achieved through the development of intelligent control that requires a continuous design and runtime co-engineering process endangered by malicious attacks. The possibility of inserting specially crafted faults capable to exploit the nature of unknown evolving intelligent behavior raises the necessity of malicious behavior detection at runtime.Adjusting to the needs and opportunities of fast AI development within digital ecosystems, in this paper, we envision a novel method and framework for runtime predictive evaluation of intelligent robots' behavior for assuring a cooperative safe adjustment.

Original language	English
Title of host publication	Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022
Publisher	The Institute of Electrical and Electronics Engineers (IEEE)
Pages	113-119
Number of pages	7
ISBN (Electronic)	9781450393058
DOIs	https://doi.org/10.1145/3524844.3528061
Publication status	Published - 15 Aug 2022
Event	17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022 - Pittsburgh, United States Duration: 18 May 2022 → 20 May 2022

Publication series

Name	Proceedings - Symposia on Software Engineering for Adaptive and Self-Managing Systems

Conference

Conference	17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022
Country/Territory	United States
City	Pittsburgh
Period	18/05/22 → 20/05/22

Keywords

Building Trust
Robots
Runtime Prediction
Safety-Critical Systems
Virtual Evaluation

Access to Document

10.1145/3524844.3528061Licence: Other

Cioroaica_etal_2022
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Final published version, 374 KBLicence: Other

Cite this

Cioroaica, E., Buhnova, B., & Tomur, E. (2022). A Paradigm for Safe Adaptation of Collaborating Robots. In Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022 (pp. 113-119). (Proceedings - Symposia on Software Engineering for Adaptive and Self-Managing Systems). The Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1145/3524844.3528061

Cioroaica, Emilia ; Buhnova, Barbora ; Tomur, Emrah. / A Paradigm for Safe Adaptation of Collaborating Robots. Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022. The Institute of Electrical and Electronics Engineers (IEEE), 2022. pp. 113-119 (Proceedings - Symposia on Software Engineering for Adaptive and Self-Managing Systems).

@inproceedings{acb9e32036214cc6bf4c03a6652cfc73,

title = "A Paradigm for Safe Adaptation of Collaborating Robots",

abstract = "The dynamic forces that transit back and forth traditional boundaries of system development have led to the emergence of digital ecosystems. Within these, business gains are achieved through the development of intelligent control that requires a continuous design and runtime co-engineering process endangered by malicious attacks. The possibility of inserting specially crafted faults capable to exploit the nature of unknown evolving intelligent behavior raises the necessity of malicious behavior detection at runtime.Adjusting to the needs and opportunities of fast AI development within digital ecosystems, in this paper, we envision a novel method and framework for runtime predictive evaluation of intelligent robots' behavior for assuring a cooperative safe adjustment.",

keywords = "Building Trust, Robots, Runtime Prediction, Safety-Critical Systems, Virtual Evaluation",

author = "Emilia Cioroaica and Barbora Buhnova and Emrah Tomur",

note = "Funding Information: The work was supported This work was supported by the project BIECO (www.bieco.org) that received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement No. 952702 and by ERDF/ESF {"}CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence{"} (No. CZ.02.1.01/0.0/0.0/16_019/0000822). Publisher Copyright: {\textcopyright} 2022 ACM.; 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022 ; Conference date: 18-05-2022 Through 20-05-2022",

year = "2022",

month = aug,

day = "15",

doi = "10.1145/3524844.3528061",

language = "English",

series = "Proceedings - Symposia on Software Engineering for Adaptive and Self-Managing Systems",

publisher = "The Institute of Electrical and Electronics Engineers (IEEE)",

pages = "113--119",

booktitle = "Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022",

address = "United States",

}

Cioroaica, E, Buhnova, B & Tomur, E 2022, A Paradigm for Safe Adaptation of Collaborating Robots. in Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022. Proceedings - Symposia on Software Engineering for Adaptive and Self-Managing Systems, The Institute of Electrical and Electronics Engineers (IEEE), pp. 113-119, 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022, Pittsburgh, United States, 18/05/22. https://doi.org/10.1145/3524844.3528061

A Paradigm for Safe Adaptation of Collaborating Robots. / Cioroaica, Emilia; Buhnova, Barbora; Tomur, Emrah.

Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022. The Institute of Electrical and Electronics Engineers (IEEE), 2022. p. 113-119 (Proceedings - Symposia on Software Engineering for Adaptive and Self-Managing Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A Paradigm for Safe Adaptation of Collaborating Robots

AU - Cioroaica, Emilia

AU - Buhnova, Barbora

AU - Tomur, Emrah

N1 - Funding Information: The work was supported This work was supported by the project BIECO (www.bieco.org) that received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 952702 and by ERDF/ESF "CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence" (No. CZ.02.1.01/0.0/0.0/16_019/0000822). Publisher Copyright: © 2022 ACM.

PY - 2022/8/15

Y1 - 2022/8/15

N2 - The dynamic forces that transit back and forth traditional boundaries of system development have led to the emergence of digital ecosystems. Within these, business gains are achieved through the development of intelligent control that requires a continuous design and runtime co-engineering process endangered by malicious attacks. The possibility of inserting specially crafted faults capable to exploit the nature of unknown evolving intelligent behavior raises the necessity of malicious behavior detection at runtime.Adjusting to the needs and opportunities of fast AI development within digital ecosystems, in this paper, we envision a novel method and framework for runtime predictive evaluation of intelligent robots' behavior for assuring a cooperative safe adjustment.

AB - The dynamic forces that transit back and forth traditional boundaries of system development have led to the emergence of digital ecosystems. Within these, business gains are achieved through the development of intelligent control that requires a continuous design and runtime co-engineering process endangered by malicious attacks. The possibility of inserting specially crafted faults capable to exploit the nature of unknown evolving intelligent behavior raises the necessity of malicious behavior detection at runtime.Adjusting to the needs and opportunities of fast AI development within digital ecosystems, in this paper, we envision a novel method and framework for runtime predictive evaluation of intelligent robots' behavior for assuring a cooperative safe adjustment.

KW - Building Trust

KW - Robots

KW - Runtime Prediction

KW - Safety-Critical Systems

KW - Virtual Evaluation

UR - http://www.scopus.com/inward/record.url?scp=85134177369&partnerID=8YFLogxK

U2 - 10.1145/3524844.3528061

DO - 10.1145/3524844.3528061

M3 - Conference contribution

AN - SCOPUS:85134177369

T3 - Proceedings - Symposia on Software Engineering for Adaptive and Self-Managing Systems

SP - 113

EP - 119

BT - Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022

PB - The Institute of Electrical and Electronics Engineers (IEEE)

T2 - 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022

Y2 - 18 May 2022 through 20 May 2022

ER -

Cioroaica E, Buhnova B, Tomur E. A Paradigm for Safe Adaptation of Collaborating Robots. In Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022. The Institute of Electrical and Electronics Engineers (IEEE). 2022. p. 113-119. (Proceedings - Symposia on Software Engineering for Adaptive and Self-Managing Systems). doi: 10.1145/3524844.3528061

A Paradigm for Safe Adaptation of Collaborating Robots

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Cite this