Sensor Placement for Plan Monitoring using Genetic Programming

Felipe  Meneguzzi; Ramon Fraga  Pereira; Nir Oren

doi:10.1007/978-3-030-03098-8_40

Sensor Placement for Plan Monitoring using Genetic Programming

Felipe Meneguzzi, Ramon Fraga Pereira, Nir Oren

Computing Science

Pontifícia Universidade Católica do Rio Grande do Sul

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

1 Citation (Scopus)

4 Downloads (Pure)

Abstract

Monitoring plan execution is useful in various multi-agent applications, from agent cooperation to norm enforcement. Realistic environments often impose constraints on the capabilities of such monitoring, limiting the amount and coverage of available sensors. In this paper, we consider the problem of sensor placement within an environment to determine whether some behaviour has occurred. Our model is based on the semantics of planning, and we provide a simple formalism
for describing sensors and behaviours in such a model. Given the computational
complexity of the sensor placement problem, we investigate heuristic techniques for performing sensor placement, demonstrating that such techniques perform well even in complex domains.

Original language	English
Title of host publication	PRIMA 2018
Subtitle of host publication	Principles and Practice of Multi-Agent Systems. PRIMA 2018
Editors	Tim Miller, Nir Oren, Yuko Sakurai, Itsuki Noda, Bastin Tony Roy Savarimuthu, Tran Cao Son
Publisher	Springer
Pages	544-551
Number of pages	8
ISBN (Electronic)	9783030030988
ISBN (Print)	9783030030971
DOIs	https://doi.org/10.1007/978-3-030-03098-8_40
Publication status	Published - Nov 2018
Event	PRIMA 2018: The 21st International Conference on Principles and Practice of Multi-Agent Systems - AIST Tokyo Waterfront, Tokyo, Japan Duration: 31 Oct 2018 → 2 Nov 2018

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11224
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	PRIMA 2018: The 21st International Conference on Principles and Practice of Multi-Agent Systems
Country/Territory	Japan
City	Tokyo
Period	31/10/18 → 2/11/18

Access to Document

10.1007/978-3-030-03098-8_40Licence: Unspecified

Accepted Manuscript
This is a post-peer-review, pre-copyedit version of an article published in PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science, vol 11224. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-03098-8_40.
Accepted author manuscript, 343 KBLicence: Unspecified

Cite this

Meneguzzi, F., Pereira, R. F., & Oren, N. (2018). Sensor Placement for Plan Monitoring using Genetic Programming. In T. Miller, N. Oren, Y. Sakurai, I. Noda, B. T. R. Savarimuthu, & T. C. Son (Eds.), PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018 (pp. 544-551). (Lecture Notes in Computer Science; Vol. 11224). Springer . https://doi.org/10.1007/978-3-030-03098-8_40

Sensor Placement for Plan Monitoring using Genetic Programming. / Meneguzzi, Felipe ; Pereira, Ramon Fraga ; Oren, Nir.
PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. ed. / Tim Miller; Nir Oren; Yuko Sakurai; Itsuki Noda; Bastin Tony Roy Savarimuthu; Tran Cao Son. Springer , 2018. p. 544-551 (Lecture Notes in Computer Science; Vol. 11224).

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

Meneguzzi, F, Pereira, RF & Oren, N 2018, Sensor Placement for Plan Monitoring using Genetic Programming. in T Miller, N Oren, Y Sakurai, I Noda, BTR Savarimuthu & TC Son (eds), PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science, vol. 11224, Springer , pp. 544-551, PRIMA 2018: The 21st International Conference on Principles and Practice of Multi-Agent Systems, Tokyo, Japan, 31/10/18. https://doi.org/10.1007/978-3-030-03098-8_40

@inproceedings{b41e75d58f5a43f68b2b6fc52d739481,

title = "Sensor Placement for Plan Monitoring using Genetic Programming",

abstract = "Monitoring plan execution is useful in various multi-agent applications, from agent cooperation to norm enforcement. Realistic environments often impose constraints on the capabilities of such monitoring, limiting the amount and coverage of available sensors. In this paper, we consider the problem of sensor placement within an environment to determine whether some behaviour has occurred. Our model is based on the semantics of planning, and we provide a simple formalismfor describing sensors and behaviours in such a model. Given the computationalcomplexity of the sensor placement problem, we investigate heuristic techniques for performing sensor placement, demonstrating that such techniques perform well even in complex domains.",

author = "Felipe Meneguzzi and Pereira, {Ramon Fraga} and Nir Oren",

year = "2018",

month = nov,

doi = "10.1007/978-3-030-03098-8_40",

language = "English",

isbn = "9783030030971",

series = "Lecture Notes in Computer Science",

publisher = "Springer ",

pages = "544--551",

editor = "Tim Miller and Nir Oren and Yuko Sakurai and Itsuki Noda and Savarimuthu, {Bastin Tony Roy} and Son, {Tran Cao}",

booktitle = "PRIMA 2018",

note = "PRIMA 2018: The 21st International Conference on Principles and Practice of Multi-Agent Systems ; Conference date: 31-10-2018 Through 02-11-2018",

}

TY - GEN

T1 - Sensor Placement for Plan Monitoring using Genetic Programming

AU - Meneguzzi, Felipe

AU - Pereira, Ramon Fraga

AU - Oren, Nir

PY - 2018/11

Y1 - 2018/11

N2 - Monitoring plan execution is useful in various multi-agent applications, from agent cooperation to norm enforcement. Realistic environments often impose constraints on the capabilities of such monitoring, limiting the amount and coverage of available sensors. In this paper, we consider the problem of sensor placement within an environment to determine whether some behaviour has occurred. Our model is based on the semantics of planning, and we provide a simple formalismfor describing sensors and behaviours in such a model. Given the computationalcomplexity of the sensor placement problem, we investigate heuristic techniques for performing sensor placement, demonstrating that such techniques perform well even in complex domains.

AB - Monitoring plan execution is useful in various multi-agent applications, from agent cooperation to norm enforcement. Realistic environments often impose constraints on the capabilities of such monitoring, limiting the amount and coverage of available sensors. In this paper, we consider the problem of sensor placement within an environment to determine whether some behaviour has occurred. Our model is based on the semantics of planning, and we provide a simple formalismfor describing sensors and behaviours in such a model. Given the computationalcomplexity of the sensor placement problem, we investigate heuristic techniques for performing sensor placement, demonstrating that such techniques perform well even in complex domains.

U2 - 10.1007/978-3-030-03098-8_40

DO - 10.1007/978-3-030-03098-8_40

M3 - Published conference contribution

SN - 9783030030971

T3 - Lecture Notes in Computer Science

SP - 544

EP - 551

BT - PRIMA 2018

A2 - Miller, Tim

A2 - Oren, Nir

A2 - Sakurai, Yuko

A2 - Noda, Itsuki

A2 - Savarimuthu, Bastin Tony Roy

A2 - Son, Tran Cao

PB - Springer

T2 - PRIMA 2018: The 21st International Conference on Principles and Practice of Multi-Agent Systems

Y2 - 31 October 2018 through 2 November 2018

ER -

Sensor Placement for Plan Monitoring using Genetic Programming

Abstract

Publication series

Conference

Access to Document

Fingerprint

Cite this