Learnability with PAC Semantics for Multi-agent Beliefs

Ionela Mocanu; Brendan Juba; Vaishak Belle

doi:10.5555/3545946.3599016

Learnability with PAC Semantics for Multi-agent Beliefs

Ionela Mocanu, Brendan Juba, Vaishak Belle

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

Abstract / Description of output

This work proposes a new technical foundation for demonstrating Probably Approximately Correct (PAC) learning with multiagent epistemic logics, using implicit learning to incorporate observations into the background knowledge. We explore the sample complexity and the circumstances in which the algorithm can be made efficient.

Original language	English
Title of host publication	Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
Place of Publication	Richland, SC
Publisher	International Foundation for Autonomous Agents and Multiagent Systems
Pages	2604–2606
Number of pages	3
ISBN (Electronic)	9781450394321
DOIs	https://doi.org/10.5555/3545946.3599016
Publication status	Published - 30 May 2023
Event	The 22nd International Conference on Autonomous Agents and Multiagent Systems - ExCel London, London, United Kingdom Duration: 29 May 2023 → 2 Jun 2023 Conference number: 22 https://aamas2023.soton.ac.uk/

Conference

Conference	The 22nd International Conference on Autonomous Agents and Multiagent Systems
Abbreviated title	AAMAS 2023
Country/Territory	United Kingdom
City	London
Period	29/05/23 → 2/06/23
Internet address	https://aamas2023.soton.ac.uk/

Access to Document

10.5555/3545946.3599016Licence: All Rights Reserved

Learnability with_MOCANU_DOA_04012023_AFV_CC-BYAccepted author manuscript, 429 KBLicence: Creative Commons: Attribution (CC-BY)

https://dl.acm.org/doi/10.5555/3545946.3599016Licence: All Rights Reserved

1 Finished

UKRI Trustworthy Autonomous Systems Node in Governance and Regulation
Ramamoorthy, R., Belle, V., Bundy, A., Jackson, P., Lascarides, A. & Rajan, A.
EPSRC
1/11/20 → 30/04/24
Project: Research

1 Article

Learnability with PAC Semantics for Multi-agent Beliefs
Mocanu, I., Belle, V. & Juba, B., 31 Jul 2023, In: Theory and Practice of Logic Programming. 23, 4, p. 730-747 18 p.
Research output: Contribution to journal › Article › peer-review

Open Access
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Cite this

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title = "Learnability with PAC Semantics for Multi-agent Beliefs",

abstract = "This work proposes a new technical foundation for demonstrating Probably Approximately Correct (PAC) learning with multiagent epistemic logics, using implicit learning to incorporate observations into the background knowledge. We explore the sample complexity and the circumstances in which the algorithm can be made efficient.",

author = "Ionela Mocanu and Brendan Juba and Vaishak Belle",

note = "Ionela Georgiana Mocanu was supported by the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Pervasive Parallelism (grant EP/L01503X/1) at the University of Edinburgh, School of Informatics. Vaishak Belle was partly supported by a Royal Society University Research Fellowship, UK, and partly supported by a grant from the UKRI Strategic Priorities Fund, UK to the UKRI Research Node on Trustworthy Autonomous Systems Governance and Regulation (EP/V026607/1, 2020–2024); The 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 ; Conference date: 29-05-2023 Through 02-06-2023",

year = "2023",

month = may,

day = "30",

doi = "10.5555/3545946.3599016",

language = "English",

pages = "2604–2606",

booktitle = "Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS)",

publisher = "International Foundation for Autonomous Agents and Multiagent Systems",

url = "https://aamas2023.soton.ac.uk/",

}

Mocanu, I, Juba, B & Belle, V 2023, Learnability with PAC Semantics for Multi-agent Beliefs. in Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, pp. 2604–2606, The 22nd International Conference on Autonomous Agents and Multiagent Systems, London, United Kingdom, 29/05/23. https://doi.org/10.5555/3545946.3599016

Learnability with PAC Semantics for Multi-agent Beliefs. / Mocanu, Ionela; Juba, Brendan; Belle, Vaishak.
Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2023. p. 2604–2606.

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

TY - GEN

T1 - Learnability with PAC Semantics for Multi-agent Beliefs

AU - Mocanu, Ionela

AU - Juba, Brendan

AU - Belle, Vaishak

N1 - Conference code: 22

PY - 2023/5/30

Y1 - 2023/5/30

N2 - This work proposes a new technical foundation for demonstrating Probably Approximately Correct (PAC) learning with multiagent epistemic logics, using implicit learning to incorporate observations into the background knowledge. We explore the sample complexity and the circumstances in which the algorithm can be made efficient.

AB - This work proposes a new technical foundation for demonstrating Probably Approximately Correct (PAC) learning with multiagent epistemic logics, using implicit learning to incorporate observations into the background knowledge. We explore the sample complexity and the circumstances in which the algorithm can be made efficient.

U2 - 10.5555/3545946.3599016

DO - 10.5555/3545946.3599016

M3 - Conference contribution

SP - 2604

EP - 2606

BT - Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

PB - International Foundation for Autonomous Agents and Multiagent Systems

CY - Richland, SC

T2 - The 22nd International Conference on Autonomous Agents and Multiagent Systems

Y2 - 29 May 2023 through 2 June 2023

ER -

Learnability with PAC Semantics for Multi-agent Beliefs

Abstract / Description of output

Conference

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Projects

UKRI Trustworthy Autonomous Systems Node in Governance and Regulation

Research output

Learnability with PAC Semantics for Multi-agent Beliefs

Cite this