Boolean games: Inferring agents' goals using taxation queries

Abhijin Adiga; Sarit Kraus; Oleg Maksimov; S. S. Ravi

Boolean games: Inferring agents' goals using taxation queries

Abhijin Adiga, Sarit Kraus, Oleg Maksimov, S. S. Ravi

University of Virginia

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

Abstract

In Boolean games, each agent controls a set of Boolean variables and has a goal represented by a propositional formula. We study inference problems in Boolean games assuming the presence of a PRINCIPAL who has the ability to control the agents and impose taxation schemes. Previous work used taxation schemes to guide a game towards certain equilibria. We present algorithms that show how taxation schemes can also be used to infer agents' goals. We present experimental results to demonstrate the efficacy our algorithms. We also consider goal inference when only limited information is available in response to a query.

Original language	English
Title of host publication	Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
Editors	Christian Bessiere
Publisher	International Joint Conferences on Artificial Intelligence
Pages	1585-1591
Number of pages	7
ISBN (Electronic)	9780999241165
State	Published - 2020
Event	29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan Duration: 1 Jan 2021 → …

Publication series

Name	IJCAI International Joint Conference on Artificial Intelligence
Volume	2021-January
ISSN (Print)	1045-0823

Conference

Conference	29th International Joint Conference on Artificial Intelligence, IJCAI 2020
Country/Territory	Japan
City	Yokohama
Period	1/01/21 → …

Bibliographical note

Publisher Copyright:
© 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

Funding

We thank the IJCAI 2020 reviewers for their comments. This work was partially supported by NSF Grants ACI-1443054 (DIBBS), IIS-1633028 (BIG DATA), CMMI-1745207 (EAGER), OAC-1916805, IIS-1908530 and by the Ministry of Science & Technology, Israel and the Ministry of Education, Science, Research and Sport of the Slovak Republic.

Funders	Funder number
Ministry of Education, Science, Research and Sport
National Science Foundation	CMMI-1745207, ACI-1443054, IIS-1908530, OAC-1916805, IIS-1633028
Ministry of science and technology, Israel

Cite this

Adiga, A., Kraus, S., Maksimov, O., & Ravi, S. S. (2020). Boolean games: Inferring agents' goals using taxation queries. In C. Bessiere (Ed.), Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 (pp. 1585-1591). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2021-January). International Joint Conferences on Artificial Intelligence.

Adiga, Abhijin ; Kraus, Sarit ; Maksimov, Oleg et al. / Boolean games : Inferring agents' goals using taxation queries. Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020. editor / Christian Bessiere. International Joint Conferences on Artificial Intelligence, 2020. pp. 1585-1591 (IJCAI International Joint Conference on Artificial Intelligence).

@inproceedings{ab0ae75079db49a49ea62b905a40b1c2,

title = "Boolean games: Inferring agents' goals using taxation queries",

abstract = "In Boolean games, each agent controls a set of Boolean variables and has a goal represented by a propositional formula. We study inference problems in Boolean games assuming the presence of a PRINCIPAL who has the ability to control the agents and impose taxation schemes. Previous work used taxation schemes to guide a game towards certain equilibria. We present algorithms that show how taxation schemes can also be used to infer agents' goals. We present experimental results to demonstrate the efficacy our algorithms. We also consider goal inference when only limited information is available in response to a query.",

author = "Abhijin Adiga and Sarit Kraus and Oleg Maksimov and Ravi, {S. S.}",

note = "Publisher Copyright: {\textcopyright} 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.; 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 ; Conference date: 01-01-2021",

year = "2020",

language = "אנגלית",

series = "IJCAI International Joint Conference on Artificial Intelligence",

publisher = "International Joint Conferences on Artificial Intelligence",

pages = "1585--1591",

editor = "Christian Bessiere",

booktitle = "Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020",

}

Adiga, A, Kraus, S, Maksimov, O & Ravi, SS 2020, Boolean games: Inferring agents' goals using taxation queries. in C Bessiere (ed.), Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020. IJCAI International Joint Conference on Artificial Intelligence, vol. 2021-January, International Joint Conferences on Artificial Intelligence, pp. 1585-1591, 29th International Joint Conference on Artificial Intelligence, IJCAI 2020, Yokohama, Japan, 1/01/21.

Boolean games: Inferring agents' goals using taxation queries. / Adiga, Abhijin; Kraus, Sarit; Maksimov, Oleg et al.
Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020. ed. / Christian Bessiere. International Joint Conferences on Artificial Intelligence, 2020. p. 1585-1591 (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2021-January).

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

TY - GEN

T1 - Boolean games

T2 - 29th International Joint Conference on Artificial Intelligence, IJCAI 2020

AU - Adiga, Abhijin

AU - Kraus, Sarit

AU - Maksimov, Oleg

AU - Ravi, S. S.

PY - 2020

Y1 - 2020

N2 - In Boolean games, each agent controls a set of Boolean variables and has a goal represented by a propositional formula. We study inference problems in Boolean games assuming the presence of a PRINCIPAL who has the ability to control the agents and impose taxation schemes. Previous work used taxation schemes to guide a game towards certain equilibria. We present algorithms that show how taxation schemes can also be used to infer agents' goals. We present experimental results to demonstrate the efficacy our algorithms. We also consider goal inference when only limited information is available in response to a query.

AB - In Boolean games, each agent controls a set of Boolean variables and has a goal represented by a propositional formula. We study inference problems in Boolean games assuming the presence of a PRINCIPAL who has the ability to control the agents and impose taxation schemes. Previous work used taxation schemes to guide a game towards certain equilibria. We present algorithms that show how taxation schemes can also be used to infer agents' goals. We present experimental results to demonstrate the efficacy our algorithms. We also consider goal inference when only limited information is available in response to a query.

UR - http://www.scopus.com/inward/record.url?scp=85097332668&partnerID=8YFLogxK

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85097332668

T3 - IJCAI International Joint Conference on Artificial Intelligence

SP - 1585

EP - 1591

BT - Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020

A2 - Bessiere, Christian

PB - International Joint Conferences on Artificial Intelligence

Y2 - 1 January 2021

ER -

Boolean games: Inferring agents' goals using taxation queries

Abstract

Publication series

Conference

Bibliographical note

Funding

Other files and links

Fingerprint

Cite this