Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability.

Shashank Shekhar; Ronen I. Brafman; Guy Shani

Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability.

Shashank Shekhar, Ronen I. Brafman, Guy Shani

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

2 Scopus citations

Abstract

Collaborative Multi-Agent Planning (MAP) problems with uncertainty and partial observability are often modeled as Dec-POMDPs. Yet, in deterministic domains, Qualitative Dec-POMDPs can scale up to much larger problem sizes. The best current QDec solver (QDec-FP) reduces MAP problems to multiple single-agent problems. In this paper we describe a planner that uses richer information about agents' knowledge to improve upon QDec-FP. With this change, the planner not only scales up to larger problems with more objects, but it can also support signaling, where agents signal information to each other by changing the state of the world.

Original language	English
Title of host publication	35th AAAI Conference on Artificial Intelligence, AAAI 2021
Publisher	Association for the Advancement of Artificial Intelligence
Pages	11954-11961
Number of pages	8
Volume	35
ISBN (Electronic)	9781713835974
State	Published - 18 May 2021
Event	35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online Duration: 2 Feb 2021 → 9 Feb 2021

Conference

Conference	35th AAAI Conference on Artificial Intelligence, AAAI 2021
City	Virtual, Online
Period	2/02/21 → 9/02/21

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

https://ojs.aaai.org/index.php/AAAI/article/view/17420

Cite this

@inproceedings{928d6a9494534825a63d7d099f4dbc9e,

title = "Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability.",

abstract = "Collaborative Multi-Agent Planning (MAP) problems with uncertainty and partial observability are often modeled as Dec-POMDPs. Yet, in deterministic domains, Qualitative Dec-POMDPs can scale up to much larger problem sizes. The best current QDec solver (QDec-FP) reduces MAP problems to multiple single-agent problems. In this paper we describe a planner that uses richer information about agents' knowledge to improve upon QDec-FP. With this change, the planner not only scales up to larger problems with more objects, but it can also support signaling, where agents signal information to each other by changing the state of the world. ",

author = "Shashank Shekhar and Brafman, {Ronen I.} and Guy Shani",

note = "Funding Information: This work was supported by ISF Grants 1651/19 and 1210/18, by the Israel Ministry of Science and Technology Grant 54178, and by the Lynn and William Frankel Center for Computer Science. Publisher Copyright: {\textcopyright} 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 35th AAAI Conference on Artificial Intelligence, AAAI 2021 ; Conference date: 02-02-2021 Through 09-02-2021",

year = "2021",

month = may,

day = "18",

language = "English",

volume = "35",

pages = "11954--11961",

booktitle = "35th AAAI Conference on Artificial Intelligence, AAAI 2021",

publisher = "Association for the Advancement of Artificial Intelligence",

}

Shekhar, S, Brafman, RI & Shani, G 2021, Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability. in 35th AAAI Conference on Artificial Intelligence, AAAI 2021. vol. 35, Association for the Advancement of Artificial Intelligence, pp. 11954-11961, 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2/02/21. <https://ojs.aaai.org/index.php/AAAI/article/view/17420>

Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability. / Shekhar, Shashank; Brafman, Ronen I.; Shani, Guy.
35th AAAI Conference on Artificial Intelligence, AAAI 2021. Vol. 35 Association for the Advancement of Artificial Intelligence, 2021. p. 11954-11961.

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

TY - GEN

T1 - Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability.

AU - Shekhar, Shashank

AU - Brafman, Ronen I.

AU - Shani, Guy

N1 - Funding Information: This work was supported by ISF Grants 1651/19 and 1210/18, by the Israel Ministry of Science and Technology Grant 54178, and by the Lynn and William Frankel Center for Computer Science. Publisher Copyright: © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

PY - 2021/5/18

Y1 - 2021/5/18

N2 - Collaborative Multi-Agent Planning (MAP) problems with uncertainty and partial observability are often modeled as Dec-POMDPs. Yet, in deterministic domains, Qualitative Dec-POMDPs can scale up to much larger problem sizes. The best current QDec solver (QDec-FP) reduces MAP problems to multiple single-agent problems. In this paper we describe a planner that uses richer information about agents' knowledge to improve upon QDec-FP. With this change, the planner not only scales up to larger problems with more objects, but it can also support signaling, where agents signal information to each other by changing the state of the world.

AB - Collaborative Multi-Agent Planning (MAP) problems with uncertainty and partial observability are often modeled as Dec-POMDPs. Yet, in deterministic domains, Qualitative Dec-POMDPs can scale up to much larger problem sizes. The best current QDec solver (QDec-FP) reduces MAP problems to multiple single-agent problems. In this paper we describe a planner that uses richer information about agents' knowledge to improve upon QDec-FP. With this change, the planner not only scales up to larger problems with more objects, but it can also support signaling, where agents signal information to each other by changing the state of the world.

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M3 - Conference contribution

VL - 35

SP - 11954

EP - 11961

BT - 35th AAAI Conference on Artificial Intelligence, AAAI 2021

PB - Association for the Advancement of Artificial Intelligence

T2 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021

Y2 - 2 February 2021 through 9 February 2021

ER -

Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability.

Abstract

Conference

ASJC Scopus subject areas

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