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

Shashank Shekhar; Ronen I. Brafman; Guy Shani

doi:10.1609/aaai.v35i13.17420

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

Shashank Shekhar, Ronen I. Brafman, Guy Shani

Ben-Gurion University of the Negev

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

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	American English
Title of host publication	35th AAAI Conference on Artificial Intelligence, AAAI 2021
Pages	11954-11961
Number of pages	8
ISBN (Electronic)	9781713835974
DOIs	https://doi.org/10.1609/aaai.v35i13.17420
State	Published - 1 Jan 2021
Event	35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online Duration: 2 Feb 2021 → 9 Feb 2021

Publication series

Name	35th AAAI Conference on Artificial Intelligence, AAAI 2021
Volume	13B

Conference

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

All Science Journal Classification (ASJC) codes

Artificial Intelligence

Access to Document

10.1609/aaai.v35i13.17420

Cite this

@inproceedings{a6e54dc93691474bbf2c44275a631053,

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 = "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 = jan,

day = "1",

doi = "10.1609/aaai.v35i13.17420",

language = "American English",

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

pages = "11954--11961",

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

}

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. 35th AAAI Conference on Artificial Intelligence, AAAI 2021, vol. 13B, pp. 11954-11961, 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2/02/21. https://doi.org/10.1609/aaai.v35i13.17420

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. 2021. p. 11954-11961 (35th AAAI Conference on Artificial Intelligence, AAAI 2021; Vol. 13B).

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

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AU - Brafman, Ronen I.

AU - Shani, Guy

PY - 2021/1/1

Y1 - 2021/1/1

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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U2 - 10.1609/aaai.v35i13.17420

DO - 10.1609/aaai.v35i13.17420

M3 - Conference contribution

T3 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021

SP - 11954

EP - 11961

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

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

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