TY - JOUR
T1 - Communication-Aware Local Search for Distributed Constraint Optimization.
AU - Rachmut, Ben
AU - Zivan, Roie
AU - Yeoh, William
N1 - Funding Information:
This research is partially supported by US-Israel Binational Science Foundation (BSF) grant #2018081 and US National Science Foundation (NSF) grant #1838364.
Publisher Copyright:
© 2022 AI Access Foundation. All rights reserved.
PY - 2022/10
Y1 - 2022/10
N2 - Most studies investigating models and algorithms for distributed constraint optimization problems (DCOPs) assume that messages arrive instantaneously and are never lost. Specifically, distributed local search DCOP algorithms, have been designed as synchronous algorithms (i.e., they perform in synchronous iterations in which each agent exchanges messages with all its neighbors), despite running in asynchronous environments. This is true also for an anytime mechanism that reports the best solution explored during the run of synchronous distributed local search algorithms. Thus, when the assumption of perfect communication is relaxed, the properties that were established for the state-of-the-art local search algorithms and the anytime mechanism may not necessarily apply.
AB - Most studies investigating models and algorithms for distributed constraint optimization problems (DCOPs) assume that messages arrive instantaneously and are never lost. Specifically, distributed local search DCOP algorithms, have been designed as synchronous algorithms (i.e., they perform in synchronous iterations in which each agent exchanges messages with all its neighbors), despite running in asynchronous environments. This is true also for an anytime mechanism that reports the best solution explored during the run of synchronous distributed local search algorithms. Thus, when the assumption of perfect communication is relaxed, the properties that were established for the state-of-the-art local search algorithms and the anytime mechanism may not necessarily apply.
UR - http://www.scopus.com/inward/record.url?scp=85142099694&partnerID=8YFLogxK
U2 - 10.1613/jair.1.13826
DO - 10.1613/jair.1.13826
M3 - Article
SN - 1076-9757
VL - 75
SP - 637
EP - 675
JO - Journal of Artificial Intelligence Research
JF - Journal of Artificial Intelligence Research
ER -