TY - GEN
T1 - Proactive dynamic DCOPs
AU - Hoang, Khoi
AU - Fioretto, Ferdinando
AU - Hou, Ping
AU - Yokoo, Makoto
AU - Yeoh, William
AU - Zivan, Roie
N1 - Publisher Copyright:
Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016
Y1 - 2016
N2 - The current approaches to model dynamism in DCOPs solve a sequence of static problems, reacting to the changes in the environment as the agents observe them. Such approaches, thus, ignore possible predictions on the environment evolution. To overcome such limitations, we introduce the Proactive Dynamic DCOP (PD-DCOP) model, a novel formalism to model dynamic DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly model the possible changes to the problem, and take such information into account proactively, when solving the dynamically changing problem.
AB - The current approaches to model dynamism in DCOPs solve a sequence of static problems, reacting to the changes in the environment as the agents observe them. Such approaches, thus, ignore possible predictions on the environment evolution. To overcome such limitations, we introduce the Proactive Dynamic DCOP (PD-DCOP) model, a novel formalism to model dynamic DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly model the possible changes to the problem, and take such information into account proactively, when solving the dynamically changing problem.
UR - http://www.scopus.com/inward/record.url?scp=85022029584&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85022029584&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85022029584
T3 - AAAI Workshop - Technical Report
SP - 233
EP - 240
BT - WS-16-01
PB - AI Access Foundation
T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
Y2 - 12 February 2016 through 17 February 2016
ER -