TY - GEN
T1 - Facility location games with optional preference
AU - YUAN, Hongning
AU - WANG, Kai
AU - FONG, Ken C.K.
AU - ZHANG, Yong
AU - LI, Minming
N1 - This research was partially supported by NSFC 61433012 and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 117913].
PY - 2016
Y1 - 2016
N2 - In this paper, we propose the optional preference model for the facility location game with two heterogeneous facilities on a line. Agents in this new model are allowed to have optional preference, which gives more flexibility for agents to report. Aiming at minimizing maximum cost or sum cost of agents, we propose different deterministic strategy-proof mechanisms without monetary transfers. Depending on which facility the agent with optional preference cares for, we consider two variants of the optional preference model: Min (caring for the closer one) and Max (caring for the further one). For the Min variant, we propose a 2-approximation mechanism for the maximum cost objective, as well as a lower bound of 4/3, and a (n/2+1)-approximation mechanism for the sum cost objective, as well as a lower bound of 2. For Max variant, we propose an optimal mechanism for the maximum cost objective and a 2-approximation mechanism for the sum cost objective.
AB - In this paper, we propose the optional preference model for the facility location game with two heterogeneous facilities on a line. Agents in this new model are allowed to have optional preference, which gives more flexibility for agents to report. Aiming at minimizing maximum cost or sum cost of agents, we propose different deterministic strategy-proof mechanisms without monetary transfers. Depending on which facility the agent with optional preference cares for, we consider two variants of the optional preference model: Min (caring for the closer one) and Max (caring for the further one). For the Min variant, we propose a 2-approximation mechanism for the maximum cost objective, as well as a lower bound of 4/3, and a (n/2+1)-approximation mechanism for the sum cost objective, as well as a lower bound of 2. For Max variant, we propose an optimal mechanism for the maximum cost objective and a 2-approximation mechanism for the sum cost objective.
UR - http://www.scopus.com/inward/record.url?scp=85013115723&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-672-9-1520
DO - 10.3233/978-1-61499-672-9-1520
M3 - Conference paper (refereed)
AN - SCOPUS:85013115723
SN - 9781614996712
T3 - Frontiers in Artificial Intelligence and Applications
SP - 1520
EP - 1527
BT - ECAI 2016
A2 - KAMINKA, Gal A.
A2 - DIGNUM, Frank
A2 - HÜLLERMEIER, Eyke
A2 - BOUQUET, Paolo
A2 - DIGNUM, Virginia
A2 - FOX, Maria
A2 - VAN HARMELEN, Frank
PB - IOS Press
CY - Netherlands
T2 - 22nd European Conference on Artificial Intelligence, ECAI 2016
Y2 - 29 August 2016 through 2 September 2016
ER -