TY - GEN
T1 - Preference elicitation for DCOPs
AU - Tabakhi, Atena M.
AU - Le, Tiep
AU - Fioretto, Ferdinando
AU - Yeoh, William
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Distributed Constraint Optimization Problems (DCOPs) offer a powerful approach for the description and resolution of cooperative multi-agent problems. In this model, a group of agents coordinate their actions to optimize a global objective function, taking into account their preferences or constraints. A core limitation of this model is the assumption that the preferences of all agents or the costs of all constraints are specified a priori. Unfortunately, this assumption does not hold in a number of application domains where preferences or constraints must be elicited from the users. One of such domains is the Smart Home Device Scheduling (SHDS) problem. Motivated by this limitation, we make the following contributions in this paper: (1) We propose a general model for preference elicitation in DCOPs; (2) We propose several heuristics to elicit preferences in DCOPs; and (3) We empirically evaluate the effect of these heuristics on random binary DCOPs as well as SHDS problems.
AB - Distributed Constraint Optimization Problems (DCOPs) offer a powerful approach for the description and resolution of cooperative multi-agent problems. In this model, a group of agents coordinate their actions to optimize a global objective function, taking into account their preferences or constraints. A core limitation of this model is the assumption that the preferences of all agents or the costs of all constraints are specified a priori. Unfortunately, this assumption does not hold in a number of application domains where preferences or constraints must be elicited from the users. One of such domains is the Smart Home Device Scheduling (SHDS) problem. Motivated by this limitation, we make the following contributions in this paper: (1) We propose a general model for preference elicitation in DCOPs; (2) We propose several heuristics to elicit preferences in DCOPs; and (3) We empirically evaluate the effect of these heuristics on random binary DCOPs as well as SHDS problems.
KW - Distributed constraint optimization
KW - Preference elicitation
KW - Smart homes
UR - http://www.scopus.com/inward/record.url?scp=85028724100&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028724100&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66158-2_18
DO - 10.1007/978-3-319-66158-2_18
M3 - Conference contribution
AN - SCOPUS:85028724100
SN - 9783319661575
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 278
EP - 296
BT - Principles and Practice of Constraint Programming - 23rd International Conference CP 2017, Proceedings
A2 - Beck, J.Christopher
PB - Springer Verlag
T2 - 23rd International Conference on the Principles and Practice of Constraint Programming, CP 2017
Y2 - 28 August 2017 through 1 September 2017
ER -