TY - GEN
T1 - Incomplete Distributed Constraint Optimization Problems: Model, Algorithms, and Heuristics.
T2 - 3rd International Conference on Distributed Artificial Intelligence, DAI 2021
AU - Tabakhi, Atena M.
AU - Yeoh, William
AU - Zivan, Roie
N1 - Funding Information:
This research is partially supported by BSF grant #2018081 and NSF grants #1812619 and #1838364.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool to model cooperative multi-agent problems, especially when they are sparsely constrained with one another. A key assumption in this model is that all constraints are fully specified or known a priori, which may not hold in applications where constraints encode preferences of human users. In this paper, we extend the model to Incomplete DCOPs (I-DCOPs), where some constraints can be partially specified. User preferences for these partially-specified constraints can be elicited during the execution of I-DCOP algorithms, but they incur some elicitation costs. Additionally, we extend SyncBB, a complete DCOP algorithm, and ALS-MGM, an incomplete DCOP algorithm, to solve I-DCOPs. We also propose parameterized heuristics that those algorithms can utilize to trade off solution quality for faster runtime and fewer elicitation. They also provide theoretical quality guarantees when used by SyncBB when elicitations are free. Our model and heuristics thus extend the state-of-the-art in distributed constraint reasoning to better model and solve distributed agent-based applications with user preferences.
AB - The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool to model cooperative multi-agent problems, especially when they are sparsely constrained with one another. A key assumption in this model is that all constraints are fully specified or known a priori, which may not hold in applications where constraints encode preferences of human users. In this paper, we extend the model to Incomplete DCOPs (I-DCOPs), where some constraints can be partially specified. User preferences for these partially-specified constraints can be elicited during the execution of I-DCOP algorithms, but they incur some elicitation costs. Additionally, we extend SyncBB, a complete DCOP algorithm, and ALS-MGM, an incomplete DCOP algorithm, to solve I-DCOPs. We also propose parameterized heuristics that those algorithms can utilize to trade off solution quality for faster runtime and fewer elicitation. They also provide theoretical quality guarantees when used by SyncBB when elicitations are free. Our model and heuristics thus extend the state-of-the-art in distributed constraint reasoning to better model and solve distributed agent-based applications with user preferences.
KW - Distributed constraint optimization problems
KW - Distributed problem solving
KW - Multi-agent problems
KW - Preference elicitation
UR - http://www.scopus.com/inward/record.url?scp=85123431451&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-94662-3_5
DO - 10.1007/978-3-030-94662-3_5
M3 - Conference contribution
SN - 9783030946616
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 64
EP - 78
BT - International Conference on Distributed Artificial Intelligence
A2 - Chen, Jie
A2 - Lang, Jérôme
A2 - Amato, Christopher
A2 - Zhao, Dengji
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 17 December 2021 through 18 December 2021
ER -