2 research outputs found
AsymDPOP: Complete Inference for Asymmetric Distributed Constraint Optimization Problems
Asymmetric distributed constraint optimization problems (ADCOPs) are an
emerging model for coordinating agents with personal preferences. However, the
existing inference-based complete algorithms which use local eliminations
cannot be applied to ADCOPs, as the parent agents are required to transfer
their private functions to their children. Rather than disclosing private
functions explicitly to facilitate local eliminations, we solve the problem by
enforcing delayed eliminations and propose AsymDPOP, the first inference-based
complete algorithm for ADCOPs. To solve the severe scalability problems
incurred by delayed eliminations, we propose to reduce the memory consumption
by propagating a set of smaller utility tables instead of a joint utility
table, and to reduce the computation efforts by sequential optimizations
instead of joint optimizations. The empirical evaluation indicates that
AsymDPOP significantly outperforms the state-of-the-arts, as well as the
vanilla DPOP with PEAV formulation
Distributed Constraint Optimization Problems and Applications: A Survey
The field of Multi-Agent System (MAS) is an active area of research within
Artificial Intelligence, with an increasingly important impact in industrial
and other real-world applications. Within a MAS, autonomous agents interact to
pursue personal interests and/or to achieve common objectives. Distributed
Constraint Optimization Problems (DCOPs) have emerged as one of the prominent
agent architectures to govern the agents' autonomous behavior, where both
algorithms and communication models are driven by the structure of the specific
problem. During the last decade, several extensions to the DCOP model have
enabled them to support MAS in complex, real-time, and uncertain environments.
This survey aims at providing an overview of the DCOP model, giving a
classification of its multiple extensions and addressing both resolution
methods and applications that find a natural mapping within each class of
DCOPs. The proposed classification suggests several future perspectives for
DCOP extensions, and identifies challenges in the design of efficient
resolution algorithms, possibly through the adaptation of strategies from
different areas