![](bg8.png)
was a side-effect where part of evaluation was con-
ditionally skipped due to the threshold based on the
globally maximum cost value.
5 DISCUSSION
There are several decentralized min-max problem set-
tings that require several assumptions including the
convexity and continuity of objective functions. How-
ever, the aim of DCOPs with similar criteria is general
discrete problems with non-convexity. To solve large-
scale and complex problems of these classes, inexact
methods are necessary. As a baseline investigation,
we addressed a class of stochastic hill-climb methods
that is an extension of a fundamental approach for tra-
ditional DCOPs.
An approximation of complete solution methods
for AMODCOPs that are based on tree search and dy-
namic programming has been presented. However,
since the approximation partially eliminates or ig-
nores several constraints, its accuracy decreases when
the induced width (Fioretto et al., 2018) of constraint
graphs is relatively larger. In such situations, there are
opportunities to employ stochastic local search meth-
ods. While deterministic local search methods for this
class of problems have been addressed in early stud-
ies, we investigated stochastic local search methods
that also employ some additional information.
In the original DSA and several variants, the prob-
ability of the algorithms is generally employed to de-
termine whether the agents select the hill-climb or
stay as is. Therefore, the methods are basically local
search that cannot escape from locally optimal solu-
tions. We mainly investigated the practical case where
the agents are basically greedy but stochastically es-
cape from locally optimal solutions.
6 CONCLUSION
We investigated the decentralized stochastic search
methods for Asymmetric Multi-Objective Distributed
Constraint Optimization Problems considering the
worst cases among the agents. The experimental
results show the effect and influence of different
optimization criteria and additional information in
stochastic local search processes. In particular, the re-
sults show that the minimization of the maximum cost
value is not straightforward with min-max based cri-
teria that are also designed to improve the global ob-
jective value or fairness. Min-max optimization with
several additional bits of information performs better
perturbation. We believe such an investigation of fun-
damental search methods for different optimization
criteria can provide a foundation for more sophisti-
cated sampling based methods for extended classes
of DCOPs. Our future work will investigate other
stochastic and sampling-based algorithms with fair-
ness and Pareto optimality.
ACKNOWLEDGEMENTS
This work was supported in part by JSPS KAKENHI
Grant Number JP19K12117.
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