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Some applications require the interactive resolution of a constraint problem by a human user.
To address the issues, the randomization technique is adopted here to solve the rank constraint problem.
Through the rearrangement of the DINDs, their spatial constraint problem can be solved.
This constraint problem is, for example, a frequent case on Twitter microblogging platform.
As aforementioned, the R+iSpace is designed to solve the DIND's spatial constraint problem.
This indicates that the DIND's spatial constraint problem can be solved in the R+iSpace.
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These behaviors also appear to arise in other constraint problems.
The effectiveness of the algorithm for constraint problems is proved by solving three constraint test problems.
We consider soft constraint problems where some of the preferences may be unspecified.
Regarding the state constraint problems, lots of results are also obtained.
On the one hand, MSBA proved its capability for handling multimodal constraint problems.
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