Real world resource allocation problems, where a set of tasks with different requirements have to be
assigned a set of resources, usually include uncertainties that can produce changes in the data of the
problem. These changes may cause difficulties in the applicability of the solution. Research in approaches that consider data uncertainty while looking for
a solution is gaining importance recently, due to the wide use of such problems in many domains.
Solutions that are able to overcome (up to a given degree) the possible changes that can occur in the
environment are called robust solutions. Our notion of robustness is based on resources that become unavailable in an auction once an allocation
has been found. In our approach, we first extend previous works on encoding auctions as weighted
Max-SAT formulas. Particularly, our approach is sustained in propositional logic and that in turn
allows the model to be able to be applied in any Boolean formula, being auctions a particular case.
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