For some computational problems (e.g., product configuration, planning, diagnosis, query answering, phylogeny reconstruction) computing a set of similar diverse solutions may be desirable for better decision-making. With this motivation, we studied several decision optimization versions of this problem in the context of Answer Set Programming (ASP), analyzed their computational complexity, and introduced offline online methods to compute similar diverse solutions of such computational problems with respect to a given distance function. All these methods rely on the idea of computing solutions to a problem by means of finding the answer sets for an ASP program that describes the problem. The offline methods compute all solutions in advance using the ASP formulation of the problem with an ASP solver, like Clasp, and then identify similar diverse solutions using clustering methods. The online methods compute similar diverse solutions following one of the three approaches: by reformulating the ASP representation of the problem to compute similar diverse solutions at once using an ASP solver; by computing similar diverse solutions iteratively (one after other) using an ASP solver; by modifying the search algorithm of an ASP solver to compute similar diverse solutions incrementally. We modified Clasp to implement the last online method and called it Clasp-NK. In the first two online methods, the given distance function is represented in ASP; in the last one it is implemented in C++. We showed the applicability and the effectiveness of these methods on reconstruction of similar diverse phylogenies for Indo-European languages, and on several planning problems in Blocks World. We observed that in terms of computational efficiency the last online method outperforms the others; also it allows us to compute similar diverse solutions when the distance function cannot be represented in ASP.
which are similar to @math -distant solution and @math -close solution . On the other hand, @cite_31 asks for a solution @math -close to a partial solution rather than a set of solutions. Also, the distance measure considered in these problems is computable in polynomial time. However, with @math containing a single solution and @math being computable in polynomial time, @math -distant solution (resp. @math -close solution ) becomes essentially the same as @math Distant (resp. @math Close ).
@cite_31
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Users can often naturally express their preferences in terms of ideal or non-ideal solutions. We show how to reason about logical combinations of distance constraints on ideals and non-ideals using a novel global constraint. We evaluate our approach on both randomly generated and real-world configuration problem instances.