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 have 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 of a problem in advance using the ASP formulation of the problem with an existing ASP solver, like CLASP, and then identify similar/diverse solutions using some clustering methods (possibly in ASP as well). The online methods compute similar/diverse solutions of a problem following one of the three approaches: by reformulating the ASP representation of the problem to compute similar/diverse solutions at once using an existing ASP solver; by computing similar/diverse solutions iteratively (one after other) using an existing ASP solver; by modifying the search algorithm of an ASP solver to compute similar/diverse solutions incrementally. All these methods are sound; the offline method and the first online method are complete whereas the others are not. We have 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 however it is implemented in C++. We have showed the applicability and the effectiveness of these methods using CLASP or CLASP-NK on two sorts of problems with different distance measures: on a real-world problem in phylogenetics (i.e., reconstruction of similar/diverse phylogenies for Indo-European languages), and on several planning problems in a well-known domain (i.e., Blocks World). We have observed that in terms of computational efficiency (both time and space) the last online method outperforms the others; also it allows us to compute similar/diverse * Part of the results in this paper are contained, in preliminary form, in Proceedings of the 25'th International Conference on Logic Programming (ICLP 2009 solutions when the distance function cannot be represented in ASP (e.g., due to some mathematical functions not supported by the ASP solvers) but can be easily implemented in C++.

We present new methods to efficiently answer complex queries overbiomedical ontologies and databases considering the relevant partsof these knowledge resources, and to generate shortest explanationsto justify these answers. Both algorithms rely on the high-levelrepresentation and efficient solvers of Answer Set Programming. Weapply these algorithms to find answers and explanations to some complexqueries related to drug discovery, over PharmGKB, DrugBank, BioGrid, CTD and Sider.

Nuclear Magnetic Resonance (NMR 1 ) spectroscopy is an important experimental technique that allows one to study protein structure in solution. An important challenge in NMR protein structure determination is the assignment of NMR peaks to corresponding nuclei. In structure-based assignment (SBA), the aim is to perform the assignments with the help of a homologous protein. NVR-BIP [1] is a tool that uses Nuclear Vector Replacement's (NVR) ([9], [10]) scoring function and binary integer programming to solve SBA problem. In this work, we introduce a method to improve NVR-BIP's assignment accuracy with amino acid typing. We use CRAACK that takes the chemical shifts of C, N and H atoms and returns the possible amino acids along with their confidence scores. We obtain improved assignment accuracies and our results show the effectiveness of integrating amino acid typing with NVR.

Abstract. For some problems with many solutions, like planning and phylogeny reconstruction, one way to compute more desirable solutions is to assign weights to solutions, and then pick the ones whose weights are over (resp. below) a threshold. This paper studies computing weighted solutions to such problems in Answer Set Programming. We investigate two sorts of methods for computing weighted solutions: one suggests modifying the representation of the problem and the other suggests modifying the search procedure of the answer set solver. We show the applicability and the effectiveness of these methods in phylogeny reconstruction.

Abstract. We study finding similar or diverse solutions of a given computational problem, in answer set programming, and introduce offline methods and online methods to compute them using answer set solvers. We analyze the computational complexity of some problems that are related to finding similar or diverse solutions, and show the applicability and effectiveness of our methods in phylogeny reconstruction.