Halit Erdogan

Finding Similar or Diverse Solutions in Answer Set Programming

For many computational problems, the main concern is to find a best solution (e.g., a most preferred product configuration, a shortest plan, a most parsimonious phylogeny) with respect to some well-described criteria. On the other hand, in many real-world applications, computing a subset of good solutions that are similar/diverse may be desirable for better decision-making. For one reason, the given computational problem may have too many good solutions, and the user may want to examine only a few of them to pick one; in such cases, finding a few similar/diverse good solutions may be useful. Also, in many real-world applications the users usually take into account further criteria that are not included in the formulation of the optimization problem; in such cases, finding a few good solutions that are close to or distant from a particular set of solutions may be useful. With this motivation, we have studied various computational problems related to finding similar/diverse (resp. close/distant) solutions with respect to a given distance function, in the context of Answer Set Programming (ASP). We have introduced novel offline/online computational methods in ASP to solve such computational problems. We have modified an ASP solver according to one of our online methods, providing a useful tool (CLASP-NK) for various ASP applications. We have showed the applicability and effectiveness of our methods/tools in three domains: phylogeny reconstruction, AI planning, and biomedical query answering. Motivated by the promising results, we have developed computational tools to be used by the experts in these areas.

Finding similar/diverse solutions in answer set programming

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.

Computing Weighted Solutions in Answer Set Programming

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.

Computing weighted solutions in ASP: representation-based method vs. search-based method

For some problems with too many solutions, one way to obtain the more desirable solutions is to assign each solution a weight that characterizes its importance quantitatively, and then compute the solutions whose weights are over (resp. below) a given threshold. This paper studies computing weighted solutions for a given computational problem, in the context of Answer Set Programming (ASP). In particular, we investigate two sorts of methods for computing weighted solutions: one method suggests modifying the ASP representation of the problem to compute weighted solutions using an existing ASP solver and the other suggests modifying the search algorithm of the answer set solver to compute weighted solutions incrementally. The applicability of these methods are shown on two sorts of problems: reconstructing weighted phylogenies (for Indo-European languages and for Quercus species) and finding weighted plans (for Blocks World planning problems). In the experiments with the representation-based method, the answer set solver clasp is used and weight functions are represented in ASP. For the search-based method, the algorithm of clasp is modified (the modified solver is called clasp-w) and weight functions are implemented in C+ +. For phylogenies, two weight functions are introduced by incorporating domain-specific information about groupings of species; one of them cannot be represented in ASP due to some mathematical functions not supported by the ASP solvers. For plans, we define a weight function that characterizes the total cost of executing actions in a plan. In these experiments the following are observed. With weight measures that can be represented in ASP, the search-based method outperforms the representation-based method in terms of computational efficiency (both time and space). With weight functions that cannot be represented in ASP, the search-based method provides a tool for computing weighted solutions in ASP. The search-based method can be applied to different domains, without modifying the algorithm of clasp-w; in that sense, the search-based method is modular and can be useful to other ASP applications. With either method, plausible phylogenies among many can be found without computing all phylogenies and requiring historical linguists to go over them manually, and less costly plans can be found without computing all plans; in that sense, our methods contribute to phylogenetics and AI planning studies as well.

Using amino acid typing to improve the accuracy of NMR structure based assignments

Nuclear Magnetic Resonance (NMR1) 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 is a tool that uses Nuclear Vector Replacements (NVR) scoring function and binary integer programming to solve SBA problem. In this work, we introduce a method to improve NVR-BIPs 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.