Finding similar or diverse solutions in answer set programming: theory and applications

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

Reconstructing weighted phylogenetic trees and phylogenetic networks using answer set programming

Evolutionary relationships between species can be modeled as a tree (called a phylogeny) whose nodes represent the species, internal vertices represent their ancestors and edges represent genetic relationships. If there are borrowings between species, then a small number of edges that denote such borrowings can be added to phylogenies turning them into (phylogenetic) networks. However, there are too many such trees/networks for a given family of species but no phylogenetic system to automatically analyze them. This thesis fulfills this need in phylogenetics, by introducing novel computational methods and tools for computing weighted phylogenies/networks, using Answer Set Programming (ASP). The main idea is to define a weight function for phylogenies/networks that characterizes their plausibility, and to reconstruct phylogenies/networks whose weights are over a given threshold using ASP solvers. We have studied computational problems related to reconstructing weighted phylogenies/networks based on the compatibility criterion, analyzed their computational complexity, and introduced two sorts of ASP-based methods (representation-based and search-based) for computing weighted phylogenies/networks. Utilizing these methods, we have introduced a novel divide-and-conquer algorithm for computing large weighted phylogenies, and implemented a phylogenetic system (Phylo-ASP) based on it. We have also implemented a phylogenetic system (PhyloNet-ASP) for reconstructing weighted networks. We have shown the applicability and the effectiveness of our methods by performing experiments on two real datasets: Indo European languages, and Quercus species in Turkey. Moreover, we have extended our methods to computing weighted solutions in ASP and modified an ASP solver accordingly, providing a useful tool (CLASP-W) for various ASP applications

Generating explanations for complex biomedical queries

Recent advances in health and life sciences have led to generation of a large amount of biomedical data. To facilitate access to its desired parts, such a big mass of data has been represented in structured forms, like databases and ontologies. On the other hand, representing these databases and ontologies in different formats, constructing them independently from each other, and storing them at different locations have brought about many challenges for answering queries about the knowledge represented in these ontologies and databases. One of the challenges for the users is to be able to represent such a biomedical query in a natural language, and get its answers in an understandable form. Another challenge is to extract relevant knowledge from different knowledge resources, and integrate them appropriately using also definitions, such as, chains of gene-gene interactions, cliques of genes based on gene-gene relations, or similarity/diversity of genes/drugs. Furthermore, once an answer is found for a complex query, the experts may need further explanations about the answer. The first two challenges have been addressed earlier using Answer Set Programming (ASP), with the development of a software system (called BIOQUERY-ASP). This thesis addresses the third challenge: explanation generation in ASP. In this thesis, we extend the earlier work on the first two challenges, to new forms of biomedical queries (e.g., about drug similarity) and to new biomedical knowledge resources. We introduce novel mathematical models and algorithms to generate (shortest or k different) explanations for queries in ASP, and provide a comprehensive theoretical analysis of these methods. We implement these algorithms and integrate them in BIOQUERY-ASP, and provide an experimental evaluation of our methods with some complex biomedical queries over the biomedical knowledge resources PHARMGKB, DRUGBANK, BIOGRID, CTD, SIDER, DISEASEONTOLOGY and ORPHADATA

Logic Programming Agents and Game Theory

In this paper we present a framework for logic programming agents to take part in games in such a way that stable models of the system, the ones agreed upon by all the members, correspond with the different equilibria of the game. The proposed transformations from games to ordered choice logic program produces a multi-agent system where each agent embodies the reasoning of a player and where the system itself represents the structure of the game. This allows us to monitor the knowledge and beliefs of the agents, i.e. the flow of information between agents/players

Logic Programming Agents and Game Theory

In this paper we present a framework for logic programming agents to take part in games in such a way that stable models of the system, the ones agreed upon by all the members, correspond with the different equilibria of the game. The proposed transformations from games to ordered choice logic program produce a multi-agent system where each agent embodies the reasoning of a player and where the system itself represents the structure of the game. This allows us to monitor the knowledge and beliefs of the agents, i.e. the flow of information between agents/players

Logic Programming Agents and Game Theory

In this paper we present a framework for logic programming agents to take part in games in such a way that stable models of the system, the ones agreed upon by all the members, correspond with the different equilibria of the game. The proposed transformations from games to ordered choice logic program produces a multi-agent system where each agent embodies the reasoning of a player and where the system itself represents the structure of the game. This allows us to monitor the knowledge and beliefs of the agents, i.e. the flow of information between agents/players