State-of-the-art model counters are based on exhaustive DPLL algorithms, and have been successfully used in probabilistic reasoning, one of the key problems in AI. In this article, we present a new exhaustive DPLL algorithm with a formal semantics, a proof of correctness, and a modular design. The modular design is based on the separation of the core model counting algorithm from SAT solving techniques. We also show that the trace of our algorithm belongs to the language of Sentential Decision Diagrams (SDDs), which is a subset of Decision-DNNFs, the trace of existing state-of-the-art model counters. Still, our experimental analysis shows comparable results against state-of-the-art model counters. Furthermore, we obtain the first top-down SDD compiler, and show orders-of-magnitude improvements in SDD construction time against the existing bottom-up SDD compiler.

We introduce novel mathematical models and algorithms to generate (shortest or k different) explanations for biomedical queries, using answer set programming. We implement these algorithms and integrate them in BIOQUERY-ASP. We illustrate the usefulness of these methods with some complex biomedical queries related to drug discovery, over the biomedical knowledge resources PHARMGKB, DRUGBANK, BIOGRID, CTD, SIDER, DISEASE ONTOLOGY and ORPHADATA. To appear in Theory and Practice of Logic Programming (TPLP).

Pathfinding for a single agent is the problem of planning a route from an initial location to a goal location in an environment, going around obstacles. Pathfinding for multiple agents also aims to plan such routes for each agent, subject to different constraints, such as restrictions on the length of each path or on the total length of paths, no self-intersecting paths, no intersection of paths/plans, no crossing/meeting each other. It also has variations for finding optimal solutions, e.g., with respect to the maximum path length, or the sum of plan lengths. These problems are important for many real-life applications, such as motion planning, vehicle routing, environmental monitoring, patrolling, computer games. Motivated by such applications, we introduce a formal framework that is general enough to address all these problems: we use the expressive high-level representation formalism and efficient solvers of the declarative programming paradigm Answer Set Programming. We also introduce heuristics to improve the computational efficiency and/or solution quality. We show the applicability and usefulness of our framework by experiments, with randomly generated problem instances on a grid, on a real-world road network, and on a real computer game terrain.

Some of these complex queries, such as Q1 or Q2, Recent advances in health and life sciences have led to generation can be represented in a formal query language (e.g., of a large amount of biomedical data. To facilitate access SQL/SPARQL) and then answered using Semantic Web to its desired parts, such a big mass of data has been represented technologies. However, queries, like Q4, that require auxiliary in structured forms, like biomedical ontologies and recursive definitions (such as transitive closure) cannot databases. On the other hand, representing these biomedical be directly represented in these languages; and thus such ontologies and databases in different forms, constructing queries cannot be answered directly using Semantic Web them independently from each other, and storing them at technologies. The experts usually compute auxiliary relations different locations have brought about many challenges for externally, for instance, by enumerating all drug-drug answering queries about the knowledge represented in these interaction chains or gene cliques, and then use these auxiliary ontologies and databases.