Answer-Set Programming (ASP) is an established declarative programming paradigm. However, classical ASP lacks subprogram calls as in procedural programming, and access to external computations (like remote procedure calls) in general. The feature is desired for increasing modularity and---assuming proper access in place---(meta-)reasoning over subprogram results. While HEX-programs extend classical ASP with external source access, they do not support calls of (sub-)programs upfront. We present nested HEX-programs, which extend HEX-programs to serve the desired feature, in a user-friendly manner. Notably, the answer sets of called sub-programs can be individually accessed. This is particularly useful for applications that need to reason over answer sets like belief set merging, user-defined aggregate functions, or preferences over answer sets.

Keywords: Answer Set Programming, HEX-Programs, Modular Logic Programming

Decision diagrams (DDs) are a popular means for decision making, e.g., in clinical guidelines. Some applications require to integrate multiple related yet different diagrams into a single one, for which algorithms have been developed. However, existing merging tools are monolithic, application-tailored programs with no clear interface to the actual merging procedures, which makes their reuse hard if not impossible. We present a general, declarative framework for merging and manipulating decision diagram tasks based on a belief set merging framework. Its modular architecture hides details of the merging algorithm and supports pre- and user-defined merging operators, which can be flexibly arranged in merging plans to express complex merging tasks. Changing and restructuring merging tasks becomes easy, and relieves the user from (repetitive) manual integration to focus on experimenting with different merging strategies, which is vital for applications, as discussed for an example from DNA classification. Our framework supports also reasoning over DDs using answer set programming (ASP), which allows to drive the merging process and select results based on the application needs.

Keywords: Answer Set Programming, Nonmonotonic Reasoning, Decision Diagram Merging

We present a declarative framework for belief set merging tasks over (possibly heterogeneous) knowledge bases, where belief sets are sets of literals. The framework is designed generically for flexible deployment to a range of applications, and allows to specify complex merging tasks in tree-structured merging plans, whose leaves are the possible belief sets of the knowledge bases that are processed using merging operators. A prototype is implemented in MELD (MErging Library for Dlvhex) on top of the DLVHEX system for HEX-programs, which are nonmonotonic logic programs with access to external sources. Plans in the task description language allow to formulate different conflict resolution strategies, and by shared object libraries, the user may also develop and integrate her own merging operators. MELD supports rapid prototyping of merging tasks, providing a computational backbone such that users can focus on operator optimization and evaluation, and on experimenting with merging strategies; this is particularly useful if a best merging operator or strategy is not known. Example applications are combining multiple decision diagrams (e.g., in biomedicine), judgment aggregation in social choice theory, and ontology merging.

Keywords: Answer Set Programming, Belief Merging, Hybrid Knowledge Base, HEX-Programs