PRELIMINARY ABSTRACT:
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Declarative Merging of and Reasoning about Decision Diagrams
Thomas Eiter, Thomas Krennwallner and Christoph Redl
Keywords: 
Answer Set Programming, Decision Diagram Merging, Decision Diagram Reasoning, DNA classification
Abstract: 
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 decision diagrams using
answer set programming (ASP), which allows to drive the merging
process and select results on the application needs.