2 research outputs found
On Deciding Local Theory Extensions via E-matching
Satisfiability Modulo Theories (SMT) solvers incorporate decision procedures
for theories of data types that commonly occur in software. This makes them
important tools for automating verification problems. A limitation frequently
encountered is that verification problems are often not fully expressible in
the theories supported natively by the solvers. Many solvers allow the
specification of application-specific theories as quantified axioms, but their
handling is incomplete outside of narrow special cases.
In this work, we show how SMT solvers can be used to obtain complete decision
procedures for local theory extensions, an important class of theories that are
decidable using finite instantiation of axioms. We present an algorithm that
uses E-matching to generate instances incrementally during the search,
significantly reducing the number of generated instances compared to eager
instantiation strategies. We have used two SMT solvers to implement this
algorithm and conducted an extensive experimental evaluation on benchmarks
derived from verification conditions for heap-manipulating programs. We believe
that our results are of interest to both the users of SMT solvers as well as
their developers
Logic Programming and Machine Ethics
Transparency is a key requirement for ethical machines. Verified ethical
behavior is not enough to establish justified trust in autonomous intelligent
agents: it needs to be supported by the ability to explain decisions. Logic
Programming (LP) has a great potential for developing such perspective ethical
systems, as in fact logic rules are easily comprehensible by humans.
Furthermore, LP is able to model causality, which is crucial for ethical
decision making.Comment: In Proceedings ICLP 2020, arXiv:2009.09158. Invited paper for the
ICLP2020 Panel on "Machine Ethics". arXiv admin note: text overlap with
arXiv:1909.0825