Abstract
Many problems in artificial intelligence can be encoded
as answer set programs (ASP) in which some
rules are uncertain. ASP programs with incorrect
rules may have erroneous conclusions, but due to
the non-monotonic nature of ASP, omitting a correct
rule may also lead to errors. To derive the
most certain conclusions from an uncertain ASP
program, we thus need to consider all situations in
which some, none, or all of the least certain rules
are omitted. This corresponds to treating some
rules as optional and reasoning about which conclusions
remain valid regardless of the inclusion of
these optional rules. While a version of possibilistic
ASP (PASP) based on this view has recently been
introduced, no implementation is currently available.
In this paper we propose a simulation of
the main reasoning tasks in PASP using (disjunctive)
ASP programs, allowing us to take advantage
of state-of-the-art ASP solvers. Furthermore, we
identify how several interesting AI problems can
be naturally seen as special cases of the considered
reasoning tasks, including cautious abductive reasoning
and conformant planning. As such, the proposed
simulation enables us to solve instances of
the latter problem types that are more general than
what current solvers can handle.
Original language | English |
---|---|
Number of pages | 8 |
Publication status | Published - Aug 2013 |
Event | Workshop on Weighted Logics for AI (WL4AI'13) - Beijing, China Duration: 03 Aug 2013 → 05 Aug 2013 |
Conference
Conference | Workshop on Weighted Logics for AI (WL4AI'13) |
---|---|
Country/Territory | China |
City | Beijing |
Period | 03/08/2013 → 05/08/2013 |