5,386 research outputs found
Recursive Online Enumeration of All Minimal Unsatisfiable Subsets
In various areas of computer science, we deal with a set of constraints to be
satisfied. If the constraints cannot be satisfied simultaneously, it is
desirable to identify the core problems among them. Such cores are called
minimal unsatisfiable subsets (MUSes). The more MUSes are identified, the more
information about the conflicts among the constraints is obtained. However, a
full enumeration of all MUSes is in general intractable due to the large number
(even exponential) of possible conflicts. Moreover, to identify MUSes
algorithms must test sets of constraints for their simultaneous satisfiabilty.
The type of the test depends on the application domains. The complexity of
tests can be extremely high especially for domains like temporal logics, model
checking, or SMT. In this paper, we propose a recursive algorithm that
identifies MUSes in an online manner (i.e., one by one) and can be terminated
at any time. The key feature of our algorithm is that it minimizes the number
of satisfiability tests and thus speeds up the computation. The algorithm is
applicable to an arbitrary constraint domain and its effectiveness demonstrates
itself especially in domains with expensive satisfiability checks. We benchmark
our algorithm against state of the art algorithm on Boolean and SMT constraint
domains and demonstrate that our algorithm really requires less satisfiability
tests and consequently finds more MUSes in given time limits
The New Hampshire, Vol. 106, No. 09 (Oct. 3, 2016)
An independent student produced newspaper from the University of New Hampshire
Tunable Online MUS/MSS Enumeration
In various areas of computer science, the problem of dealing with a set of constraints arises. If the set of constraints is unsatisfiable, one may ask for a minimal description of the reason for this unsatisifiability. Minimal unsatisfiable subsets (MUSes) and maximal satisfiable subsets (MSSes) are two kinds of such minimal descriptions. The goal of this work is the enumeration of MUSes and MSSes for a given constraint system. As such full enumeration may be intractable in general, we focus on building an online algorithm, which produces MUSes/MSSes in an on-the-fly manner as soon as they are discovered. The problem has been studied before even in its online version. However, our algorithm uses a novel approach that is able to outperform the current state-of-the art algorithms for online MUS/MSS enumeration. Moreover, the performance of our algorithm can be adjusted using tunable parameters. We evaluate the algorithm on a set of benchmarks
On Finding Minimally Unsatisfiable Cores of CSPs
International audienceWhen a Constraint Satisfaction Problem (CSP) admits no solution, it can be useful to pinpoint which constraints are actually contradicting one another and make the problem infeasible. In this paper, a recent heuristic-based approach to compute infeasible min- imal subparts of discrete CSPs, also called Minimally Unsatisfiable Cores (MUCs), is improved. The approach is based on the heuristic exploitation of the number of times each constraint has been falsified during previous failed search steps. It appears to en- hance the performance of the initial technique, which was the most efficient one until now
On Exploiting Hitting Sets for Model Reconciliation
In human-aware planning, a planning agent may need to provide an explanation
to a human user on why its plan is optimal. A popular approach to do this is
called model reconciliation, where the agent tries to reconcile the differences
in its model and the human's model such that the plan is also optimal in the
human's model. In this paper, we present a logic-based framework for model
reconciliation that extends beyond the realm of planning. More specifically,
given a knowledge base entailing a formula and a second
knowledge base not entailing it, model reconciliation seeks an
explanation, in the form of a cardinality-minimal subset of , whose
integration into makes the entailment possible. Our approach, based on
ideas originating in the context of analysis of inconsistencies, exploits the
existing hitting set duality between minimal correction sets (MCSes) and
minimal unsatisfiable sets (MUSes) in order to identify an appropriate
explanation. However, differently from those works targeting inconsistent
formulas, which assume a single knowledge base, MCSes and MUSes are computed
over two distinct knowledge bases. We conclude our paper with an empirical
evaluation of the newly introduced approach on planning instances, where we
show how it outperforms an existing state-of-the-art solver, and generic
non-planning instances from recent SAT competitions, for which no other solver
exists
Axiom Pinpointing
Axiom pinpointing refers to the task of finding the specific axioms in an
ontology which are responsible for a consequence to follow. This task has been
studied, under different names, in many research areas, leading to a
reformulation and reinvention of techniques. In this work, we present a general
overview to axiom pinpointing, providing the basic notions, different
approaches for solving it, and some variations and applications which have been
considered in the literature. This should serve as a starting point for
researchers interested in related problems, with an ample bibliography for
delving deeper into the details
Commonwealth Times 1994-04-06
https://scholarscompass.vcu.edu/com/1794/thumbnail.jp
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