100,949 research outputs found
Deriving a Fast Inverse of the Generalized Cantor N-tupling Bijection
We attack an interesting open problem (an efficient algorithm to invert the generalized Cantor N-tupling bijection) and solve it through a sequence of equivalence preserving transformations of logic programs, that take advantage of unique strengths of this programming paradigm. An extension to set and multiset tuple encodings, as well as a simple application to a "fair-search" mechanism illustrate practical uses of our algorithms.
The code in the paper (a literate Prolog program, tested with SWI-Prolog and Lean Prolog) is available at http://logic.cse.unt.edu/tarau/research/2012/pcantor.pl
Unary Pushdown Automata and Straight-Line Programs
We consider decision problems for deterministic pushdown automata over a
unary alphabet (udpda, for short). Udpda are a simple computation model that
accept exactly the unary regular languages, but can be exponentially more
succinct than finite-state automata. We complete the complexity landscape for
udpda by showing that emptiness (and thus universality) is P-hard, equivalence
and compressed membership problems are P-complete, and inclusion is
coNP-complete. Our upper bounds are based on a translation theorem between
udpda and straight-line programs over the binary alphabet (SLPs). We show that
the characteristic sequence of any udpda can be represented as a pair of
SLPs---one for the prefix, one for the lasso---that have size linear in the
size of the udpda and can be computed in polynomial time. Hence, decision
problems on udpda are reduced to decision problems on SLPs. Conversely, any SLP
can be converted in logarithmic space into a udpda, and this forms the basis
for our lower bound proofs. We show coNP-hardness of the ordered matching
problem for SLPs, from which we derive coNP-hardness for inclusion. In
addition, we complete the complexity landscape for unary nondeterministic
pushdown automata by showing that the universality problem is -hard, using a new class of integer expressions. Our techniques have
applications beyond udpda. We show that our results imply -completeness for a natural fragment of Presburger arithmetic and coNP lower
bounds for compressed matching problems with one-character wildcards
Strong Equivalence of Qualitative Optimization Problems
We introduce the framework of qualitative optimization problems (or, simply, optimization problems) to represent preference theories. The formalism uses separate modules to describe the space of outcomes to be compared (the generator) and the preferences on outcomes (the selector). We consider two types of optimization problems. They differ in the way the generator, which we model by a propositional theory, is interpreted: by the standard propositional logic semantics, and by the equilibrium-model (answer-set) semantics. Under the latter interpretation of generators, optimization problems directly generalize answer-set optimization programs proposed previously. We study strong equivalence of optimization problems, which guarantees their interchangeability within any larger context. We characterize several versions of strong equivalence obtained by restricting the class of optimization problems that can be used as extensions and establish the complexity of associated reasoning tasks. Understanding strong equivalence is essential for modular representation of optimization problems and rewriting techniques to simplify them without changing their inherent properties
An Algorithm for Distribution problem under Uncertainty
The distribution problem whose constsained resource vector is random variable can be solved by using an algorithm, developed by Dantzig and Madansky, called "tow-stage programs under uncertainty conditions". It's certainty equivalence problem, however, would be formulated into nonlinear programming problem and it is difficult to treat for it's nonlinearity. In this paper, we propose an algorithm to solve the distribution problem under uncertainty by considering the characteristic of the objective function of the problem. This algorithm consists of iterations of solving a simple simplex method and is more advantage than general procedure solving non-linear programming in view of both the computing time and the computer storage required
Pac-Learning Recursive Logic Programs: Efficient Algorithms
We present algorithms that learn certain classes of function-free recursive
logic programs in polynomial time from equivalence queries. In particular, we
show that a single k-ary recursive constant-depth determinate clause is
learnable. Two-clause programs consisting of one learnable recursive clause and
one constant-depth determinate non-recursive clause are also learnable, if an
additional ``basecase'' oracle is assumed. These results immediately imply the
pac-learnability of these classes. Although these classes of learnable
recursive programs are very constrained, it is shown in a companion paper that
they are maximally general, in that generalizing either class in any natural
way leads to a computationally difficult learning problem. Thus, taken together
with its companion paper, this paper establishes a boundary of efficient
learnability for recursive logic programs.Comment: See http://www.jair.org/ for any accompanying file
A Common View on Strong, Uniform, and Other Notions of Equivalence in Answer-Set Programming
Logic programming under the answer-set semantics nowadays deals with numerous
different notions of program equivalence. This is due to the fact that
equivalence for substitution (known as strong equivalence) and ordinary
equivalence are different concepts. The former holds, given programs P and Q,
iff P can be faithfully replaced by Q within any context R, while the latter
holds iff P and Q provide the same output, that is, they have the same answer
sets. Notions in between strong and ordinary equivalence have been introduced
as theoretical tools to compare incomplete programs and are defined by either
restricting the syntactic structure of the considered context programs R or by
bounding the set A of atoms allowed to occur in R (relativized equivalence).For
the latter approach, different A yield properly different equivalence notions,
in general. For the former approach, however, it turned out that any
``reasonable'' syntactic restriction to R coincides with either ordinary,
strong, or uniform equivalence. In this paper, we propose a parameterization
for equivalence notions which takes care of both such kinds of restrictions
simultaneously by bounding, on the one hand, the atoms which are allowed to
occur in the rule heads of the context and, on the other hand, the atoms which
are allowed to occur in the rule bodies of the context. We introduce a general
semantical characterization which includes known ones as SE-models (for strong
equivalence) or UE-models (for uniform equivalence) as special cases.
Moreover,we provide complexity bounds for the problem in question and sketch a
possible implementation method.
To appear in Theory and Practice of Logic Programming (TPLP)
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