112 research outputs found
Complexity of Nested Circumscription and Nested Abnormality Theories
The need for a circumscriptive formalism that allows for simple yet elegant
modular problem representation has led Lifschitz (AIJ, 1995) to introduce
nested abnormality theories (NATs) as a tool for modular knowledge
representation, tailored for applying circumscription to minimize exceptional
circumstances. Abstracting from this particular objective, we propose L_{CIRC},
which is an extension of generic propositional circumscription by allowing
propositional combinations and nesting of circumscriptive theories. As shown,
NATs are naturally embedded into this language, and are in fact of equal
expressive capability. We then analyze the complexity of L_{CIRC} and NATs, and
in particular the effect of nesting. The latter is found to be a source of
complexity, which climbs the Polynomial Hierarchy as the nesting depth
increases and reaches PSPACE-completeness in the general case. We also identify
meaningful syntactic fragments of NATs which have lower complexity. In
particular, we show that the generalization of Horn circumscription in the NAT
framework remains CONP-complete, and that Horn NATs without fixed letters can
be efficiently transformed into an equivalent Horn CNF, which implies
polynomial solvability of principal reasoning tasks. Finally, we also study
extensions of NATs and briefly address the complexity in the first-order case.
Our results give insight into the ``cost'' of using L_{CIRC} (resp. NATs) as a
host language for expressing other formalisms such as action theories,
narratives, or spatial theories.Comment: A preliminary abstract of this paper appeared in Proc. Seventeenth
International Joint Conference on Artificial Intelligence (IJCAI-01), pages
169--174. Morgan Kaufmann, 200
Horn formula minimization
Horn formulas make up an important subclass of Boolean formulas that exhibits interesting and useful computational properties. They have been widely studied due to the fact that the satisfiability problem for Horn formulas is solvable in linear time. Also resulting from this, Horn formulas play an important role in the field of artificial intelligence. The minimization problem of Horn formulas is to reduce the size of a given Horn formula to find a shortest equivalent representation. Many knowledge bases in propositional expert systems are represented as Horn formulas. Therefore the minimization of Horn formulas can be used to reduce the size of these knowledge bases, thereby increasing the efficiency of queries. The goal of this project is to study the properties of Horn formulas and the minimization of Horn formulas. Topics discussed include The satisfiability problem for Horn formulas. NP-completeness of Horn formula minimization. Subclasses of Horn formulas for which the minimization problem is solvable in polynomial time. Approximation algorithms for Horn formula minimization
Approximating minimum representations of key Horn functions
Horn functions form a subclass of Boolean functions and appear in many different areas of computer science and mathematics as a general tool to describe implications and dependencies. Finding minimum sized representations for such functions with respect to most commonly used measures is a computationally hard problem that remains hard even for the important subclass of key Horn functions. In this paper we provide logarithmic factor approximation algorithms for key Horn functions with respect to all measures studied in the literature for which the problem is known to be hard
Unique key Horn functions
Given a relational database, a key is a set of attributes such that a value
assignment to this set uniquely determines the values of all other attributes.
The database uniquely defines a pure Horn function , representing the
functional dependencies. If the knowledge of the attribute values in set
determines the value for attribute , then is an implicate
of . If is a key of the database, then is an implicate
of for all attributes .
Keys of small sizes play a crucial role in various problems. We present
structural and complexity results on the set of minimal keys of pure Horn
functions. We characterize Sperner hypergraphs for which there is a unique pure
Horn function with the given hypergraph as the set of minimal keys.
Furthermore, we show that recognizing such hypergraphs is co-NP-complete
already when every hyperedge has size two. On the positive side, we identify
several classes of graphs for which the recognition problem can be decided in
polynomial time.
We also present an algorithm that generates the minimal keys of a pure Horn
function with polynomial delay. By establishing a connection between keys and
target sets, our approach can be used to generate all minimal target sets with
polynomial delay when the thresholds are bounded by a constant. As a byproduct,
our proof shows that the Minimum Key problem is at least as hard as the Minimum
Target Set Selection problem with bounded thresholds.Comment: 12 pages, 5 figure
Generalising unit-refutation completeness and SLUR via nested input resolution
We introduce two hierarchies of clause-sets, SLUR_k and UC_k, based on the
classes SLUR (Single Lookahead Unit Refutation), introduced in 1995, and UC
(Unit refutation Complete), introduced in 1994.
The class SLUR, introduced in [Annexstein et al, 1995], is the class of
clause-sets for which unit-clause-propagation (denoted by r_1) detects
unsatisfiability, or where otherwise iterative assignment, avoiding obviously
false assignments by look-ahead, always yields a satisfying assignment. It is
natural to consider how to form a hierarchy based on SLUR. Such investigations
were started in [Cepek et al, 2012] and [Balyo et al, 2012]. We present what we
consider the "limit hierarchy" SLUR_k, based on generalising r_1 by r_k, that
is, using generalised unit-clause-propagation introduced in [Kullmann, 1999,
2004].
The class UC, studied in [Del Val, 1994], is the class of Unit refutation
Complete clause-sets, that is, those clause-sets for which unsatisfiability is
decidable by r_1 under any falsifying assignment. For unsatisfiable clause-sets
F, the minimum k such that r_k determines unsatisfiability of F is exactly the
"hardness" of F, as introduced in [Ku 99, 04]. For satisfiable F we use now an
extension mentioned in [Ansotegui et al, 2008]: The hardness is the minimum k
such that after application of any falsifying partial assignments, r_k
determines unsatisfiability. The class UC_k is given by the clause-sets which
have hardness <= k. We observe that UC_1 is exactly UC.
UC_k has a proof-theoretic character, due to the relations between hardness
and tree-resolution, while SLUR_k has an algorithmic character. The
correspondence between r_k and k-times nested input resolution (or tree
resolution using clause-space k+1) means that r_k has a dual nature: both
algorithmic and proof theoretic. This corresponds to a basic result of this
paper, namely SLUR_k = UC_k.Comment: 41 pages; second version improved formulations and added examples,
and more details regarding future directions, third version further examples,
improved and extended explanations, and more on SLUR, fourth version various
additional remarks and editorial improvements, fifth version more
explanations and references, typos corrected, improved wordin
- …