224 research outputs found
Space Efficiency of Propositional Knowledge Representation Formalisms
We investigate the space efficiency of a Propositional Knowledge
Representation (PKR) formalism. Intuitively, the space efficiency of a
formalism F in representing a certain piece of knowledge A, is the size of the
shortest formula of F that represents A. In this paper we assume that knowledge
is either a set of propositional interpretations (models) or a set of
propositional formulae (theorems). We provide a formal way of talking about the
relative ability of PKR formalisms to compactly represent a set of models or a
set of theorems. We introduce two new compactness measures, the corresponding
classes, and show that the relative space efficiency of a PKR formalism in
representing models/theorems is directly related to such classes. In
particular, we consider formalisms for nonmonotonic reasoning, such as
circumscription and default logic, as well as belief revision operators and the
stable model semantics for logic programs with negation. One interesting result
is that formalisms with the same time complexity do not necessarily belong to
the same space efficiency class
Positive Unit Hyperresolution Tableaux and Their Application to Minimal Model Generation
Minimal Herbrand models of sets of first-order clauses are useful in several areas of computer science, e.g. automated theorem proving, program verification, logic programming, databases, and artificial intelligence. In most cases, the conventional model generation algorithms are
inappropriate because they generate nonminimal Herbrand models and can
be inefficient. This article describes an approach for generating the minimal
Herbrand models of sets of first-order clauses. The approach builds upon
positive unit hyperresolution (PUHR) tableaux, that are in general smaller
than conventional tableaux. PUHR tableaux formalize the approach initially introduced with the theorem prover SATCHMO. Two minimal model generation procedures are described. The first one expands PUHR tableaux
depth-first relying on a complement splitting expansion rule and on a form
of backtracking involving constraints. A Prolog implementation, named
MM-SATCHMO, of this procedure is given and its performance on benchmark suites is reported. The second minimal model generation procedure
performs a breadth-first, constrained expansion of PUHR (complement)
tableaux. Both procedures are optimal in the sense that each minimal model
is constructed only once, and the construction of nonminimal models is interrupted as soon as possible. They are complete in the following sense
The depth-first minimal model generation procedure computes all minimal
Herbrand models of the considered clauses provided these models are all
finite. The breadth-first minimal model generation procedure computes all
finite minimal Herbrand models of the set of clauses under consideration.
The proposed procedures are compared with related work in terms of both
principles and performance on benchmark problems
The CIFF Proof Procedure for Abductive Logic Programming with Constraints: Theory, Implementation and Experiments
We present the CIFF proof procedure for abductive logic programming with
constraints, and we prove its correctness. CIFF is an extension of the IFF
proof procedure for abductive logic programming, relaxing the original
restrictions over variable quantification (allowedness conditions) and
incorporating a constraint solver to deal with numerical constraints as in
constraint logic programming. Finally, we describe the CIFF system, comparing
it with state of the art abductive systems and answer set solvers and showing
how to use it to program some applications. (To appear in Theory and Practice
of Logic Programming - TPLP)
Hybrid ASP-based Approach to Pattern Mining
Detecting small sets of relevant patterns from a given dataset is a central
challenge in data mining. The relevance of a pattern is based on user-provided
criteria; typically, all patterns that satisfy certain criteria are considered
relevant. Rule-based languages like Answer Set Programming (ASP) seem
well-suited for specifying such criteria in a form of constraints. Although
progress has been made, on the one hand, on solving individual mining problems
and, on the other hand, developing generic mining systems, the existing methods
either focus on scalability or on generality. In this paper we make steps
towards combining local (frequency, size, cost) and global (various condensed
representations like maximal, closed, skyline) constraints in a generic and
efficient way. We present a hybrid approach for itemset, sequence and graph
mining which exploits dedicated highly optimized mining systems to detect
frequent patterns and then filters the results using declarative ASP. To
further demonstrate the generic nature of our hybrid framework we apply it to a
problem of approximately tiling a database. Experiments on real-world datasets
show the effectiveness of the proposed method and computational gains for
itemset, sequence and graph mining, as well as approximate tiling.
Under consideration in Theory and Practice of Logic Programming (TPLP).Comment: 29 pages, 7 figures, 5 table
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