29,179 research outputs found
On the Expressive Power of Multiple Heads in CHR
Constraint Handling Rules (CHR) is a committed-choice declarative language
which has been originally designed for writing constraint solvers and which is
nowadays a general purpose language. CHR programs consist of multi-headed
guarded rules which allow to rewrite constraints into simpler ones until a
solved form is reached. Many empirical evidences suggest that multiple heads
augment the expressive power of the language, however no formal result in this
direction has been proved, so far.
In the first part of this paper we analyze the Turing completeness of CHR
with respect to the underneath constraint theory. We prove that if the
constraint theory is powerful enough then restricting to single head rules does
not affect the Turing completeness of the language. On the other hand,
differently from the case of the multi-headed language, the single head CHR
language is not Turing powerful when the underlying signature (for the
constraint theory) does not contain function symbols.
In the second part we prove that, no matter which constraint theory is
considered, under some reasonable assumptions it is not possible to encode the
CHR language (with multi-headed rules) into a single headed language while
preserving the semantics of the programs. We also show that, under some
stronger assumptions, considering an increasing number of atoms in the head of
a rule augments the expressive power of the language.
These results provide a formal proof for the claim that multiple heads
augment the expressive power of the CHR language.Comment: v.6 Minor changes, new formulation of definitions, changed some
details in the proof
Logic Programming Applications: What Are the Abstractions and Implementations?
This article presents an overview of applications of logic programming,
classifying them based on the abstractions and implementations of logic
languages that support the applications. The three key abstractions are join,
recursion, and constraint. Their essential implementations are for-loops, fixed
points, and backtracking, respectively. The corresponding kinds of applications
are database queries, inductive analysis, and combinatorial search,
respectively. We also discuss language extensions and programming paradigms,
summarize example application problems by application areas, and touch on
example systems that support variants of the abstractions with different
implementations
Combining Relational Algebra, SQL, Constraint Modelling, and Local Search
The goal of this paper is to provide a strong integration between constraint
modelling and relational DBMSs. To this end we propose extensions of standard
query languages such as relational algebra and SQL, by adding constraint
modelling capabilities to them. In particular, we propose non-deterministic
extensions of both languages, which are specially suited for combinatorial
problems. Non-determinism is introduced by means of a guessing operator, which
declares a set of relations to have an arbitrary extension. This new operator
results in languages with higher expressive power, able to express all problems
in the complexity class NP. Some syntactical restrictions which make data
complexity polynomial are shown. The effectiveness of both extensions is
demonstrated by means of several examples. The current implementation, written
in Java using local search techniques, is described. To appear in Theory and
Practice of Logic Programming (TPLP)Comment: 30 pages, 5 figure
The DLV System for Knowledge Representation and Reasoning
This paper presents the DLV system, which is widely considered the
state-of-the-art implementation of disjunctive logic programming, and addresses
several aspects. As for problem solving, we provide a formal definition of its
kernel language, function-free disjunctive logic programs (also known as
disjunctive datalog), extended by weak constraints, which are a powerful tool
to express optimization problems. We then illustrate the usage of DLV as a tool
for knowledge representation and reasoning, describing a new declarative
programming methodology which allows one to encode complex problems (up to
-complete problems) in a declarative fashion. On the foundational
side, we provide a detailed analysis of the computational complexity of the
language of DLV, and by deriving new complexity results we chart a complete
picture of the complexity of this language and important fragments thereof.
Furthermore, we illustrate the general architecture of the DLV system which
has been influenced by these results. As for applications, we overview
application front-ends which have been developed on top of DLV to solve
specific knowledge representation tasks, and we briefly describe the main
international projects investigating the potential of the system for industrial
exploitation. Finally, we report about thorough experimentation and
benchmarking, which has been carried out to assess the efficiency of the
system. The experimental results confirm the solidity of DLV and highlight its
potential for emerging application areas like knowledge management and
information integration.Comment: 56 pages, 9 figures, 6 table
A Logic Programming Approach to Knowledge-State Planning: Semantics and Complexity
We propose a new declarative planning language, called K, which is based on
principles and methods of logic programming. In this language, transitions
between states of knowledge can be described, rather than transitions between
completely described states of the world, which makes the language well-suited
for planning under incomplete knowledge. Furthermore, it enables the use of
default principles in the planning process by supporting negation as failure.
Nonetheless, K also supports the representation of transitions between states
of the world (i.e., states of complete knowledge) as a special case, which
shows that the language is very flexible. As we demonstrate on particular
examples, the use of knowledge states may allow for a natural and compact
problem representation. We then provide a thorough analysis of the
computational complexity of K, and consider different planning problems,
including standard planning and secure planning (also known as conformant
planning) problems. We show that these problems have different complexities
under various restrictions, ranging from NP to NEXPTIME in the propositional
case. Our results form the theoretical basis for the DLV^K system, which
implements the language K on top of the DLV logic programming system.Comment: 48 pages, appeared as a Technical Report at KBS of the Vienna
University of Technology, see http://www.kr.tuwien.ac.at/research/reports
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