10,579 research outputs found
REVISION PROGRAMMING: A KNOWLEDGE REPRESENTATION FORMALISM
The topic of the dissertation is revision programming. It is a knowledge representation formalismfor describing constraints on databases, knowledge bases, and belief sets, and providing acomputational mechanism to enforce them. Constraints are represented by sets of revision rules.Revision rules could be quite complex and are usually in a form of conditions (for instance, ifthese elements are present and those elements are absent, then this element must be absent). Inaddition to being a logical constraint, a revision rule specify a preferred way to satisfy the constraint.Justified revisions semantics assigns to any database a set (possibly empty) of revisions.Each revision satisfies the constraints, and all deletions and additions of elements in a transitionfrom initial database to the revision are derived from revision rules.Revision programming and logic programming are closely related. We established an elegantembedding of revision programs into logic programs, which does not increase the size of a program.Initial database is used in transformation of a revision program into the corresponding logicprogram, but it is not represented in the logic program.The connection naturally led to extensions of revision programming formalism which correspondto existing extensions of logic programming. More specific, a disjunctive and a nestedversions of revision programming were introduced.Also, we studied annotated revision programs, which allow annotations like confidence factors,multiple experts, etc. Annotations were assumed to be elements of a complete infinitely distributivelattice. We proposed a justified revisions semantics for annotated revision programs which agreedwith intuitions.Next, we introduced definitions of well-founded semantics for revision programming. It assignsto a revision problem a single intended model which is computable in polynomial time.Finally, we extended syntax of revision problems by allowing variables and implemented translatorsof revision programs into logic programs and a grounder for revision programs. The implementationallows us to compute justified revisions using existing implementations of the stablemodel semantics for logic programs
Coherent Integration of Databases by Abductive Logic Programming
We introduce an abductive method for a coherent integration of independent
data-sources. The idea is to compute a list of data-facts that should be
inserted to the amalgamated database or retracted from it in order to restore
its consistency. This method is implemented by an abductive solver, called
Asystem, that applies SLDNFA-resolution on a meta-theory that relates
different, possibly contradicting, input databases. We also give a pure
model-theoretic analysis of the possible ways to `recover' consistent data from
an inconsistent database in terms of those models of the database that exhibit
as minimal inconsistent information as reasonably possible. This allows us to
characterize the `recovered databases' in terms of the `preferred' (i.e., most
consistent) models of the theory. The outcome is an abductive-based application
that is sound and complete with respect to a corresponding model-based,
preferential semantics, and -- to the best of our knowledge -- is more
expressive (thus more general) than any other implementation of coherent
integration of databases
An encompassing framework for Paraconsistent Logic Programs
AbstractWe propose a framework which extends Antitonic Logic Programs [Damásio and Pereira, in: Proc. 6th Int. Conf. on Logic Programming and Nonmonotonic Reasoning, Springer, 2001, p. 748] to an arbitrary complete bilattice of truth-values, where belief and doubt are explicitly represented. Inspired by Ginsberg and Fitting's bilattice approaches, this framework allows a precise definition of important operators found in logic programming, such as explicit and default negation. In particular, it leads to a natural semantical integration of explicit and default negation through the Coherence Principle [Pereira and Alferes, in: European Conference on Artificial Intelligence, 1992, p. 102], according to which explicit negation entails default negation. We then define Coherent Answer Sets, and the Paraconsistent Well-founded Model semantics, generalizing many paraconsistent semantics for logic programs. In particular, Paraconsistent Well-Founded Semantics with eXplicit negation (WFSXp) [Alferes et al., J. Automated Reas. 14 (1) (1995) 93–147; Damásio, PhD thesis, 1996]. The framework is an extension of Antitonic Logic Programs for most cases, and is general enough to capture Probabilistic Deductive Databases, Possibilistic Logic Programming, Hybrid Probabilistic Logic Programs, and Fuzzy Logic Programming. Thus, we have a powerful mathematical formalism for dealing simultaneously with default, paraconsistency, and uncertainty reasoning. Results are provided about how our semantical framework deals with inconsistent information and with its propagation by the rules of the program
A multi-INT semantic reasoning framework for intelligence analysis support
Lockheed Martin Corp. has funded research to generate a framework
and methodology for developing semantic reasoning applications to support the
discipline oflntelligence Analysis. This chapter outlines that framework, discusses
how it may be used to advance the information sharing and integrated analytic
needs of the Intelligence Community, and suggests a system I software
architecture for such applications
Modular Logic Programming: Full Compositionality and Conflict Handling for Practical Reasoning
With the recent development of a new ubiquitous nature of data and the profusity
of available knowledge, there is nowadays the need to reason from multiple sources
of often incomplete and uncertain knowledge. Our goal was to provide a way to
combine declarative knowledge bases – represented as logic programming modules
under the answer set semantics – as well as the individual results one already inferred
from them, without having to recalculate the results for their composition and without
having to explicitly know the original logic programming encodings that produced
such results. This posed us many challenges such as how to deal with fundamental
problems of modular frameworks for logic programming, namely how to define a
general compositional semantics that allows us to compose unrestricted modules.
Building upon existing logic programming approaches, we devised a framework
capable of composing generic logic programming modules while preserving the
crucial property of compositionality, which informally means that the combination of
models of individual modules are the models of the union of modules. We are also
still able to reason in the presence of knowledge containing incoherencies, which is
informally characterised by a logic program that does not have an answer set due
to cyclic dependencies of an atom from its default negation. In this thesis we also
discuss how the same approach can be extended to deal with probabilistic knowledge
in a modular and compositional way.
We depart from the Modular Logic Programming approach in Oikarinen &
Janhunen (2008); Janhunen et al. (2009) which achieved a restricted form of compositionality
of answer set programming modules. We aim at generalising this
framework of modular logic programming and start by lifting restrictive conditions
that were originally imposed, and use alternative ways of combining these (so called
by us) Generalised Modular Logic Programs. We then deal with conflicts arising
in generalised modular logic programming and provide modular justifications and
debugging for the generalised modular logic programming setting, where justification
models answer the question: Why is a given interpretation indeed an Answer Set?
and Debugging models answer the question: Why is a given interpretation not an
Answer Set?
In summary, our research deals with the problematic of formally devising a
generic modular logic programming framework, providing: operators for combining
arbitrary modular logic programs together with a compositional semantics; We
characterise conflicts that occur when composing access control policies, which are
generalisable to our context of generalised modular logic programming, and ways of
dealing with them syntactically: provided a unification for justification and debugging
of logic programs; and semantically: provide a new semantics capable of dealing
with incoherences. We also provide an extension of modular logic programming
to a probabilistic setting. These goals are already covered with published work. A prototypical tool implementing the unification of justifications and debugging is
available for download from http://cptkirk.sourceforge.net
(Co-)Inductive semantics for Constraint Handling Rules
In this paper, we address the problem of defining a fixpoint semantics for
Constraint Handling Rules (CHR) that captures the behavior of both
simplification and propagation rules in a sound and complete way with respect
to their declarative semantics. Firstly, we show that the logical reading of
states with respect to a set of simplification rules can be characterized by a
least fixpoint over the transition system generated by the abstract operational
semantics of CHR. Similarly, we demonstrate that the logical reading of states
with respect to a set of propagation rules can be characterized by a greatest
fixpoint. Then, in order to take advantage of both types of rules without
losing fixpoint characterization, we present an operational semantics with
persistent. We finally establish that this semantics can be characterized by
two nested fixpoints, and we show the resulting language is an elegant
framework to program using coinductive reasoning.Comment: 17 page
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