30 research outputs found
A flexible framework for defeasible logics
Logics for knowledge representation suffer from over-specialization: while
each logic may provide an ideal representation formalism for some problems, it
is less than optimal for others. A solution to this problem is to choose from
several logics and, when necessary, combine the representations. In general,
such an approach results in a very difficult problem of combination. However,
if we can choose the logics from a uniform framework then the problem of
combining them is greatly simplified. In this paper, we develop such a
framework for defeasible logics. It supports all defeasible logics that satisfy
a strong negation principle. We use logic meta-programs as the basis for the
framework.Comment: Proceedings of 8th International Workshop on Non-Monotonic Reasoning,
April 9-11, 2000, Breckenridge, Colorad
Shinren : Non-monotonic trust management for distributed systems
The open and dynamic nature of modern distributed systems and pervasive environments presents significant challenges to security management. One solution may be trust management which utilises the notion of trust in order to specify and interpret security policies and make decisions on security-related actions. Most trust management systems assume monotonicity where additional information can only result in the increasing of trust. The monotonic assumption oversimplifies the real world by not considering negative information, thus it cannot handle many real world scenarios. In this paper we present Shinren, a novel non-monotonic trust management system based on bilattice theory and the anyworld assumption. Shinren takes into account negative information and supports reasoning with incomplete information, uncertainty and inconsistency. Information from multiple sources such as credentials, recommendations, reputation and local knowledge can be used and combined in order to establish trust. Shinren also supports prioritisation which is important in decision making and resolving modality conflicts that are caused by non-monotonicity
Stable Model Counting and Its Application in Probabilistic Logic Programming
Model counting is the problem of computing the number of models that satisfy
a given propositional theory. It has recently been applied to solving inference
tasks in probabilistic logic programming, where the goal is to compute the
probability of given queries being true provided a set of mutually independent
random variables, a model (a logic program) and some evidence. The core of
solving this inference task involves translating the logic program to a
propositional theory and using a model counter. In this paper, we show that for
some problems that involve inductive definitions like reachability in a graph,
the translation of logic programs to SAT can be expensive for the purpose of
solving inference tasks. For such problems, direct implementation of stable
model semantics allows for more efficient solving. We present two
implementation techniques, based on unfounded set detection, that extend a
propositional model counter to a stable model counter. Our experiments show
that for particular problems, our approach can outperform a state-of-the-art
probabilistic logic programming solver by several orders of magnitude in terms
of running time and space requirements, and can solve instances of
significantly larger sizes on which the current solver runs out of time or
memory.Comment: Accepted in AAAI, 201
A Unified Framework For Three-Valued Semantical Treatments of Logic Programming
Based on Fiting\u27s Φ operator a unified framework for three-valued semantics of logic programming is presented. The truth space used in the framework is the class of partial interpretations. Underlying the truth space is two partial orderings, knowledge ordering and truth ordering. It turns out that the truth space with the truth ordering is a complete lattice and the truth space with knowledge ordering is a semi-complete lattice. Φ is proved to be continuous over the complete lattice and monotonic over the semi-complete lattice. With the use of Φ operator two well-known three-valued semantics for logic programming, Fitting\u27s three-valued semantics and well-founded semantics, are characterized within the framework in a simple and elegant way. We show that Fitting\u27s semantics is the least stable three-valued model with respect to the knowledge ordering and well-founded semantics is the least stable three-valued model with respect to the truth ordering
A Goal-Directed Implementation of Query Answering for Hybrid MKNF Knowledge Bases
Ontologies and rules are usually loosely coupled in knowledge representation
formalisms. In fact, ontologies use open-world reasoning while the leading
semantics for rules use non-monotonic, closed-world reasoning. One exception is
the tightly-coupled framework of Minimal Knowledge and Negation as Failure
(MKNF), which allows statements about individuals to be jointly derived via
entailment from an ontology and inferences from rules. Nonetheless, the
practical usefulness of MKNF has not always been clear, although recent work
has formalized a general resolution-based method for querying MKNF when rules
are taken to have the well-founded semantics, and the ontology is modeled by a
general oracle. That work leaves open what algorithms should be used to relate
the entailments of the ontology and the inferences of rules. In this paper we
provide such algorithms, and describe the implementation of a query-driven
system, CDF-Rules, for hybrid knowledge bases combining both (non-monotonic)
rules under the well-founded semantics and a (monotonic) ontology, represented
by a CDF Type-1 (ALQ) theory. To appear in Theory and Practice of Logic
Programming (TPLP
Beyond depth-first: improving tabled logic programs through alternative scheduling strategies
Journal ArticleTabled evaluations ensure termination of logic programs with fi nite models by keeping track of which subgoals have been called Given several variant subgoals in an evaluation, only the fi rst one encountered will use program clause resolution the rest uses answer resolution This use of answer resolution prevents infi nite looping which happens in SLD Given the asynchronicity of answer generation and answer return, tabling systems face an important scheduling choice not present in traditional top-down evaluation How does the order of returning answers to consuming subgoals affect program efficiency This paper investigates alternate scheduling strategies for tabling in a WAM implementation, the SLG-WAM. The original SLG-WAM had a simple mechanism of scheduling answers to be returned to callers which was expensive in terms of trailing and choice point creation We propose here a more sophisticated scheduling strategy, Batched Scheduling, which reduces the overheads of these operations and provides dramatic space reduction as well as speedups for many programs We also propose a second strategy, Local Scheduling, which has applications to non-monotonic reasoning and when combined with answer subsumption can improve the performance of some programs by arbitrary amounts