4,124 research outputs found
S+Net: extending functional coordination with extra-functional semantics
This technical report introduces S+Net, a compositional coordination language
for streaming networks with extra-functional semantics. Compositionality
simplifies the specification of complex parallel and distributed applications;
extra-functional semantics allow the application designer to reason about and
control resource usage, performance and fault handling. The key feature of
S+Net is that functional and extra-functional semantics are defined
orthogonally from each other. S+Net can be seen as a simultaneous
simplification and extension of the existing coordination language S-Net, that
gives control of extra-functional behavior to the S-Net programmer. S+Net can
also be seen as a transitional research step between S-Net and AstraKahn,
another coordination language currently being designed at the University of
Hertfordshire. In contrast with AstraKahn which constitutes a re-design from
the ground up, S+Net preserves the basic operational semantics of S-Net and
thus provides an incremental introduction of extra-functional control in an
existing language.Comment: 34 pages, 11 figures, 3 table
Disjunctive Logic Programs with Inheritance
The paper proposes a new knowledge representation language, called DLP<,
which extends disjunctive logic programming (with strong negation) by
inheritance. The addition of inheritance enhances the knowledge modeling
features of the language providing a natural representation of default
reasoning with exceptions.
A declarative model-theoretic semantics of DLP< is provided, which is shown
to generalize the Answer Set Semantics of disjunctive logic programs.
The knowledge modeling features of the language are illustrated by encoding
classical nonmonotonic problems in DLP<.
The complexity of DLP< is analyzed, proving that inheritance does not cause
any computational overhead, as reasoning in DLP< has exactly the same
complexity as reasoning in disjunctive logic programming. This is confirmed by
the existence of an efficient translation from DLP< to plain disjunctive logic
programming. Using this translation, an advanced KR system supporting the DLP<
language has been implemented on top of the DLV system and has subsequently
been integrated into DLV.Comment: 28 pages; will be published in Theory and Practice of Logic
Programmin
Multi-Dimensional Inheritance
In this paper, we present an alternative approach to multiple inheritance for
typed feature structures. In our approach, a feature structure can be
associated with several types coming from different hierarchies (dimensions).
In case of multiple inheritance, a type has supertypes from different
hierarchies. We contrast this approach with approaches based on a single type
hierarchy where a feature structure has only one unique most general type, and
multiple inheritance involves computation of greatest lower bounds in the
hierarchy. The proposed approach supports current linguistic analyses in
constraint-based formalisms like HPSG, inheritance in the lexicon, and
knowledge representation for NLP systems. Finally, we show that
multi-dimensional inheritance hierarchies can be compiled into a Prolog term
representation, which allows to compute the conjunction of two types
efficiently by Prolog term unification.Comment: 9 pages, styles: a4,figfont,eepic,eps
Bayesian Logic Programs
Bayesian networks provide an elegant formalism for representing and reasoning
about uncertainty using probability theory. Theyare a probabilistic extension
of propositional logic and, hence, inherit some of the limitations of
propositional logic, such as the difficulties to represent objects and
relations. We introduce a generalization of Bayesian networks, called Bayesian
logic programs, to overcome these limitations. In order to represent objects
and relations it combines Bayesian networks with definite clause logic by
establishing a one-to-one mapping between ground atoms and random variables. We
show that Bayesian logic programs combine the advantages of both definite
clause logic and Bayesian networks. This includes the separation of
quantitative and qualitative aspects of the model. Furthermore, Bayesian logic
programs generalize both Bayesian networks as well as logic programs. So, many
ideas developedComment: 52 page
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