7,764 research outputs found
A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees
FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language")
is a fuzzy logic programming language with implicit/explicit truth degree
annotations, a great variety of connectives and unification by similarity.
FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic
Programming, a fuzzy logic language with explicitly annotated rules) and
Bousi~Prolog (which uses a weak unification algorithm and is well suited for
flexible query answering). Hence, it properly manages similarity and truth
degrees in a single framework combining the expressive benefits of both
languages. This paper presents the main features and implementations details of
FASILL. Along the paper we describe its syntax and operational semantics and we
give clues of the implementation of the lattice module and the similarity
module, two of the main building blocks of the new programming environment
which enriches the FLOPER system developed in our research group.Comment: In Proceedings PROLE 2014, arXiv:1501.0169
Handling Network Partitions and Mergers in Structured Overlay Networks
Structured overlay networks form a major class of peer-to-peer systems, which are touted for their abilities to
scale, tolerate failures, and self-manage. Any long-lived
Internet-scale distributed system is destined to face network partitions. Although the problem of network partitions
and mergers is highly related to fault-tolerance and
self-management in large-scale systems, it has hardly been
studied in the context of structured peer-to-peer systems.
These systems have mainly been studied under churn (frequent
joins/failures), which as a side effect solves the problem
of network partitions, as it is similar to massive node
failures. Yet, the crucial aspect of network mergers has been
ignored. In fact, it has been claimed that ring-based structured
overlay networks, which constitute the majority of the
structured overlays, are intrinsically ill-suited for merging
rings. In this paper, we present an algorithm for merging
multiple similar ring-based overlays when the underlying
network merges. We examine the solution in dynamic conditions,
showing how our solution is resilient to churn during
the merger, something widely believed to be difficult or
impossible. We evaluate the algorithm for various scenarios
and show that even when falsely detecting a merger, the
algorithm quickly terminates and does not clutter the network
with many messages. The algorithm is flexible as the
tradeoff between message complexity and time complexity
can be adjusted by a parameter
A Transformation-based Implementation for CLP with Qualification and Proximity
Uncertainty in logic programming has been widely investigated in the last
decades, leading to multiple extensions of the classical LP paradigm. However,
few of these are designed as extensions of the well-established and powerful
CLP scheme for Constraint Logic Programming. In a previous work we have
proposed the SQCLP (proximity-based qualified constraint logic programming)
scheme as a quite expressive extension of CLP with support for qualification
values and proximity relations as generalizations of uncertainty values and
similarity relations, respectively. In this paper we provide a transformation
technique for transforming SQCLP programs and goals into semantically
equivalent CLP programs and goals, and a practical Prolog-based implementation
of some particularly useful instances of the SQCLP scheme. We also illustrate,
by showing some simple-and working-examples, how the prototype can be
effectively used as a tool for solving problems where qualification values and
proximity relations play a key role. Intended use of SQCLP includes flexible
information retrieval applications.Comment: 49 pages, 5 figures, 1 table, preliminary version of an article of
the same title, published as Technical Report SIC-4-10, Universidad
Complutense, Departamento de Sistemas Inform\'aticos y Computaci\'on, Madrid,
Spai
End-to-End Differentiable Proving
We introduce neural networks for end-to-end differentiable proving of queries
to knowledge bases by operating on dense vector representations of symbols.
These neural networks are constructed recursively by taking inspiration from
the backward chaining algorithm as used in Prolog. Specifically, we replace
symbolic unification with a differentiable computation on vector
representations of symbols using a radial basis function kernel, thereby
combining symbolic reasoning with learning subsymbolic vector representations.
By using gradient descent, the resulting neural network can be trained to infer
facts from a given incomplete knowledge base. It learns to (i) place
representations of similar symbols in close proximity in a vector space, (ii)
make use of such similarities to prove queries, (iii) induce logical rules, and
(iv) use provided and induced logical rules for multi-hop reasoning. We
demonstrate that this architecture outperforms ComplEx, a state-of-the-art
neural link prediction model, on three out of four benchmark knowledge bases
while at the same time inducing interpretable function-free first-order logic
rules.Comment: NIPS 2017 camera-ready, NIPS 201
A Data-Oriented Approach to Semantic Interpretation
In Data-Oriented Parsing (DOP), an annotated language corpus is used as a
stochastic grammar. The most probable analysis of a new input sentence is
constructed by combining sub-analyses from the corpus in the most probable way.
This approach has been succesfully used for syntactic analysis, using corpora
with syntactic annotations such as the Penn Treebank. If a corpus with
semantically annotated sentences is used, the same approach can also generate
the most probable semantic interpretation of an input sentence. The present
paper explains this semantic interpretation method, and summarizes the results
of a preliminary experiment. Semantic annotations were added to the syntactic
annotations of most of the sentences of the ATIS corpus. A data-oriented
semantic interpretation algorithm was succesfully tested on this semantically
enriched corpus.Comment: 10 pages, Postscript; to appear in Proceedings Workshop on
Corpus-Oriented Semantic Analysis, ECAI-96, Budapes
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