942 research outputs found
COLAB : a hybrid knowledge representation and compilation laboratory
Knowledge bases for real-world domains such as mechanical engineering require expressive and efficient representation and processing tools. We pursue a declarative-compilative approach to knowledge engineering. While Horn logic (as implemented in PROLOG) is well-suited for representing relational clauses, other kinds of declarative knowledge call for hybrid extensions: functional dependencies and higher-order knowledge should be modeled directly. Forward (bottom-up) reasoning should be integrated with backward (top-down) reasoning. Constraint propagation should be used wherever possible instead of search-intensive resolution. Taxonomic knowledge should be classified into an intuitive subsumption hierarchy. Our LISP-based tools provide direct translators of these declarative representations into abstract machines such as an extended Warren Abstract Machine (WAM) and specialized inference engines that are interfaced to each other. More importantly, we provide source-to-source transformers between various knowledge types, both for user convenience and machine efficiency. These formalisms with their translators and transformers have been developed as part of COLAB, a compilation laboratory for studying what we call, respectively, "vertical\u27; and "horizontal\u27; compilation of knowledge, as well as for exploring the synergetic collaboration of the knowledge representation formalisms. A case study in the realm of mechanical engineering has been an important driving force behind the development of COLAB. It will be used as the source of examples throughout the paper when discussing the enhanced formalisms, the hybrid representation architecture, and the compilers
Abstract Contract Synthesis and Verification in the Symbolic K Framework
[EN] In this article, we propose a symbolic technique that can be used for automatically inferring software contracts from programs that are written in a non-trivial fragment of C, called KERNELC, that supports pointer-based structures and heap manipulation. Starting from the semantic definition of KERNELC in the K semantic framework, we enrich the symbolic execution facilities recently provided by K with novel capabilities for contract synthesis that are based on abstract subsumption. Roughly speaking, we define an abstract symbolic technique that axiomatically explains the execution of any (modifier) C function by using other (observer) routines in the same program. We implemented our technique in the automated tool KINDSPEC 2.1, which generates logical axioms that express pre- and post-condition assertions which define the precise input/output behavior of the C routines. Thanks to the integrated support for symbolic execution and deductive verification provided by K, some synthesized axioms that cannot be guaranteed to be correct by construction due to abstraction can finally be verified in our setting with little effort.This work has been partially supported by the EC H2020-EU grant agreement No. 952215 (TAILOR), the EU (FEDER) and the Spanish MCIU under grant RTI2018-094403-B-C32, by Generalitat Valenciana under grant PROMETEO/2019/098.Alpuente Frasnedo, M.; Pardo, D.; Villanueva, A. (2020). Abstract Contract Synthesis and Verification in the Symbolic K Framework. Fundamenta Informaticae. 177(3-4):235-273. https://doi.org/10.3233/FI-2020-1989S2352731773-
Principles and Implementation of Deductive Parsing
We present a system for generating parsers based directly on the metaphor of
parsing as deduction. Parsing algorithms can be represented directly as
deduction systems, and a single deduction engine can interpret such deduction
systems so as to implement the corresponding parser. The method generalizes
easily to parsers for augmented phrase structure formalisms, such as
definite-clause grammars and other logic grammar formalisms, and has been used
for rapid prototyping of parsing algorithms for a variety of formalisms
including variants of tree-adjoining grammars, categorial grammars, and
lexicalized context-free grammars.Comment: 69 pages, includes full Prolog cod
Neuere Entwicklungen der deklarativen KI-Programmierung : proceedings
The field of declarative AI programming is briefly characterized. Its recent developments in Germany are reflected by a workshop as part of the scientific congress KI-93 at the Berlin Humboldt University. Three tutorials introduce to the state of the art in deductive databases, the programming language Gödel, and the evolution of knowledge bases. Eleven contributed papers treat knowledge revision/program transformation, types, constraints, and type-constraint combinations
Efficient Learning and Evaluation of Complex Concepts in Inductive Logic Programming
Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic
programming. In ILP, logic programming, a subset of first-order logic, is used as a uniform
representation language for the problem specification and induced theories. ILP has been
successfully applied to many real-world problems, especially in the biological domain (e.g. drug
design, protein structure prediction), where relational information is of particular importance.
The expressiveness of logic programs grants flexibility in specifying the learning task and understandability
to the induced theories. However, this flexibility comes at a high computational
cost, constraining the applicability of ILP systems. Constructing and evaluating complex concepts
remain two of the main issues that prevent ILP systems from tackling many learning
problems. These learning problems are interesting both from a research perspective, as they
raise the standards for ILP systems, and from an application perspective, where these target
concepts naturally occur in many real-world applications. Such complex concepts cannot
be constructed or evaluated by parallelizing existing top-down ILP systems or improving the
underlying Prolog engine. Novel search strategies and cover algorithms are needed.
The main focus of this thesis is on how to efficiently construct and evaluate complex hypotheses
in an ILP setting. In order to construct such hypotheses we investigate two approaches.
The first, the Top Directed Hypothesis Derivation framework, implemented in the ILP system
TopLog, involves the use of a top theory to constrain the hypothesis space. In the second approach
we revisit the bottom-up search strategy of Golem, lifting its restriction on determinate
clauses which had rendered Golem inapplicable to many key areas. These developments led to
the bottom-up ILP system ProGolem. A challenge that arises with a bottom-up approach is the
coverage computation of long, non-determinate, clauses. Prolog’s SLD-resolution is no longer
adequate. We developed a new, Prolog-based, theta-subsumption engine which is significantly
more efficient than SLD-resolution in computing the coverage of such complex clauses.
We provide evidence that ProGolem achieves the goal of learning complex concepts by presenting
a protein-hexose binding prediction application. The theory ProGolem induced has
a statistically significant better predictive accuracy than that of other learners. More importantly,
the biological insights ProGolem’s theory provided were judged by domain experts to
be relevant and, in some cases, novel
Knowledge formalization in experience feedback processes : an ontology-based approach
Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable
An Abstract Machine for Unification Grammars
This work describes the design and implementation of an abstract machine,
Amalia, for the linguistic formalism ALE, which is based on typed feature
structures. This formalism is one of the most widely accepted in computational
linguistics and has been used for designing grammars in various linguistic
theories, most notably HPSG. Amalia is composed of data structures and a set of
instructions, augmented by a compiler from the grammatical formalism to the
abstract instructions, and a (portable) interpreter of the abstract
instructions. The effect of each instruction is defined using a low-level
language that can be executed on ordinary hardware.
The advantages of the abstract machine approach are twofold. From a
theoretical point of view, the abstract machine gives a well-defined
operational semantics to the grammatical formalism. This ensures that grammars
specified using our system are endowed with well defined meaning. It enables,
for example, to formally verify the correctness of a compiler for HPSG, given
an independent definition. From a practical point of view, Amalia is the first
system that employs a direct compilation scheme for unification grammars that
are based on typed feature structures. The use of amalia results in a much
improved performance over existing systems.
In order to test the machine on a realistic application, we have developed a
small-scale, HPSG-based grammar for a fragment of the Hebrew language, using
Amalia as the development platform. This is the first application of HPSG to a
Semitic language.Comment: Doctoral Thesis, 96 pages, many postscript figures, uses pstricks,
pst-node, psfig, fullname and a macros fil
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