7,660 research outputs found
Overview of an abstract fixed point theory for non-monotonic functions and its applications to logic programming
The purpose of the present paper is to give an overview of our joint work with Zoltán Ésik, namely the development of an abstract fixed point theory for a class of non-monotonic functions [4] and its use in providing a novel denotational semantics for a very broad extension of classical logic programming [1]. Our purpose is to give a high-level presentation of the main developments of these two works, that avoids as much as possible the underlying technical details, and which can be used as a mild introduction to the area
An analysis of the equational properties of the well-founded fixed point
Well-founded fixed points have been used in several areas of knowledge
representation and reasoning and to give semantics to logic programs involving
negation. They are an important ingredient of approximation fixed point theory.
We study the logical properties of the (parametric) well-founded fixed point
operation. We show that the operation satisfies several, but not all of the
equational properties of fixed point operations described by the axioms of
iteration theories
The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence
Intelligent systems based on first-order logic on the one hand, and on
artificial neural networks (also called connectionist systems) on the other,
differ substantially. It would be very desirable to combine the robust neural
networking machinery with symbolic knowledge representation and reasoning
paradigms like logic programming in such a way that the strengths of either
paradigm will be retained. Current state-of-the-art research, however, fails by
far to achieve this ultimate goal. As one of the main obstacles to be overcome
we perceive the question how symbolic knowledge can be encoded by means of
connectionist systems: Satisfactory answers to this will naturally lead the way
to knowledge extraction algorithms and to integrated neural-symbolic systems.Comment: In Proceedings of INFORMATION'2004, Tokyo, Japan, to appear. 12 page
Mixin Composition Synthesis based on Intersection Types
We present a method for synthesizing compositions of mixins using type
inhabitation in intersection types. First, recursively defined classes and
mixins, which are functions over classes, are expressed as terms in a lambda
calculus with records. Intersection types with records and record-merge are
used to assign meaningful types to these terms without resorting to recursive
types. Second, typed terms are translated to a repository of typed combinators.
We show a relation between record types with record-merge and intersection
types with constructors. This relation is used to prove soundness and partial
completeness of the translation with respect to mixin composition synthesis.
Furthermore, we demonstrate how a translated repository and goal type can be
used as input to an existing framework for composition synthesis in bounded
combinatory logic via type inhabitation. The computed result is a class typed
by the goal type and generated by a mixin composition applied to an existing
class
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.Comment: 28 page
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