5,669 research outputs found
Coherent Integration of Databases by Abductive Logic Programming
We introduce an abductive method for a coherent integration of independent
data-sources. The idea is to compute a list of data-facts that should be
inserted to the amalgamated database or retracted from it in order to restore
its consistency. This method is implemented by an abductive solver, called
Asystem, that applies SLDNFA-resolution on a meta-theory that relates
different, possibly contradicting, input databases. We also give a pure
model-theoretic analysis of the possible ways to `recover' consistent data from
an inconsistent database in terms of those models of the database that exhibit
as minimal inconsistent information as reasonably possible. This allows us to
characterize the `recovered databases' in terms of the `preferred' (i.e., most
consistent) models of the theory. The outcome is an abductive-based application
that is sound and complete with respect to a corresponding model-based,
preferential semantics, and -- to the best of our knowledge -- is more
expressive (thus more general) than any other implementation of coherent
integration of databases
On-line planning and scheduling: an application to controlling modular printers
We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge
Proceedings of the International Workshop on Reactive Concepts in Knowledge Representation 2014
These are the proceedings of the International Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014), which took place on August 19th, 2014 in Prague, co-located with the 21st European Conference on Artificial Intelligence (ECAI 2014)
Specifying Logic Programs in Controlled Natural Language
Writing specifications for computer programs is not easy since one has to
take into account the disparate conceptual worlds of the application domain and
of software development. To bridge this conceptual gap we propose controlled
natural language as a declarative and application-specific specification
language. Controlled natural language is a subset of natural language that can
be accurately and efficiently processed by a computer, but is expressive enough
to allow natural usage by non-specialists. Specifications in controlled natural
language are automatically translated into Prolog clauses, hence become formal
and executable. The translation uses a definite clause grammar (DCG) enhanced
by feature structures. Inter-text references of the specification, e.g.
anaphora, are resolved with the help of discourse representation theory (DRT).
The generated Prolog clauses are added to a knowledge base. We have implemented
a prototypical specification system that successfully processes the
specification of a simple automated teller machine.Comment: 16 pages, compressed, uuencoded Postscript, published in Proceedings
CLNLP 95, COMPULOGNET/ELSNET/EAGLES Workshop on Computational Logic for
Natural Language Processing, Edinburgh, April 3-5, 199
Towards a Programming Paradigm for Artificial Intelligence Applications Based On Simulation
In this work, we propose to use simulation as a key principle for programming AI applications. The approach aims at integrating techniques from different areas of AI and is based on the idea that simulated entities may freely exchange data and behavioural patterns. We introduce basic notions of a simulation-based programming paradigm and show how it can be used for implementing different scenarios
Logic Programming as Constructivism
The features of logic programming that
seem unconventional from the viewpoint of classical logic
can be explained in terms of constructivistic logic. We
motivate and propose a constructivistic proof theory of
non-Horn logic programming. Then, we apply this formalization
for establishing results of practical interest.
First, we show that 'stratification can be motivated in a
simple and intuitive way. Relying on similar motivations,
we introduce the larger classes of 'loosely stratified' and
'constructively consistent' programs. Second, we give a
formal basis for introducing quantifiers into queries and
logic programs by defining 'constructively domain
independent* formulas. Third, we extend the Generalized
Magic Sets procedure to loosely stratified and constructively
consistent programs, by relying on a 'conditional
fixpoini procedure
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