19,653 research outputs found
Transforming specifications of observable behaviour into programs
A methodology for deriving programs from specifications of observable
behaviour is described. The class of processes to which this methodology
is applicable includes those whose state changes are fully definable by labelled
transition systems, for example communicating processes without
internal state changes. A logic program representation of such labelled
transition systems is proposed, interpreters based on path searching techniques
are defined, and the use of partial evaluation techniques to derive
the executable programs is described
Business Process Configuration According to Data Dependency Specification
Configuration techniques have been used in several fields, such as the design of business
process models. Sometimes these models depend on the data dependencies, being easier to describe
what has to be done instead of how. Configuration models enable to use a declarative representation
of business processes, deciding the most appropriate work-flow in each case. Unfortunately,
data dependencies among the activities and how they can affect the correct execution of the process,
has been overlooked in the declarative specifications and configurable systems found in the literature.
In order to find the best process configuration for optimizing the execution time of processes according
to data dependencies, we propose the use of Constraint Programming paradigm with the aim of
obtaining an adaptable imperative model in function of the data dependencies of the activities
described declarative.Ministerio de Ciencia y Tecnología TIN2015-63502-C3-2-RFondo Europeo de Desarrollo Regiona
An Algebra of Hierarchical Graphs and its Application to Structural Encoding
We define an algebraic theory of hierarchical graphs, whose axioms
characterise graph isomorphism: two terms are equated exactly when
they represent the same graph. Our algebra can be understood as
a high-level language for describing graphs with a node-sharing, embedding
structure, and it is then well suited for defining graphical
representations of software models where nesting and linking are key
aspects. In particular, we propose the use of our graph formalism as a
convenient way to describe configurations in process calculi equipped
with inherently hierarchical features such as sessions, locations, transactions,
membranes or ambients. The graph syntax can be seen as an
intermediate representation language, that facilitates the encodings of
algebraic specifications, since it provides primitives for nesting, name
restriction and parallel composition. In addition, proving soundness
and correctness of an encoding (i.e. proving that structurally equivalent
processes are mapped to isomorphic graphs) becomes easier as it can
be done by induction over the graph syntax
Hierarchical models for service-oriented systems
We present our approach to the denotation and representation of hierarchical graphs: a suitable algebra of hierarchical graphs and two domains of interpretations. Each domain of interpretation focuses on a particular perspective of the graph hierarchy: the top view (nested boxes) is based on a notion of embedded graphs while the side view (tree hierarchy) is based on gs-graphs. Our algebra can be understood as a high-level language for describing such graphical models, which are well suited for defining graphical representations of service-oriented systems where nesting (e.g. sessions, transactions, locations) and linking (e.g. shared channels, resources, names) are key aspects
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Language acquisition and machine learning
In this paper, we review recent progress in the field of machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, we propose four component tasks involved in learning from experience - aggregation, clustering, characterization, and storage. We then consider four common problems studied by machine learning researchers - learning from examples, heuristics learning, conceptual clustering, and learning macro-operators - describing each in terms of our framework. After this, we turn to the problem of grammar acquisition, relating this problem to other learning tasks and reviewing four AI systems that have addressed the problem. Finally, we note some limitations of the earlier work and propose an alternative approach to modeling the mechanisms underlying language acquisition
Representing a P-complete problem by small trellis automata
A restricted case of the Circuit Value Problem known as the Sequential NOR
Circuit Value Problem was recently used to obtain very succinct examples of
conjunctive grammars, Boolean grammars and language equations representing
P-complete languages (Okhotin, http://dx.doi.org/10.1007/978-3-540-74593-8_23
"A simple P-complete problem and its representations by language equations",
MCU 2007). In this paper, a new encoding of the same problem is proposed, and a
trellis automaton (one-way real-time cellular automaton) with 11 states solving
this problem is constructed
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