6,828 research outputs found
Data types as a more ergonomic frontend for Grammar-Guided Genetic Programming
Genetic Programming (GP) is an heuristic method that can be applied to many
Machine Learning, Optimization and Engineering problems. In particular, it has
been widely used in Software Engineering for Test-case generation, Program
Synthesis and Improvement of Software (GI).
Grammar-Guided Genetic Programming (GGGP) approaches allow the user to refine
the domain of valid program solutions. Backus Normal Form is the most popular
interface for describing Context-Free Grammars (CFG) for GGGP. BNF and its
derivatives have the disadvantage of interleaving the grammar language and the
target language of the program.
We propose to embed the grammar as an internal Domain-Specific Language in
the host language of the framework. This approach has the same expressive power
as BNF and EBNF while using the host language type-system to take advantage of
all the existing tooling: linters, formatters, type-checkers, autocomplete, and
legacy code support. These tools have a practical utility in designing software
in general, and GP systems in particular.
We also present Meta-Handlers, user-defined overrides of the tree-generation
system. This technique extends our object-oriented encoding with more
practicability and expressive power than existing CFG approaches, achieving the
same expressive power of Attribute Grammars, but without the grammar vs target
language duality.
Furthermore, we evidence that this approach is feasible, showing an example
Python implementation as proof. We also compare our approach against textual
BNF-representations w.r.t. expressive power and ergonomics. These advantages do
not come at the cost of performance, as shown by our empirical evaluation on 5
benchmarks of our example implementation against PonyGE2. We conclude that our
approach has better ergonomics with the same expressive power and performance
of textual BNF-based grammar encodings
BSML: A Binding Schema Markup Language for Data Interchange in Problem Solving Environments (PSEs)
We describe a binding schema markup language (BSML) for describing data
interchange between scientific codes. Such a facility is an important
constituent of scientific problem solving environments (PSEs). BSML is designed
to integrate with a PSE or application composition system that views model
specification and execution as a problem of managing semistructured data. The
data interchange problem is addressed by three techniques for processing
semistructured data: validation, binding, and conversion. We present BSML and
describe its application to a PSE for wireless communications system design
Probabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient retrieval of
most probable analyses. These questions have been answered satisfactorily only
for probabilistic regular and context-free models. We address these problems
for a more expressive probabilistic constraint logic programming model. We
present a log-linear probability model for probabilistic constraint logic
programming. On top of this model we define an algorithm to estimate the
parameters and to select the properties of log-linear models from incomplete
data. This algorithm is an extension of the improved iterative scaling
algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm
applies to log-linear models in general and is accompanied with suitable
approximation methods when applied to large data spaces. Furthermore, we
present an approach for searching for most probable analyses of the
probabilistic constraint logic programming model. This method can be applied to
the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl
Advances and applications of automata on words and trees : abstracts collection
From 12.12.2010 to 17.12.2010, the Dagstuhl Seminar 10501 "Advances and Applications of Automata on Words and Trees" was held in Schloss Dagstuhl - Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available
Learning morphological phenomena of Modern Greek an exploratory approach
This paper presents a computational model for the description of concatenative morphological phenomena of modern Greek (such as inflection, derivation and compounding) to allow learners, trainers and developers to explore linguistic processes through their own constructions in an interactive openāended multimedia environment. The proposed model introduces a new language metaphor, the āpuzzleāmetaphorā (similar to the existing āturtleāmetaphorā for concepts from mathematics and physics), based on a visualized unificationālike mechanism for pattern matching. The computational implementation of the model can be used for creating environments for learning through design and learning by teaching
gMotion: A spatio-temporal grammar for the procedural generation of motion graphics
Creating by hand compelling 2D animations that choreograph several groups of shapes requires a large number of manual edits. We present a method to procedurally generate motion graphics with timeslice grammars. Timeslice grammars are to time what split grammars are to space. We use this grammar to formally model motion graphics, manipulating them in both temporal and spatial components. We are able to combine both these aspects by representing animations as sets of affine transformations sampled uniformly in both space and time. Rules and operators in the grammar manipulate all spatio-temporal matrices as a whole, allowing us to expressively construct animation with few rules. The grammar animates shapes, which are represented as highly tessellated polygons, by applying the affine transforms to each shape vertex given the vertex position and the animation time. We introduce a small set of operators showing how we can produce 2D animations of geometric objects, by combining the expressive power of the grammar model, the composability of the operators with themselves, and the capabilities that derive from using a unified spatio-temporal representation for animation data. Throughout the paper, we show how timeslice grammars can produce a wide variety of animations that would take artists hours of tedious and time-consuming work. In particular, in cases where change of shapes is very common, our grammar can add motion detail to large collections of shapes with greater control over per-shape animations along with a compact rules structure
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