15,066 research outputs found
E-Generalization Using Grammars
We extend the notion of anti-unification to cover equational theories and
present a method based on regular tree grammars to compute a finite
representation of E-generalization sets. We present a framework to combine
Inductive Logic Programming and E-generalization that includes an extension of
Plotkin's lgg theorem to the equational case. We demonstrate the potential
power of E-generalization by three example applications: computation of
suggestions for auxiliary lemmas in equational inductive proofs, computation of
construction laws for given term sequences, and learning of screen editor
command sequences.Comment: 49 pages, 16 figures, author address given in header is meanwhile
outdated, full version of an article in the "Artificial Intelligence
Journal", appeared as technical report in 2003. An open-source C
implementation and some examples are found at the Ancillary file
Synthesizing Program Input Grammars
We present an algorithm for synthesizing a context-free grammar encoding the
language of valid program inputs from a set of input examples and blackbox
access to the program. Our algorithm addresses shortcomings of existing grammar
inference algorithms, which both severely overgeneralize and are prohibitively
slow. Our implementation, GLADE, leverages the grammar synthesized by our
algorithm to fuzz test programs with structured inputs. We show that GLADE
substantially increases the incremental coverage on valid inputs compared to
two baseline fuzzers
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Evaluative conditioning of artificial grammars: evidence that subjectively-unconscious structures bias affective evaluations of novel stimuli
Evaluative conditioning (EC) refers to the acquisition of emotional valence by an initially-neutral stimulus (conditioned stimulus; CS), after being paired with an emotional stimulus (unconditioned stimulus; US). An important issue regards whether, when participants are unaware of the CS-US contingency, the affective valence can generalize to new stimuli that share similarities with the CS. Previous studies have shown that generalization of EC ef-fects appears only when participants are aware of the contingencies, but we suggest that this is because (a) the contingencies typically used in these studies are salient and easy to detect consciously, and (b) the performance-based measures of awareness (so-called “ob-jective measures”), typically used in these studies, tend to overestimate the amount of available conscious knowledge. We report a preregistered study in which participants (N = 217) were exposed to letter strings generated from two complex artificial grammars that are difficult to decipher consciously. Stimuli from one grammar were paired with positive USs, while those from the other grammar were paired with negative USs. Subsequently, partici-pants evaluated new, previously-unseen, stimuli from the positively-conditioned grammar more positively than new stimuli from the negatively-conditioned grammar. Importantly, this effect appeared even when trial-by-trial subjective measures indicated lack of relevant conscious knowledge. We provide evidence for the generalization of EC effects even with-out subjective awareness of the structures that enable those generalizations
Inducing Probabilistic Grammars by Bayesian Model Merging
We describe a framework for inducing probabilistic grammars from corpora of
positive samples. First, samples are {\em incorporated} by adding ad-hoc rules
to a working grammar; subsequently, elements of the model (such as states or
nonterminals) are {\em merged} to achieve generalization and a more compact
representation. The choice of what to merge and when to stop is governed by the
Bayesian posterior probability of the grammar given the data, which formalizes
a trade-off between a close fit to the data and a default preference for
simpler models (`Occam's Razor'). The general scheme is illustrated using three
types of probabilistic grammars: Hidden Markov models, class-based -grams,
and stochastic context-free grammars.Comment: To appear in Grammatical Inference and Applications, Second
International Colloquium on Grammatical Inference; Springer Verlag, 1994. 13
page
Turchin's Relation for Call-by-Name Computations: A Formal Approach
Supercompilation is a program transformation technique that was first
described by V. F. Turchin in the 1970s. In supercompilation, Turchin's
relation as a similarity relation on call-stack configurations is used both for
call-by-value and call-by-name semantics to terminate unfolding of the program
being transformed. In this paper, we give a formal grammar model of
call-by-name stack behaviour. We classify the model in terms of the Chomsky
hierarchy and then formally prove that Turchin's relation can terminate all
computations generated by the model.Comment: In Proceedings VPT 2016, arXiv:1607.0183
Some Novel Applications of Explanation-Based Learning to Parsing Lexicalized Tree-Adjoining Grammars
In this paper we present some novel applications of Explanation-Based
Learning (EBL) technique to parsing Lexicalized Tree-Adjoining grammars. The
novel aspects are (a) immediate generalization of parses in the training set,
(b) generalization over recursive structures and (c) representation of
generalized parses as Finite State Transducers. A highly impoverished parser
called a ``stapler'' has also been introduced. We present experimental results
using EBL for different corpora and architectures to show the effectiveness of
our approach.Comment: uuencoded postscript fil
Construct redundancy in process modelling grammars: Improving the explanatory power of ontological analysis
Conceptual modelling supports developers and users of information systems in areas of documentation, analysis or system redesign. The ongoing interest in the modelling of business processes has led to a variety of different grammars, raising the question of the quality of these grammars for modelling. An established way of evaluating the quality of a modelling grammar is by means of an ontological analysis, which can determine the extent to which grammars contain construct deficit, overload, excess or redundancy. While several studies have shown the relevance of most of these criteria, predictions about construct redundancy have yielded inconsistent results in the past, with some studies suggesting that redundancy may even be beneficial for modelling in practice. In this paper we seek to contribute to clarifying the concept of construct redundancy by introducing a revision to the ontological analysis method. Based on the concept of inheritance we propose an approach that distinguishes between specialized and distinct construct redundancy. We demonstrate the potential explanatory power of the revised method by reviewing and clarifying previous results found in the literature
A Fuzzy Approach to Erroneous Inputs in Context-Free Language Recognition
Using fuzzy context-free grammars one can easily describe a finite number of ways to derive incorrect strings together with their degree of correctness. However, in general there is an infinite number of ways to perform a certain task wrongly. In this paper we introduce a generalization of fuzzy context-free grammars, the so-called fuzzy context-free -grammars, to model the situation of making a finite choice out of an infinity of possible grammatical errors during each context-free derivation step. Under minor assumptions on the parameter this model happens to be a very general framework to describe correctly as well as erroneously derived sentences by a single generating mechanism.
Our first result characterizes the generating capacity of these fuzzy context-free -grammars. As consequences we obtain: (i) bounds on modeling grammatical errors within the framework of fuzzy context-free grammars, and (ii) the fact that the family of languages generated by fuzzy context-free -grammars shares closure properties very similar to those of the family of ordinary context-free languages.
The second part of the paper is devoted to a few algorithms to recognize fuzzy context-free languages: viz. a variant of a functional version of Cocke-Younger- Kasami's algorithm and some recursive descent algorithms. These algorithms turn out to be robust in some very elementary sense and they can easily be extended to corresponding parsing algorithms
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