Machine Learning Theory and Practice as a Source of Insight into Universal Grammar

Articl

On empirical methodology, constraints, and hierarchy in artificial grammar learning

This paper considers the AGL literature from a psycholinguistic perspective. It first presents a taxonomy of the experimental familiarization test procedures used, which is followed by a consideration of shortcomings and potential improvements of the empirical methodology. It then turns to reconsidering the issue of grammar learning from the point of view of acquiring constraints, instead of the traditional AGL approach in terms of acquiring sets of rewrite rules. This is, in particular, a natural way of handling long‐distance dependences. The final section addresses an underdeveloped issue in the AGL literature, namely how to detect latent hierarchical structure in AGL response patterns

A Grammatical Inference Approach to Language-Based Anomaly Detection in XML

False-positives are a problem in anomaly-based intrusion detection systems. To counter this issue, we discuss anomaly detection for the eXtensible Markup Language (XML) in a language-theoretic view. We argue that many XML-based attacks target the syntactic level, i.e. the tree structure or element content, and syntax validation of XML documents reduces the attack surface. XML offers so-called schemas for validation, but in real world, schemas are often unavailable, ignored or too general. In this work-in-progress paper we describe a grammatical inference approach to learn an automaton from example XML documents for detecting documents with anomalous syntax. We discuss properties and expressiveness of XML to understand limits of learnability. Our contributions are an XML Schema compatible lexical datatype system to abstract content in XML and an algorithm to learn visibly pushdown automata (VPA) directly from a set of examples. The proposed algorithm does not require the tree representation of XML, so it can process large documents or streams. The resulting deterministic VPA then allows stream validation of documents to recognize deviations in the underlying tree structure or datatypes.Comment: Paper accepted at First Int. Workshop on Emerging Cyberthreats and Countermeasures ECTCM 201

Classifying the Arithmetical Complexity of Teaching Models

This paper classifies the complexity of various teaching models by their position in the arithmetical hierarchy. In particular, we determine the arithmetical complexity of the index sets of the following classes: (1) the class of uniformly r.e. families with finite teaching dimension, and (2) the class of uniformly r.e. families with finite positive recursive teaching dimension witnessed by a uniformly r.e. teaching sequence. We also derive the arithmetical complexity of several other decision problems in teaching, such as the problem of deciding, given an effective coding $\{\mathcal L_0,\mathcal L_1,\mathcal L_2,\ldots\}$ of all uniformly r.e. families, any $e$ such that $\mathcal L_e = \{L^e_0,L^e_1,\ldots,\}$, any $i$ and $d$, whether or not the teaching dimension of $L^e_i$ with respect to $\mathcal L_e$ is upper bounded by $d$.Comment: 15 pages in International Conference on Algorithmic Learning Theory, 201

Context and perceptual salience influence the formation of novel stereotypes via cumulative cultural evolution

We use a transmission chain method to establish how context and category salience influence the formation of novel stereotypes through cumulative cultural evolution. We created novel alien targets by combining features from three category dimensions—color, movement, and shape—thereby creating social targets that were individually unique but that also shared category membership with other aliens (e.g., two aliens might be the same color and shape but move differently). At the start of the transmission chains each alien was randomly assigned attributes that described it (e.g., arrogant, caring, confident). Participants were given training on the alien-attribute assignments and were then tested on their memory for these. The alien-attribute assignments participants produced during test were used as the training materials for the next participant in the transmission chain. As information was repeatedly transmitted an increasingly simplified, learnable stereotype-like structure emerged for targets who shared the same color, such that by the end of the chains targets who shared the same color were more likely to share the same attributes (a reanalysis of data from Martin et al., 2014 which we term Experiment 1). The apparent bias toward the formation of novel stereotypes around the color category dimension was also found for objects (Experiment 2). However, when the category dimension of color was made less salient, it no longer dominated the formation of novel stereotypes (Experiment 3). The current findings suggest that context and category salience influence category dimension salience, which in turn influences the cumulative cultural evolution of information.<br/