Schema Independent Relational Learning

Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions

Transcranial direct current stimulation of the frontal eye fields during pro- and antisaccade tasks

Transcranial direct current stimulation (tDCS) has been successfully applied to cortical areas such as the motor cortex and visual cortex. In the present study, we examined whether tDCS can reach and selectively modulate the excitability of the frontal eye field (FEF). In order to assess potential effects of tDCS, we measured saccade latency, landing point, and its variability in a simple prosaccade task and in an antisaccade task. In the prosaccade task, we found that anodal tDCS shortened the latency of saccades to a contralateral visual cue. However, cathodal tDCS did not show a significant modulation of saccade latency. In the antisaccade task, on the other hand, we found that the latency for ipisilateral antisaccades was prolonged during the stimulation, whereas anodal stimulation did not modulate the latency of antisaccades. In addition, anodal tDCS reduced the erroneous saccades toward the contralateral visual cue. These results in the antisaccade task suggest that tDCS modulates the function of FEF to suppress reflexive saccades to the contralateral visual cue. Both in the prosaccade and antisaccade tasks, we did not find any effect of tDCS on saccade landing point or its variability. Our present study is the first to show effects of tDCS over FEF and opens the possibility of applying tDCS for studying the functions of FEF in oculomotor and attentional performance

Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation

Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to learn theories from first-order logic representations that allows corpus-based evidence to be combined with any kind of background knowledge. This approach has been shown to be effective over several disambiguation tasks using a combination of deep and shallow knowledge sources. Is it important to understand the contribution of the various knowledge sources used in such a system. This paper investigates the contribution of nine knowledge sources to the performance of the disambiguation models produced for the SemEval-2007 English lexical sample task. The outcome of this analysis will assist future work on WSD in concentrating on the most useful knowledge sources

Theory completion using inverse entailment

The main real-world applications of Inductive Logic Programming (ILP) to date involve the "Observation Predicate Learning" (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery and language learning potential applications exist in which OPL does not hold. OPL is ingrained within the theory and performance testing of Machine Learning. A general ILP technique called "Theory Completion using Inverse Entailment" (TCIE) is introduced which is applicable to non-OPL applications. TCIE is based on inverse entailment and is closely allied to abductive inference. The implementation of TCIE within Progol5.0 is described. The implementation uses contra-positives in a similar way to Stickel's Prolog Technology Theorem Prover. Progol5.0 is tested on two different data-sets. The first dataset involves a grammar which translates numbers to their representation in English. The second dataset involves hypothesising the function of unknown genes within a network of metabolic pathways. On both datasets near complete recovery of performance is achieved after relearning when randomly chosen portions of background knowledge are removed. Progol5.0's running times for experiments in this paper were typically under 6 seconds on a standard laptop PC

Ecological Predictors of Reproductive Strategies

Since Charles Darwin’s insights into sexual selection, evolutionary psychologists have shown that human reproductive strategies are dynamic, context-dependent, and adaptive. The study of evolution and human behaviour has typically categorised adaptations as occurring at the individual level (within-subjects), as individual differences (between-subjects), or at the regional level (cross-cultural). These approaches are reviewed in Chapter 1. To assess the extent to which individuals modify their mating strategies, Chapter 2 tests women’s propensity to vary their mate preferences across different relationship types. The results indicate that women who are less experienced sexually are less likely to vary their reproductive strategies when seeking a short- vs. long-term partner. Although flexible mating strategies have been traditionally viewed as adaptive, there could be some circumstances when variation in mate behaviour is costly. Chapter 3 explores the role of social conservatism in modifying mate preference. The results indicate that men and women are less inclined to vary their short- and long-term mating behaviour when there are social taboos surrounding sexual values. These findings indicate that conservative cultures suppress sexual behaviour. I explore the implications of this in Chapter 4, where I ask whether men or women promote the sexual double standard. Here I find that both sexes are less altruistic to, and less trusting of, women that signal sexual promiscuity. Women, but not men, are driven by intrasexual competition, such that they are willing to inflict punishment on sexualised peers, even when it is costly to do so. Chapters 2-4 use experimental methods to uncover individual-level variation in mate preferences and sexual attitudes. Chapter 5 investigates the role of socioecological factors in shaping gender attitudes. The results highlight the importance of environmental harshness, inequality, and economic opportunities in fostering gender attitudes. Chapter 6 discusses the implications of the thesis, and emphasises the importance of socioecological accounts in understanding, and overcoming, unequal gender attitudes

How does predicate invention affect human comprehensibility?

During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as that of Mitchell, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present the results of experiments testing human comprehensibility of logic programs learned with and without predicate invention. Results indicate that comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols

A stochastic action language A

In this paper we present a new stochastic nondeterministic high-level action language SAA which is a stochastic extension of Action Language A. We describe the syntax and semantics of SAA and show it has an equivalent expressive power to Hidden Markov Models (HMMs). The main advantage of SAA is its smooth conversion of propositions and probability, and use of a well-established stochastic model. We show two simple examples in the nuclear reactor domain and propose a normalisation technique for declarative probability assignments which match our intuition