Learning Rational Stochastic Tree Languages

Abstract. We consider the problem of learning stochastic tree languages, i.e. probability distributions over a set of trees T(F), from a sample of trees independently drawn according to an unknown target P. We consider the case where the target is a rational stochastic tree language, i.e. it can be computed by a rational tree series or, equivalently, by a multiplicity tree automaton. In this paper, we provide two contributions. First, we show that rational tree series admit a canonical representation with parameters that can be efficiently estimated from samples. Then, we give an inference algorithm that identifies the class of rational stochastic tree languages in the limit with probability one.

New advances in logic-based probabilistic modeling by PRISM

Abstract. We review a logic-based modeling language PRISM and report recent developments including belief propagation by the generalized inside-outside algorithm and generative modeling with constraints. The former implies PRISM subsumes belief propagation at the algorithmic level. We also compare the performance of PRISM with state-of-theart systems in statistical natural language processing and probabilistic inference in Bayesian networks respectively, and show that PRISM is reasonably competitive.

Improving Methodological Standards in Behavioral Interventions for Cognitive Enhancement

There is substantial interest in the possibility that cognitive skills can be improved by dedicated behavioral training. Yet despite the large amount of work being conducted in this domain, there is not an explicit and widely agreed upon consensus around the best methodological practices. This document seeks to fill this gap. We start from the perspective that there are many types of studies that are important in this domain—e.g., feasibility, mechanistic, efficacy, and effectiveness. These studies have fundamentally different goals, and, as such, the best-practice methods to meet those goals will also differ. We thus make suggestions in topics ranging from the design and implementation of control groups, to reporting of results, to dissemination and communication, taking the perspective that the best practices are not necessarily uniform across all study types. We also explicitly recognize and discuss the fact that there are methodological issues around which we currently lack the theoretical and/or empirical foundation to determine best practices (e.g., as pertains to assessing participant expectations). For these, we suggest important routes forward, including greater interdisciplinary collaboration with individuals from domains that face related concerns. Our hope is that these recommendations will greatly increase the rate at which science in this domain advances.Action Contro