Synchronous Subsequentiality and Approximations to Undecidable Problems

We introduce the class of synchronous subsequential relations, a subclass of the synchronous relations which embodies some properties of subsequential relations. If we take relations of this class as forming the possible transitions of an infinite automaton, then most decision problems (apart from membership) still remain undecidable (as they are for synchronous and subsequential rational relations), but on the positive side, they can be approximated in a meaningful way we make precise in this paper. This might make the class useful for some applications, and might serve to establish an intermediate position in the trade-off between issues of expressivity and (un)decidability.Comment: In Proceedings GandALF 2015, arXiv:1509.0685

A linear time extension of deterministic pushdown automata

Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009. Editors: Kristiina Jokinen and Eckhard Bick. NEALT Proceedings Series, Vol. 4 (2009), 182-189. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/9206

Pere Alberch's developmental morphospaces and the evolution of cognition

In this article we argue for an extension of Pere Alberch's notion of developmental morphospace into the realm of cognition and introduce the notion of cognitive phenotype as a new tool for the evolutionary and developmental study of cognitive abilities

Exact Recursive Probabilistic Programming

Recursive calls over recursive data are widely useful for generating probability distributions, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful, but unfortunately, existing probabilistic programming languages do not perform exact inference on recursive calls over recursive data, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wide variety of recursion can be expressed naturally, and inference carried out exactly. For instance, probabilistic pushdown automata and their generalizations are easy to express, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types using program transformations related to defunctionalization and refunctionalization. These transformations are assured correct by a linear type system, and a successful choice of transformations, if there is one, is guaranteed to be found by a greedy algorithm

Computational phenotypes : where the theory of computation meets evo-devo

This article argues that the Chomsky Hierarchy can be reinterpreted as a developmental morphospace constraining the evolution of a discrete and finite series of computational phenotypes. In doing so, the theory of Morphological Evolution as stated by Pere Alberch, a pioneering figure of Evo-Devo thinking, is adhered to

The Failure of the Strong Pumping Lemma for Multiple Context-Free Languages

International audienceSeki et al. (Theoretical Computer Science 88(2):191–229, 1991) showed that every m-multiple context-free language L is weakly 2m-iterative in the sense that either L is finite or L contains a subset of the form $\{u_0 w_1^i u_1 \dots w_{2m}^i u_{2m} \mid i \in \mathbb{N}\}$, where $w_1 \dots w_{2m} \neq \epsilon$. Whether for every m-multiple context-free language L is 2m-iterative, that is to say, whether all but finitely many elements $z$ of L can be written as $z=u_0 w_1 u_1 \dots w_{2m} u_{2m}$ with $w_1 \dots w_{2m} \neq \epsilon$ and $\{u_0 w_1^i u_1 \dots w_{2m}^i u_{2m} \mid i \in \mathbb{N}\} \subseteq L$ has been open. We show that there is a 3-multiple context-free language that is not k-iterative for any k