Constraint Solving over Multiple Similarity Relations

Similarity relations are reflexive, symmetric, and transitive fuzzy relations. They help to make approximate inferences, replacing the notion of equality. Similarity-based unification has been quite intensively investigated, as a core computational method for approximate reasoning and declarative programming. In this paper we consider solving constraints over several similarity relations, instead of a single one. Multiple similarities pose challenges to constraint solving, since we can not rely on the transitivity property anymore. Existing methods for unification with fuzzy proximity relations (reflexive, symmetric, non-transitive relations) do not provide a solution that would adequately reflect particularities of dealing with multiple similarities. To address this problem, we develop a constraint solving algorithm for multiple similarity relations, prove its termination, soundness, and completeness properties, and discuss applications

Regular matching problems for infinite trees

We investigate regular matching problems. The classical reference is Conway's textbook "Regular algebra and finite machines". Some of his results can be stated as follows. Let $L\subseteq(\Sigma\cup X)^*$ and $R\subseteq\Sigma^*$ be regular languages where $\Sigma$ is a set of constants and $X$ is a set of variables. Substituting every $x\in X$ by a regular subset $\sigma(x)$ of $\Sigma^*$ yields a regular set $\sigma(L)\subseteq\Sigma^*$. A substitution $\sigma$ solves a matching problem "$L\subseteq R$?" if $\sigma(L)\subseteq R$. There are finitely many maximal solutions $\sigma$; they are effectively computable and $\sigma(x)$ is regular for all $x\in X$; and every solution is included in a maximal one. Also, in the case of words "$\exists\sigma:\sigma(L)=R$?" is decidable. Apart from the last property, we generalize these results to infinite trees. We define a notion of choice function $\gamma$ which for any tree $s$ over $\Sigma\cup X$ and position $u$ of a variable $x$ selects at most one tree $\gamma(u)\in\sigma(x)$; next, we define $\gamma_\infty(s)$ as the limit of a Cauchy sequence; and the union over all $\gamma_\infty(s)$ yields $\sigma(s)$. Since our definition coincides with that for IO substitutions, we write $\sigma_{io}(L)$ instead of $\sigma(L)$. Our main result is the decidability of "$\exists\sigma:\sigma_{io}(L)\subseteq R$?" if $R$ is regular and $L$ belongs to a class of tree languages closed under intersection with regular sets. Such a special case pops up if $L$ is context-free. Note that "$\exists\sigma:\sigma_{io}(L)=R$?" is undecidable, in general in that case. However, the decidability of "$\exists\sigma:\sigma_{io}(L)=R$?" if both $L$ and $R$ are regular remains open because, in contrast to word languages, the homomorphic image of a regular tree language is not always regular if $\sigma(x)$ is regular for all $x\in X$.Comment: 18 pages. This replacement eliminates a false claim from the previous arXiv version of this paper: Item 4 of Theorem 1 did not hold for # = {=

Strategies in PRholog

PRholog is an experimental extension of logic programming with strategic conditional transformation rules, combining Prolog with Rholog calculus. The rules perform nondeterministic transformations on hedges. Queries may have several results that can be explored on backtracking. Strategies provide a control on rule applications in a declarative way. With strategy combinators, the user can construct more complex strategies from simpler ones. Matching with four different kinds of variables provides a flexible mechanism of selecting (sub)terms during execution. We give an overview on programming with strategies in PRholog and demonstrate how rewriting strategies can be expressed

P-rho-Log: Combining Logic Programming with Conditional Transformation Systems

P-rho-Log extends Prolog by conditional transformations that are controlled by strategies. We give a brief overview of the tool and illustrate its capabilities