Rewriting techniques constitute a foundational theory of computing science. They are being investigated for several structures, such as lambda-terms, strings, first-order terms or graphs, and have been successfully used in many areas such as programming languages, automated reasoning, program verification, security, etc. The growing interest in this research area is witnessed by the leading international events, such as ICGT (International Conference on Graph Transformation) and the recent FSCD conference (International Conference on Formal Structures for Computation and Deduction), which gathers all topics of the former international conferences RTA (Rewriting Techniques and Applications) and TLCA (Typed Lambda Calculi and Applications). During the last decade, a particular interest has been devoted to the study of the impact of shared structures in term and graph rewriting through the international workshops editions of TERMGRAPH and GCM (Graph Computation Models).

In this article, we generalize Courcelle's recognizable graph languages and results on monadic second-order logic to more general structures.

First, we give a category-theoretical characterization of recognizability. A recognizable subset of arrows in a category is defined via a functor into the category of relations on finite sets. This can be seen as a straightforward generalization of finite automata. We show that our notion corresponds to recognizable graph languages if we apply the theory to the category of cospans of graphs.

In the second part of the paper, we introduce a simple logic that allows to quantify over the subobjects of a categorical object. Again, we show that, for the category of graphs, this logic is equally expressive as monadic second-order graph logic (msogl). Furthermore, we show that in the more general setting of hereditary pushout categories, a class of categories closely related to adhesive categories, we can recover Courcelle's result that every msogl-expressible property is recognizable. This is done by giving an inductive translation of formulas of our logic into automaton functors.

This article explores methods to provide computer support for reasoning about graph transformations. We first define a general framework for representing graphs, graph morphisms and single graph rewriting steps. This setup allows for interactively reasoning about graph transformations. In order to achieve a higher degree of automation, we identify fragments of the graph description language in which we can reduce reasoning about global graph properties to reasoning about local properties, involving only a bounded number of nodes, which can be decided by Boolean satisfiability solving or even by deterministic computation of low complexity.

Term graph rewriting provides a formalism for implementing term rewriting in an efficient manner by emulating duplication via sharing. Infinitary term rewriting has been introduced to study infinite term reduction sequences. Such infinite reductions can be used to model non-strict evaluation. In this paper, we unify term graph rewriting and infinitary term rewriting thereby addressing both components of lazy evaluation: non-strictness and sharing. In contrast to previous attempts to formalise infinitary term graph rewriting, our approach is based on a simple and natural generalisation of the modes of convergence of infinitary term rewriting. We show that this new approach is better suited for infinitary term graph rewriting as it is simpler and more general. The latter is demonstrated by the fact that our notions of convergence give rise to two independent canonical and exhaustive constructions of infinite term graphs from finite term graphs via metric and ideal completion. In addition, we show that our notions of convergence on term graphs are sound w.r.t. the ones employed in infinitary term rewriting in the sense that convergence is preserved by unravelling term graphs to terms. Moreover, the resulting infinitary term graph calculi provide a unified framework for both infinitary term rewriting and term graph rewriting, which makes it possible to study the correspondences between these two worlds more closely.

We consider term rewriting under sharing in the form of compression by singleton tree grammars (STG), which is more general than the term dags. Algorithms for the subtasks of rewriting are analysed: finding a redex for rewriting by locating a position for a match, performing a rewrite step by constructing the compressed result and executing a sequence of rewrite steps. The first main result is that locating a match of a linear term s in another term t can be performed in polynomial time if s, t are both STG-compressed. This generalizes results on matching of STG-compressed terms, matching of straight-line-program-compressed strings with character-variables, where every variable occurs at most once, and on fully compressed matching of strings. Also, for the case where s is directed-acyclic-graph (DAG)-compressed, it is shown that submatching can be performed in polynomial time. The general case of compressed submatching can be computed in non-deterministic polynomial time, and an algorithm is described that may be exponential in the worst case, its complexity is nO(k), where k is the number of variables with double occurrences in s and n is the size of the input. The second main result is that in case there is an oracle for the redex position, a sequence of m parallel or single-step rewriting steps under STG-compression can be performed in polynomial time. This generalizes results on DAG-compressed rewriting sequences. Combining these results implies that for an STG-compressed term rewrite system with left-linear rules, m parallel or single-step term rewrite steps can be performed in polynomial time in the input size n and m.

A very large amount of work in Natural Language Processing (NLP) use tree structure as the first class citizen mathematical structures to represent linguistic structures, such as parsed sentences or feature structures. However, some linguistic phenomena do not cope properly with trees; for instance, in the sentence ‘Max decides to leave,’ ‘Max’ is the subject of the both predicates ‘to_decide’ and ‘to_leave’. Tree-based linguistic formalisms generally use some encoding to manage sentences like the previous example. In former papers (Bonfante et al. 2011; Guillaume and Perrier 2012), we discussed the interest to use graphs rather than trees to deal with linguistic structures, and we have shown how Graph Rewriting could be used for their processing, for instance in the transformation of the sentence syntax into its semantics. Our experiments have shown that Graph Rewriting applications to NLP do not require the full computational power of the general Graph Rewriting setting. The most important observation is that all graph vertices in the final structures are in some sense ‘predictable’ from the input data, and so we can consider the framework of Non-size increasing Graph Rewriting. In our previous papers, we have formally described the Graph Rewriting calculus we used and our purpose here is to study the theoretical aspect of termination with respect to this calculus. Given that termination is undecidable in general, we define termination criterions based on weight, we prove the termination of weighted rewriting systems, and we give complexity bounds on derivation lengths for these rewriting systems.

Rewriting notions like termination, normal forms and confluence can be described in an abstract way referring to rewriting only as a binary relation. Several theorems on rewriting, like Newman's lemma, can be proved in this abstract setting. For investigating possible generalizations of such theorems, it is fruitful to have counterexamples showing that particular generalizations do not hold. In this paper, we develop a technique to find such counterexamples fully automatically, and we describe our tool Carpa that follows this technique. The basic idea is to fix the number of objects of the abstract rewrite system, and to express the conditions and the negation of the conclusion in a satisfiability (SAT) formula, and then call a current SAT solver. In case the formula turns out to be satisfiable, the resulting satisfying assignment yields a counterexample to the encoded property. We give several examples of finite abstract rewrite systems having remarkable properties that are found in this way fully automatically.