August 14, 2024

Confluence in Graph Rewriting

Posted by John Baez

guest post by Anna Matsui and Innocent Ob

In this blog post we discuss the main (‘algebraic’) ideas of term rewriting and how they can be applied to term graph and arbitrary graph (i.e. string diagrams) rewriting. Our group was interested in the application of critical pairs for string diagram rewriting. We read Detlef Plump’s Critical Pairs in Term Graph Rewriting and given the task before us wanted to understand the following question: if Plump needed a new decision procedure for critical pairs in graph rewriting, how would we know what we might need for string diagram rewriting? In the this blog post we start at the root (pun intended) and work our way up from term rewriting to rewriting on arbitrary graphs. The goal is to understand, for example, why we can’t just apply, without modification, the ideas in Plump’s paper to rewriting in the λ-calculus. This post is aimed at an audience unfamiliar with term rewriting and presents foundational ideas and results to set the stage for (old and more) recent categorical applications.

I. Preliminaries

I.a On theories, signatures, and terms

A theory can be thought as a “finite or an infinite set of formulas characterized by common grammatical rules, allowed functions and predicates, and a domain of values. Example of theories include propositional logic, equality, linear arithmetic, bit vectors. These are examples of first-order (FOL) theories. We can also have higher-order theories . First order logic (FOL) is based on the following four elements: variables, logical symbols (boolean connectives and quantifiers), non-logical symbols (functions, predicates, constants), and syntax (rules about constructing valid formulas in the logic). Typically, a theory defines restrictions on the non-logical symbols. This set of non-logical symbols is called the signature. With a signature Σ, A Σ-formula is constructed using only non-logical symbols from Σ. A first-order Σ- theory T consists of a set of Σ-sentences. In the case of rewriting systems, the only logical symbol we care about is equality. Said differently, with rewriting systems it makes sense to restrict not only the set of non-logical symbols, but also logical symbols. Such a restriction exists in the equality logic. It is the quantifier-free fragment of FOL with equality as the singular logical symbol.

Definition 1 (Signature). A signature Σ consists of non-empty set of function symbols each equipped with a fixed arity. The arity of a function symbol F is a natural number, indicating the number of arguments it is supposed to have. Nullary functions are allowed: these are called constant symbols.A term is a string of symbols from an alphabet consisting of the signature and a countably infinite set, Var, of variables. The set of terms over Σ is indicated as $\mathcal{F}$(Σ, Var). All variables and functions are terms.

I.b On abstract reduction systems

More generally, we can view term rewriting systems as instances of an abstract reduction system.

Definition 2 (Abstract Reduction System). An abstract reduction systems is a pair $(A, \rightarrow)$, where $\rightarrow$ is a binary relation on the set $A$, i.e $\rightarrow \subseteq A \times A$.

In an abstract reduction system, the relation → can represent a program evaluation (a0 → a1an) or identities (a0 ← a1⋯ → a2⋯). The former describes some directed computation from one element to another. The latter implies the reflexive transitive symmetric closure ($\xleftrightarrow{\text{\*}}$): there exists some finite path the first term to the second and vice versa. By closure we use the common description : the P closure of R is the least set with the property P which contains R. So the $\xleftrightarrow{\text{\*}}$ is the least reflexive transitive symmetric relation which contains →. Given two terms x and y, we say that X is reducible if and only if there is a y such that x → y. x is in normal form if it is not reducible. y is a normal form of x if y is in normal form and there exist some finite path from x to y, i.e $x \xrightarrow{\text{\*}} y$. x and y are joinable (x ↓ y) if there is a z such that $x \xrightarrow{\text{\*}} z \xleftarrow{\text{\*}} y$.

Definition 3 (Terminating, Confluent). *A reduction → is called:

• terminating iff there is not infinite descending chain $x_0 \rightarrow x_1 \rightarrow \cdots$

• confluent iff $x \xrightarrow{\text{\*}} z \xleftarrow{\text{\*}} y \implies x\downarrow y$*

Whether a string, term, or graph, rewriting system are mechanisms for computing stepwise transformations of objects. A term rewriting systems (TRS) is an abstract reduction system where the objects are first-order terms and where the reduction relation is presented as reduction or rewrite rules. The canonical term rewriting systems were viewed as decision procedures for proving the validity of equalities in first-order equational theories.

I.c Term rewriting and all that!

Definition 4 (Term Rewriting System). A rewrite or reduction rule is an identity $l \equiv r$ where $l$ is not a variable and all the varialbles in $r$ are also in $l$. A term rewriting system is a set of a pairs consisting of the set of rewrite rules and a $\Sigma$ definition the non-logical symbols of the grammar.

How do we know when any two arbitrary terms (from some grammar) are equivalent and why should we care if any two terms are equivalent? One approach to answer this question is start from both terms and search exhaustively search until we find term in a set of finite paths that ’generalizes’ the two terms - a process akin to anti-unification. The other would be to use a set of rules to reduce both terms to their normal forms and then testing that those normal forms are joinable/equivalent - akin to unification. This is the task of confluence tests. From this description, its clear that it is difficult to prove confluence if our reductions steps never terminate. Therefore its intuitive that termination is necessary to prove confluence. This is in essence what we learn about term rewriting systems: confluence is undecidable for non-terminating term rewrite systems. However we find that in term graph rewriting, it is possible to take a non-terminating term rewriting system and transform it into a terminating term-graph rewrite system! For now, before we return to our discussion on confluence, let’s introduce term rewriting and term graph rewriting more formally using the universal algebra we presented above.

II. Context, overlaps, and critical pairs

In term rewriting, a position in a term $t$ detemines both a prefix of $t$, i.e holes and the occurrence of the subterm with the prefix. The prefix of t that corresponds to p is denoted by $t[ ]_p$, the subterm occurring at position $p$ by $t|_p$. When rewrites occur they occur in some context.

$\begin{array}[t]{ccc} t = f_{1}(g_{2}(a_{3})) & t[]_2=f_{1}(\Box(a_{3})) & t|_{2} = g \\ \end{array}$

Definition 5 (Context). A context is a term containing zero, one or more occurrences of a special constant symbols $\Box$ denoting holes, a term over the extended signature $\Sigma \cup \lbrace \Box \rbrace$. If $C$ is a context containing exactly n holes, and $t_1,\cdots,t_n$ are terms, then $C[t_1,\cdots,t_n ]$ denotes the result of replacing the holes of $C$ from left to right by $t_1,\cdots,t_n$ . To distinguish between difference occurrence of a sub term in a term we adopt a definition of occurrence. We define an occurrence as the ordered pair $\langle s | C[] \rangle$. This uniquely determines the occurrence of $s$ in $C[s]$ with the one-hole context $C[]$. That is every occurrence of the sub-term can be replaced by a one-hold context. For a given term $s$ this new term $s^'$ with holes can be called the prefix of $s$.

Definition 6 (Reduction Step). A $\textit{reduction step}$ according to the reduction rule $p: l \rightarrow r$ consists of contracting a redex within an arbitrary context:

(1)$C[l^\sigma] \xrightarrow[p]{} C[r^\sigma ]$

Given a reduction rule p, an instance of p is obtained by applying a substitution σ. The result is an atomic reduction step $l^{\sigma} \xrightarrow{p} r^{\sigma}$. The left-hand side lσ is called the redex and the right-hand side rσ is called its embedding. A term may contain one or more occurrences of a redex. A reduction step consists of contracting a redex within an arbitrary context.

Example 1. Given rule $p : F(G(x),y) \rightarrow F(x, x)$ and substitution $\sigma : \lbrace x \rightarrow 0, y \rightarrow G(x) \rbrace$, we get the following reduction step:

(2)$F(G(0), G(x)) \xrightarrow[p]{} F(0,0)$

with redex $F(G(0), G(x))$, and embedding $F(0,0)$. With a term $F(z, G(F(G(0), (G(x)))))$ and the contraction describe above, we get the following reduction step

(3)$F(z, G(F(G(0), (G(x))))) \xrightarrow[p]{} F(z,G(F(0,0)))$

where the context is $F(z, G(\Box))$.

The contraction of redex can lead to some interesting and uninteresting conflicts. Non-confluence can usually be attributed to the specific case of conflicts : the overlapping of reductions. Remember that redexes depends on some rewriting rule and some substitution of terms. In order to understand if a TRS is confluent it is important to know when such overlaps are harmful or harmless. In general, a redex is harmful it is overlaps. The following example will illustrate this case:

Example 2. Consider the TRS with the following rules:

(4)$p_1 : F(G(x),y) \rightarrow x$
(5)$p_1 : G(a) \rightarrow b$

The term $F(G(a), x)$ can be reduced in two ways: $F(G(a),x) \xrightarrow[p]{} a$ and $F(G(a),x) \xrightarrow[p]{} F(b,x)$, where terms $a$ and $F(b,x)$ are normal forms. The following TRS is not confluent as $a$ and $F(G(b,x))$ have no common reduct. The reductions rules and substitutions give use the two redexes: $F(G(a),x)$ and $G(a)$. Both redexes share the function symbol $G$ in both. This sharing means that a contraction using the second rule $\textit{destroys}$ $G$ thus making it impossible to apply $p_1$. Each of the left-hand side of the rules on matches (redex) expresses in many ways the $\textit{context}$ of the rewriting. This say that the TRS does not support rewriting in arbitrary contexts in a way that preserves confluence. We need to find the redexes that cause these conflicts and use them to generate additional rules that allow us to reach a common reduction. This is known as completion or is the more operational contexts $\textit{saturation}$.

Again overlap result when symbols are shared between any two redexes. We do this by defining a $\textit{pattern}$ of a redex as the fixed part of the left-hand side. The result is a $\textif{prefix}$ of the redex where all its variables are left out, i,e a potential many-hole context. It is the context that is created by replacing all variables in the redex with holes. So to be more precise, two redexs overlap when their patterns share a symbol occurrence.

II.a Redex considered harmful?

However, it is not the case that all overlaps of redexes are harmful. Some overlaps may occur after one-step reductions, but may be unified after a finite number of steps. This is the difference between confluence and local confluence. Another way to say this is that local confluence can also be a sufficient criterion for confluence of a term rewriting system (TRS). This allow us to understand confluence of a TRS by thinking of how overlapping redexes over a number of steps/paths are contracted. From the definition described above this requires considering how holes in context can be substituted to preserve the pattern between two redexes. But there are potentially infinitely many possible substitutions and infinitely many cases of redex overlaps accordingly. Operationally, we need an efficient method of proving confluence that doesn’t require searching over the entire space of substitutions. The idea here is to associate each pair of overlapping redex rules with a more general cases of an overlap from which other cases can be generated. In literature this is exactly where critical pairs play a role.

The key idea behind critical pairs is the following: all overlapping redexes can be treated as substitution instances of some general pattern. Using our definition of confluence given in Section V on abstract rewriting systems: a TRS is confluent if all of its critical pairs are joinable.

Definition 7 (Critical Pair). Let $p_i: l_i\rightarrow r_i$, $i = 1,2,$ be two rules whose variables have been renamed such that $\mathcal{V}ar(l_1,r_1) \cap \mathcal{V}ar(l_2,r_2) = \emptyset$. A pair of one-step reductions $\langle \theta r_{1}, \theta l_{1}[\theta r_2]_p \rangle$ is a critical pair if

1. Let $p \in \mathcal{P}os(l_1)$ such that $l_{1}|_{p}$ is not a variable

2. $\theta$ be an maximum unifier of $l_{1}|_{p} =^{?} l_{2}$.

This is a definition and constraint will come up again in confluence decision procedures in term graph rewriting. But before we get into the similarities let stalk a little but about the different between trees and graphs as applied to terms rewriting.

II.b Structure and Contents (from Trees to Graphs)

Figure 1:

The structure of a term can be represented by visualizing it as a tree where function symbols are nodes are arrows point to arguments of the function. There is a function node with no arrows point to it: we call this node the root node. Except these are not your simple trees. We require trees with a labeling and numbering on nodes. The numbering give is a terms position in the tree. Formally, the position is a string over the alphabet of positive integers where the root position (the root node) is the empty string. Labelling is necessary to distinguish between similar (isomorphic) trees. This is all to say is that our classical tree is just a labelled rooted undirected graph. The tree as a data structure captures the content of a term in node labels. Instead of labelling the arrows between nodes, our labeling rests with the node. What this does is to leave all information about the content and structure of terms in the nodes. We can distinguish the two trees in Figure 2b because testing for equivalence is based on both its position and node label. However, computationally this is an expensive representation of terms. In Figure 2a we have a situation where we have a duplicated term. It would be more efficient to just share references to this term rather than duplicating it. But this simple approach would be problematic in this model. This is because our node label contains information on both the content and structure of the terms. So even if we see the term b at multiple levels in our tree, they are indeed not equivalent. This is because our test of equivalence would have to take into account both the position and the node label. In order to actually share references, we need to decouple structure from content. We need to move information about the position to the graph level. In this approach, our position or numbering of nodes would now be attached to the arrows or edges. We would also need to require that all isomorphic subtrees be replaced by only ‘one’ amounting to an equivalence class of nodes each considered as a node. This process would turn our previously rooted undirected graph into a directed acyclic graph. One of the really interesting things about moving from the former to a DAG is that what were once previously non-terminating TRS can now become terminating. This has noting to do with our signature and everything to do with the choice of a DAG.

In the next section we discuss the paper by Detlef Plump called Critical pairs in term graph rewriting. The goal of this paper is to understand how much of the work describe above for critical pairs that is used in term rewriting (with trees) can apply to rewriting on graphs. Before we move on let just review some keys points.

1. we are still working with first-order terms in a qualifier-free fragment of first-order logic: equality logic

2. We’ve moved from (simple trees) to graphs: Our signature has not changed just the data model used to represent terms

3. Our use of a DAG allows the use of references to used instead of duplicated nodes. Although, we will need to generalize our graph by removing the restrict that each edge have only two vertices (endpoints). That is we need to move from just the generic graph to the hypergraph.

III. Algebraic graph transformation and Plump’s (strong) joinability

There are two way of approaching graph rewriting. In many ways they mirror term rewriting (matching, deleting, copying). However as we discussed above the context is different. Our context is not a strings with a holes but graphs with holes. This can be best visualized in the algebraic approach to graph transformation via pushouts. Pushouts are to graphs what syntactic unification and contraction are to term rewriting (on trees). We will define the pushout approach in a bit but first a bit about hypergraphs. In the previous section, we highlighted how we can move from trees to graphs (DAGs). For term graph rewriting, we will generalize graphs even further and use the notion of a hypergraph. In a hypergraph unlike simple graphs, edge (called hyperedges) can have multiple targets. The formal definition is as follows:

Definition 8 (Hypergraph). A hypergraph G over Σ, X is a system G = (VG, EG, sG, tG, lG) where VG, EG are finite sets of nodes (vertices) and hyperedges, sG : EG → VG, tG : EG → VG* are mappings that assign a source and a list of target nodes to each hyperedge, and lG : EG → Σ ∪ X is a labelling map such that arity (lG(e)) = length(tG(e)).

This again is a choice of implementation. With a hypergraph, we drop the restriction that edges must have only two end points. To capture this structure, our morphisms are also a bit different. Instead of relation on terms in a tree, our morphisms are between graphs.

Definition 9 (Graph Morphism). A graph morphism f : G → H between graphs G and H consists of two functions fV : VG → VH and fE : EG → EH that preserve the labels and targets of nodes, lH ∘ fE = lG and tH ∘ fE = fV ∘ tG. If this f is injective and surjective, then it is an isomorphism and G and H are isomorphic.

In the algebraic/categorical approach, working with isomorphism classes of term graphs would cause us to lose access to nodes and edges. Instead, Plump uses standard term graphs which represent their isomorphism classes of of those node. In order to do this we introduce the notion of access path. An access path for a node v in term graph G is a possible empty sequence of labels representing a path from the root to v. For example, the access path ⟨i1, i2, ⋅in⟩ indicates that following path exists ⟨v0i1, v1, i2, v2, ⋅in, v⟩ where v0 = root and vn = v. The set of all access paths is denoted Acc(v). For every term graph we can construct an isomorphic standard term graph by replacing each node v with Acc(v) and modifying the edge set and the labeling and target functions correspondingly. We will call a graph H a collapsed version of G, if there is a surjective graph morphism f : G → H such that fv(rootG) = rootH. This is usually denoted ≥ and the inverse (copying) is denoted as ≤. A term graph is a tree if there is only a unique path from the root to each other node and it is fully collapsed if and only if for all nodes v and w, equality of the terms representing nodesv and w implies that node v and w are equal.

Using the definitions of term rewriting given, we will now define term graph rewriting.

III.a Instance, redex, and context

Definition 10 ((Hyper-)graph Rewriting System). A hypergraph rewriting system Σ, ℛ⟩ consists of a signature Σ and a set of ℛ of rules over Σ.

When proving confluence in hypergraphs, the goal is to find isomorphic graphs. It is a view of confluence that considers hypergraphs transformation “up to isomorphism”.

Lemma 1. A hypergraph transformation systems ⟨Σ, ℛ⟩ is confluence (locally confluent) if and only if the relation ⇒ on isomorphism classes of hypergraphs over Σ is confluent (locally confluent). Additionally the system ⟨Σ, ℛ⟩ is terminating if ⇒ is terminating.

Using the same language we used for term rewriting, given a node v in the term graph G and a term rewrite rule l → r, the pair ⟨v,,l → r⟩ is our redex if the term subgraph at v in G is an instance of l. While term rewriting systems have reduction rules, in term graph rewriting we have similar ideas except in a new context. As previously mentioned, in a graph system a rewrite rule is morphism between (hyper-)graphs. These morphisms are mappings between the sets of nodes and edges that they preserve sources, targets, and labels. Similarly in term rewriting systems, we have reductions steps. In term graph rewriting we have the corresponding graph transformations - evaluations steps. Evaluation steps create derivations. When we apply a rewrite rule to a graph, the core idea is to identify the subgraph corresponding to the left-hand side of the rewrite, “cut it out”, replace it with the right-hand side graph, and then “glue” it back. This is the ideas behind the double pushout.

III.c The double pushout and duality of labeling and tracing

The double pushout (DPO) might be hard to follow at first. As an aid, we’ve include in Fig.3 a visualize of the DPO approach that capture the rewrite steps in Fig. 4. (Also, Fig.4 represents the push described in Plump’s paper!). In the double pushout approach, our rewriting rule p is a span $\lozenge l \xleftarrow{l} \mathcal{D} \xrightarrow{r} \lozenge r$ of morphisms that start from gluing object 𝒟 and ends in the left-hand side ◊l and right-hand side ◊r. Our redex for the rule p is a morphism g : ◊l → 𝒢 for the left-hand into an intermediate graph 𝒢. The pushout only exists if there is a host object 𝒢1 with a morphism q from our interface 𝒟 that allows us to recover the match and also embed our intermediate graph 𝒢. The match reconstructed from two morphisms (q and l) is called the pushout complement of (l and g). One way to think of this push out is once we’ve found a graph (𝒢) that matched our left-hand side, this (sub)graph that is created by removing the nodes and edges that match except for those ’marked’ by the gluing object (our interface). If we for some reason remove these nodes then it is impossible to complete the pushout. There are two conditions the double pushout requires: the identification and dangling condition. Essentially, these two boil down to saying that the pushout complement must contain nodes in the image of g and l. Figures 3 and 4 show examples of a double pushout diagram. In Figure 3, the rewriting step that is able to complete the double pushout is called a direct derivation.

Definition 11 (Direct Derivation). Let G and H be hypergraphs, $r: \langle L \hookleftarrow K \xrightarrow{\text{b}} R\rangle$ a rule and f : L ↪ G an injective hypergraph morphism. The G directly derive H by r and f, denoted Gr, fH, if there exisit two pushouts of the following form.

For a rule $p : \lozenge l \xleftarrow{l} \mathcal{D} \xrightarrow{r} \lozenge r$, a direct derivation can be written as $\mathcal{G} \xRightarrow{p} \mathcal{H}$. What we call here a direct derivation is equivalent to Plump’s evaluation step . Plump discusses two other types of operations: folding and tracing. The goal of the former is to increase the level of sharing between edges that share the same labels and terms. The latter, tracing, is a bit more involved.

III.d Tracing semantics

In term rewriting and term graph rewriting, labeling allows us to “lift” our rewrite rules into a context where each reduction step can now track symbols and their occurrences through multiple derivations. This labeling can be extended to arbitrary rewrite systems with the condition that restrictions must to placed on the labelling to ensure that identical arguments of a redex are labelled in the same way. Tracing is the dual of labeling. While labeling records the history of a reduction internally, tracing or tracking does so externally. A trace is a relation on the positions of the source and target of a rewrite rule. The idea is that as we step through a series of derivations (apply rewrite rules) we keep track of the terms of the left-hand and right-hand side. When reasoning over the label via trace there are three parts we care about as we step through the trace: terms belonging to the pattern of the contracted redex, terms disjoint with the redex, and terms that are arguments to the redex.

We capture the following example of tracing and labeling from as a pushout diagram in Fig.5 . In Fig. 5, we consider the rewrite rule f(a, x) → f(f(x, x), a) and the reduction step f(f(a, a), a) → f(f(f(a, a), a), a). Our goal is to trace the thee a’s that show up in original term - the left-hand side of the reduction step. In the application graph (bottom-left) of the pushout diagram, we label each a with a different letter (to represent out coloring). Fig.5 show this result of this reduction and lifting of rewrite rules: $f(f(a_{\color{blue}{b}},a_{\color{red}{r}}),a_{\color{green}{g}}) \rightarrow f(f(f(a_{\color{red}{r}},a_{\color{red}{r}}),a),a_{\color{green}{g}})$. We can observe the following in our trace: (1) $a_{\color{blue}{b}}$ belong to the contracted redex (top-left) but has no descendents, (2) $a_{\color{green}{g}}$ is disjoint eith the redex and is copied one, and (3) $a_{\color{red}{r}}$ is the redex argument (hole) and is copied twice. Additionally, there is one more $a$ with no label. It sourced from the original term, but from the pattern in the embedding. It has no ancestors. The occurrences of a term traced through derivations by a label are the descendants and the original term is the ancestor.

Definition 12 (Descendant and Ancestor ). Let t be a term in a TRS ℛ and let f be a function symbol occurring in t. We give f a mark or label to be able to trace it during a sequence of rewrites. For a unique label b, the labeled symbol f can be written as fb. Consider the rewrite step $t \xrightarrow{s} t'$ obtained by the contraction of the redex s in t. As a result of the contraction, all occurrences of f in t′ which are labelled b are the descendents of the original f in t. Given the one-to-one correspondence between sub-terms and their head symbols, descendants of redex can be found by tracing head symbols.

In regards to rewriting, symbols can be partitioned into two classes: those that trace to its target and does that dont. The symbols that leave a trace are needed. The symbols which are needed then in essence become the context for further reductions. Tracing provides verification that positions in the target are originated with the source or are created dynamically by some rule applications. Tracing is necessary for the double pushout, particularly in the creation of the pushout complement. Given this labeling-tracing duality, asking how it can be useful in proving confluence of graph rewriting systems is somewhat equivalent to demonstrating that the dangling condition and the identification condition are necessary in completing the pushout.

III.d Overlaps in the context and critical pairs

As we mentioned previously, overlaps occur. In the term rewriting context, to ensure local confluence we needed to show that through a finite number of derivations, our sub-graphs with conflicting reductions have a common reduct. In the graph rewriting context we have similar questions: (1) what is a “harmful” overlap? and (2) what is a sufficient criteria for proving joinability?

Definition 13 (Critical Pair). Let S be a term graph and $S \rightarrow U_i$ be an evaluation step for a rewrite rule $l_i \rightarrow r_i$ and a hypergraph morphism $g_i: \lozenge l_i \rightarrow S$ for i = 1, 2. Then $U_1 \leftarrow S \rightarrow U_2$ is a critical pair if: - $S = g_1(\lozenge l_1) U g_2(\lozenge l_2)$ - $g_1(e_1) \in g_2(\lozenge l_2)$ or $g_2(e_2) \in g_1(\lozenge l_1)$, where $e_i$ is the edge outgoing from $root_{\lozenge l_i}$ for i = 1, 2

Moreover, $g_1 \neq g_2$ is required for the case $(l_1 \rightarrow r_1) = (l_2 \rightarrow r_2)$.

From the Critical Pair Lemma discussed in the Plump paper , we know that a hypergraph rewriting system is locally confluent if all its critical pairs are strongly joinable. So in addition to the decision procedure for critical pairs, we require an additional qualification for term graph rewriting called strong joinability.

Definition 14 (Joinabiility). A critical pair, $\Gamma: U_1 \Leftarrow S \Rightarrow U_2$ is $\textit{joinable}$ if there are derivations $U_i \Rightarrow^{*} X_i$ for i = 1, 2, and an isomorphism $f: X_1 \rightarrow X_2$. Additionally $\Gamma$ is strongly joinable if there exists for every node ins $S$ that can can be traced through derivations the following two condition:

• trS ⇒ U1*X1(v) and trS ⇒ U2*X2(v)

• fV(trS ⇒ U1*X1(v)) = trS ⇒ U2*X2(v)

So there you have it! As long as our definition of joinabilty ensures traces are consistent and compatible through derivations, then for a terminating term graph rewriting system, strong joinability is sufficient for (local) confluence. So we are settled, right? Maybe for the case of term graphs, but for arbitrary graphs, confluence is, unfortunately, undecidable.

Theorem 1. It is undecidable in general whether a finite, terminating hypergraph rewriting system is confluent. A hypergraph rewriting system is said to be finite if ΣV, ΣE and R are finite.

We discuss why below.

IV. The insufficiency of strong joinability

Figure 7

To explain why, we will borrow an instructive example from [5]. In Fig.7 we consider an graph transformation system {Σ, R} with three rewrite rules: r1, r2 and r3. Some inspection will show that this system is terminating: the right-hand side removes edges from the left. We can also show that it is confluent as there are derivations which will reduce any overlaps to a common reduct (or specifically structurally isomorphic graphs). That is, any overlap is joinable. However despite this confluence, the two critical pairs in Figure 7 are not strong joinable. In the first,cp1, applying r1 give us two graphs in normal form for which the ismorphism between them is not compatible with their traces. By labeling the nodes, we have distinguish one isomorphic graphs. Strong joinability require compatibility with traces. As a result, the presense of critical pairs that are joinable implies that we can report confluence, but the lack of strong joinability for at least one pair implies that we cannot report confluence. Which is it? It seems like neither is sufficient. In the previous section, we mentioned that our signature has not change. Well, what happens if it does? To keep thing minimally destructive, lets still remain in the domain of first-order terms. Lets add edge labels. As you may surmise, our “tracing” now have over nodes and edges. In Figure 7, cp2 is a critical that results from rewriting a term with an edge label. In this case our critcal pair is still joinable, but we lose confluence because our definition of critical pairs did not account for compatibility with edge labels. The core idea highlighted by Plump is that relatively harmless signature extensions may not preserve confluence. We may need an even stronger joinability that capture compatibility between node and edge labels across derivations. This means that adding signture extensions that are more “destructive” will likely break our current decision procedure for confluence. Specifically, there are two cases where our current decision procedure for confluence on term graphs breaks down: variable binding and higher-order logics. The former is related to the question posed in the Sec.1: Why can’t we just apply the ideas from to rewriting the untyped λ-calculus?

V. Critical pairs for arbitrary graphs

Variable binding and higher-order rewriting. First, let’s revisit the basic term writing system {Σ, ℛ} but with a tiny signature extension: (t1 t2) and (λx.t). The first is application and represents the application of a function t1 to an argument t2. The second is abstraction and represent a function with a parameter x in the body of t. In the lambda calculus, a variable s in a term t is bound by the first λ above s. If there is no λx above then the term is free. For the following term t := λx.y the clearest way to represent this as a a term graph is a two-node graph with the root labeled λx and the child node labeled y. It is clear that the notion of variable binding is not reflect in the structure of this graph. Instead it is represented as the content. What has happened is that that simple signature extension is not so simple. In fact, binding requires higher-order term rewriting which require expanding and rethinking many of the formulation for first-order rewriting. Here are some resource on higher-order equational logics and rewriting: .

double pushout with interfaces (DPOI). Okay I know we said that confluence for finite, terminating hypergraph rewrite systems was undecidable, but there may be a way at least for string diagrams. The work of double pushout with interfaces (DPOI) introduces a new notion of path joinability that relies on lifting critical pairs into arbitrary contexts. This work links hypergraphs and symmetric monoidal categories: the former is a combinatorial representation of the latter. This work says that we we can show that string diagram rewriting, represented as hypergraphs with interfaces, is decidable! Our colleague will discuss this a bit in their blog post, but our one little bit is that DPOI seems like a double pushout with a single pullback. To tease, that work, similar to theories and signatures for terms in our universal algebra, the structure of a symmetric monoidal category can be represented with the notion of a symmetric monoidal theory.

Definition 15 (One-sorted Symmetric Monoidal Theory (SMT) (See [2])). A one-sorted SMT is determined by (Σ, ℰ) where Σ, the signature, consists of a set of generators o : n → m with arity n and coarity m where m, n ∈ ℕ. The set of Σ-terms is obtained by combining generators in Σ, the unit id : 1 → 1, and the symmetry σ1, 1 : 3 → 2 with sequencing (;) and parallel composition (⊕). Any SMT (Σ, E) freely generates a product and permutation category (PROP). For an arbitary PROP X, a rewriting rule is a pair $langle l, r\range$ where l, r : i → j are arrows in X with the same sourcre and target. A rewriting systems ℛ is a set of rewriting rules.

VI. Conclusion

In term and string rewriting, confluence is decidable for terminating systems requiring finding all critical terms s and and checking that all critical pairs t and u in t ← s → u reduce to a common term or string. In the case of terminating graph-transformation systems, confluence is undecidable (using critical pairs as a DP) unless certain restrictions are applied. In this setting, join-ability of critical pairs does not need to imply the confluence of a rewriting systems. That is, there are confluent rewriting systems where are critical pairs are not (strongly) joinable. (Strong) joinability is not a necessary condition for confluence of a rewriting system and can be used a a decision procedure for general (hyper-)graph rewriting systems. This result does not appear to apply to string diagram rewriting if we treat them as symmetric monoidial categories with some extra structure. In the category of symmetric monoidal categories with a Frobenius structure, confluence of rewriting systems based on the theory is decidable.

Reference

[1]: Franz Baader and Tobias Nipkow. Term rewriting and all that. 1998

[2]: Filippo Bonchi, Fabio Gadducci, Aleks Kissinger, Pawel Sobocinski, and Fabio Zanasi. Rewriting modulo symmetric monoidal structure. 2016 31st Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), pages 1–10, 2016

[3]: Jan Willem Klop Marc Bezem and Roel de Vrijer. Term rewriting systems. Cambridge Tracts in Theoretical Computer Science Cambridge University Press, 55, 2003

[4]: Detlef Plump. Critical pairs in term graph rewriting. In International Symposium on Mathematical Foundations of Computer Science, 1994.

[5]: Detlef Plump. Checking graph-transformation systems for confluence. Electron. Commun. Eur. Assoc. Softw. Sci. Technol., 26, 2010

[6]: Kroening O. Strichman. Decision procedures: An algorithmic point of view. 2008.

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