Reflective Sequential ASMs (rsASMs)

• rsASMs are a formal model of sequential algorithms that incorporate self-inspection and dynamic rule evolution via self-encoded syntax trees.
• They utilize a tree algebra for explicit state representation, allowing the active rule and signature to be inspected and modified during computation.
• The framework ensures bounded exploration and behavioral equivalence, rigorously simulating reflective sequential algorithms step-by-step.

Reflective Sequential Abstract State Machines (rsASMs) formalize the concept of sequential algorithms capable of modifying their own behavior, capturing the essence of linguistic reflection at the level of algorithmic (not merely data) evolution. By extending Abstract State Machines (ASMs) to include updatable syntactic representations of the rule currently governing the computation, rsASMs provide the first language-independent, mathematically precise account of reflective sequential algorithms (RSAs). Their theoretical framework is based on language-agnostic postulates, a sophisticated tree algebra for self-encoding, and a behavioral equivalence to all RSAs in the sense of bounded exploration and abstract-state machine theory (Schewe et al., 2020, Ferrarotti et al., 2017).

1. Foundational Postulates for Reflective Sequential Algorithms

Reflective sequential algorithms (RSAs) generalize Gurevich's sequential algorithm model to support self-inspection and self-modification. Four postulates form the foundation of the behavioral theory for such algorithms:

1. Sequential Time: An RSA $\mathcal{A}$ consists of a nonempty set $\mathcal{S}_\mathcal{A}$ of extended states, a distinguished subset of initial states $\mathcal{I}_\mathcal{A}$, and a (partial) transition function $$\tau : \mathcal{S}_\mathcal{A} \to \mathcal{S}_\mathcal{A}$$.
2. Abstract State: Each state $S \in \mathcal{S}_\mathcal{A}$ is a first-order structure over a (possibly growing) finite signature $\Sigma_S$. There exists a fixed subsignature $\Sigma_{\text{alg}} \subseteq \Sigma_S$ whose interpretation $A(S)$ represents the instantaneously active algorithm. Initial states are isomorphism-closed, and transitions preserve the underlying domain $B$.
3. Background Infrastructure: States have access to an infinite reserve of uninterpreted values, distinguished constants (e.g., ${\sf undef}$), Booleans, tuples, naturals, as well as reflection-specific primitives:
   • $\mathsf{drop} : \Terms(\Sigma_S) \to B_\mathrm{ext}$
   • $\mathsf{raise} : B_\mathrm{ext} \to \Terms(\Sigma_S)$
   • an extraction function $\beta$ for exposing data-subterms from syntax trees.
4. Bounded Exploration: There exists a finite set $W$ of witness terms such that, for states $S, S'$ strongly coinciding on $W$, the update sets $\Delta_{\mathcal{A}(S)}(S)$ and $\Delta_{\mathcal{A}(S')}(S')$ are identical.

These postulates rigorously axiomatize the behavior of RSAs, delineating their operational boundaries and distinguishing them from conventional, non-reflective sequential algorithms.

2. State Representation and Self-Encoding in rsASMs

To realize the postulates concretely, rsASMs extend the classic ASM state with explicit self-representation. Each extended state over a signature $\Sigma \supseteq \{\mathsf{self}\}$ is a Tarski structure

$$S = (B_\mathrm{ext},\, \Sigma,\, \{f_S : B_\mathrm{ext}^{\operatorname{arity}(f)} \to B_\mathrm{ext}\}_{f\in\Sigma})$$

where:

• $B_\mathrm{ext}$ expands the data domain $B$ to include all ground terms over $\Sigma$.
• The 0-ary symbol $\mathsf{self}$ is interpreted by an unranked labeled tree over a finite alphabet $L$, encapsulating both the currently active signature and the main ASM rule to be executed.

The concrete encoding utilizes the tree algebra (see below), with

$$\mathrm{val}_S(\mathsf{self}) = \mathrm{label\_hedge}(\mathsf{self},\, \mathrm{label\_hedge}(\mathsf{signature}, \dots),\, \mathrm{label\_hedge}(\mathsf{rule},\dots))$$

This design allows the current algorithmic rule to be treated as manipulable data: rule and signature can be dynamically deconstructed, inspected, and updated during computation.

3. The Tree Algebra: Encoding and Manipulation of Rules

rsASMs incorporate a background-supplied tree algebra to enable rule and signature manipulation through finite symbolic operations. The label alphabet

$$L = \{\mathsf{self},\mathsf{signature},\mathsf{rule},\mathsf{func},\mathsf{name},\mathsf{arity},\mathsf{update},\mathsf{term},\mathsf{if},\mathsf{bool},\mathsf{par},\mathsf{let},\mathsf{partial}\}$$

underpins sorts for labeled trees ($T_L$), hedges ($H_L$), and tree-contexts ($C_L$). The main operators are:

Operator	Type	Description
$\mathrm{label\_hedge}$	$L \times H_L \to T_L$	Node with labeled children
$\mathrm{left\_extend}$, $\mathrm{right\_extend}$	$H_L \times T_L \to T_L$	Attach children
$\mathrm{concat}$	$H_L \times H_L \to H_L$	Concatenate hedges
$\mathrm{inject\_hedge}$	$C_L \times H_L \to T_L$	Fill context hole with hedge
$\mathrm{subtree}$	$\mathcal{O} \to T_L$, $$\mathrm{context} : \mathcal{O} \times \mathcal{O} \to C_L$$	Subtree/context retrieval

These operations suffice for explicit and finite manipulation of the abstract syntax trees representing ASM rules and signatures. Through these mechanisms, rsASMs maintain, traverse, and update their own execution rules, achieving linguistic reflection by symbolic tree rewriting.

4. Transition Semantics of rsASMs

At each computation step, the transition from state $S$ is executed as follows:

1. Rule Extraction: From $\mathrm{val}_S(\mathsf{self})$, extract the current signature $\sigma$ and rule $r_S$ through $\mathrm{signature}$ and $\mathrm{rule}$ selectors and the $\mathsf{raise}$ background function:

$$\sigma = \Sigma_S = \mathsf{raise}(\mathrm{signature}(\mathrm{val}_S(\mathsf{self})))$$

$$r_S = \mathsf{raise}(\mathrm{rule}(\mathrm{val}_S(\mathsf{self})))$$

1. Rule Interpretation: $r_S$ is interpreted over $\sigma$ to yield a multi-update set $\ddot{\Delta}_{r_S}(S)$, which includes both data updates and possible shared partial updates for background complex values.
2. Tree-Rule Update: In parallel, a tree-update rule $r^{\mathsf{self}}$ (constructed by a fixed, finite program from $\mathrm{val}_S(\mathsf{self})$) applies symbolic transformations to update the self-encoding to that of the successor state.
3. Transition Step: The total update set is

$$\Delta(S) = \Delta_{r_S}(S) \cup \Delta_{r^{\mathsf{self}}}(S)$$

The successor state is then $S' = S + \Delta(S)$, where $+$ denotes simultaneous update application and shared-update resolution as in classical ASMs.

The bounded exploration property is preserved: both $r_S$ and $r^{\mathsf{self}}$ depend only on a finite witness set $W$ of data and tree terms. If two states coincide on $W$, their transitions are behaviorally identical.

5. Behavioral Equivalence: The Reflective Sequential ASM Thesis

The main theoretical result, known as the Reflective Sequential ASM Thesis, asserts:

• Every reflective sequential algorithm (RSA) in the sense of the four postulates can be simulated step-by-step by a behaviorally equivalent rsASM.
• Every rsASM satisfies the structural requirements and bounded exploration property of RSAs.

The simulation construct operates as follows:

1. Bounded exploration yields a finite set of critical terms $W_S$ for the current state.
2. For each possible Boolean pattern over $W$, a local ASM rule $r_{[S_i]}$ can be computed, reproducing the update behavior.
3. States are partitioned into finitely many W-similar equivalence classes, each with its own guarded parallel rule.
4. The dynamic update of the self-encoding is realized by a tree-update rule $r^{\mathsf{self}}$, determined by $W$.
5. The overall machine executes the data- and self-update rules in parallel and applies the result, ensuring stepwise behavioral correspondence with the original RSA.

Table 1: Key Properties of RSAs and rsASMs

Property	RSAs (Axiomatized)	rsASMs (Machine Model)
State structure	FO-structure + alg	FO-structure + self-encoded rule
Time progression	Partial function $\tau$	Transition by admitted rule/tree
Rule update capability	Axiomatic (can change)	Tree algebra rewriting
Bounded exploration	Witness set $W$	Witness set $W$ (data/trees)

The thesis is formally proven for the axiomatization in (Schewe et al., 2020); (Ferrarotti et al., 2017) provides a complementary postulate set and sketch.

6. Illustrative Examples and Partial Reflection

Demonstrative constructions clarify the ability of rsASMs to encode reflective computation:

• Self-Rewriting Rule Example: Given a rule that at each step updates a function location (e.g., $f := g + a + \cdots + a$, with $i$ additional $a$s at step $i$), the ASM rule is paired with tree-updates that rebuild the concrete syntax tree representing the computation rule, effectively incrementing the number of $a$ children. This exemplifies full reflection—computation that rewrites its own code.
• Partial Reflection via Evaluation: In Abiteboul et al.'s relational reflective machines, the algorithm dynamically generates queries such as $\mathbf{eval}\ t$ (where $t$ is a first-order sentence extracted or constructed on-the-fly). Partial reflection is realized by evaluating terms as if they were part of the stored rule, a behavior that rsASMs may capture by extending the rule-language appropriately.

These examples demonstrate the practical and expressive power of rsASMs to model self-changing, rule-evolving, and even query-reflective algorithms in a systematic manner.

7. Open Issues, Limitations, and Impact

While rsASMs and their associated theoretical framework provide a robust model for reflective sequential computation, several open questions and limitations are noted:

• The proof of the simulation theorem (RSA-to-rsASM) requires addressing the full generality and technical details of the raise-evaluation mechanism and potential signature growth due to reflection.
• Ensuring closure under isomorphism is non-trivial when signatures are dynamically extended.
• Partial reflection scenarios (where only queries, not the entire rule, are changed) may necessitate extending the rsASM rule language (e.g., via explicit eval constructs).
• A plausible implication is that further formalization and mechanization would make the model more directly usable for practical verification or compilation frameworks.

The theoretical developments outlined in (Schewe et al., 2020, Ferrarotti et al., 2017) establish rsASMs as a precise, language-independent foundation for reflective sequential algorithms, matching the rigor of the classical ASM approach while extending it to encompass self-modifying computations. This framework opens avenues for deeper investigation into reflective computation, algorithmic metaprogramming, and the formal analysis of systems with dynamic control structure.