arXiv subjects

Takayoshi Shoudai

Publications and source records attributed to Takayoshi Shoudai.

3 recordsLinked to original sources

arXiv2026

Efficient Pattern Matching for Unordered Term Tree Patterns under Generalized Height-Constrained Bindings

Unordered trees are useful for modeling hierarchical structures in which the order among siblings is irrelevant. To represent flexible structural patterns in such data, unordered term tree patterns with height-constrained variables provide a natural framework. In our previous work, we studied the pattern matching problem for rooted unordered term tree patterns with height-constrained variables under the restriction that the child port of each variable must correspond to a leaf of a binding tree. In this paper, we remove this restriction and generalize the binding model so that the child port may correspond to any non-root vertex of a binding tree. Under generalized bindings, we formulate the corresponding membership problem and present a polynomial-time pattern matching algorithm. We also implement the proposed algorithm and conduct computational experiments to evaluate its running time. The experimental results show that the proposed method achieves practical running times.

cs.DS↗

arXiv2026

Efficient Pattern Matching in Unordered Term Tree Patterns with Height Constraints

Unordered trees appear in applications where the order among child vertices is insignificant, such as abstract syntax trees and chemical structures. To describe patterns in such trees, we propose unordered term tree patterns, which employ height-constrained variables that restrict trunk length and subtree height. We formalize the pattern matching problem between an unordered term tree pattern and an unordered tree, and present an $O(N \cdot \max\{nD^{3/2}, \mathcal{S}\})$-time algorithm, where $n$ and $N$ are the numbers of vertices in the pattern and tree, $D$ is the maximum vertex degree, and $\mathcal{S}$ is the sum of trunk constraints. Computational results show that the algorithm runs efficiently in practice.

cs.DS↗

arXiv2026

Distributional Learning of Graph Languages Generated by Fixed-Interface Clause Systems

Distributional learning provides a useful framework for studying the learnability of structured languages from positive data. In this paper, we extend this framework to graph languages generated by fixed-interface clause systems (FICSs). We formulate FICSs explicitly and study the corresponding learning problem under positive presentations and membership queries. We consider a bounded class of graph languages satisfying the finite context property (FCP) under a bounded-degree assumption. The bounds are expressed by the degree bound $Δ$ together with five structural parameters $m,s,t,w$, and $d$, which control the clause-system structure, interface ranks, and local head-frame complexity. The learning algorithm constructs hypotheses from ordered boundary representations induced by the observed positive examples. These representations make explicit the interface information needed to compare contexts and to test candidate clauses by membership queries. We prove that target contexts eventually appear in the observed sample, target clauses are reconstructed over the corresponding predicate representatives, and spurious non-fact clauses are eventually excluded. Consequently, for every fixed parameter tuple, the target language is identifiable in the limit from positive data and membership queries. We also prove that the learner has polynomial-time update on $\FICSLFCP_Δ(m,s,t,w,d)$: at each stage, only polynomially many ordered boundary representations, predicate symbols, clause candidates, and membership queries are needed. Overall, the paper gives a parameterized reformulation of distributional learning for interface-based graph languages in a fixed-interface setting.

cs.FL↗