Schema Complexity in Propositional-Based Logics

被引:0
|
作者
Ramos, Jaime [1 ,2 ]
Rasga, Joao [1 ,2 ]
Sernadas, Cristina [1 ,2 ]
机构
[1] Univ Lisbon, Inst Super Tecn, P-1049001 Lisbon, Portugal
[2] Inst Telecomunicacoes, P-1049001 Lisbon, Portugal
关键词
schematic complexity; propositional-based schema calculus; schema derivation; schema metatheorems; PROOFS;
D O I
10.3390/math9212671
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The essential structure of derivations is used as a tool for measuring the complexity of schema consequences in propositional-based logics. Our schema derivations allow the use of schema lemmas and this is reflected on the schema complexity. In particular, the number of times a schema lemma is used in a derivation is not relevant. We also address the application of metatheorems and compare the complexity of a schema derivation after eliminating the metatheorem and before doing so. As illustrations, we consider a propositional modal logic presented by a Hilbert calculus and an intuitionist propositional logic presented by a Gentzen calculus. For the former, we discuss the use of the metatheorem of deduction and its elimination, and for the latter, we analyze the cut and its elimination. Furthermore, we capitalize on the result for the cut elimination for intuitionistic logic, to obtain a similar result for Nelson's logic via a language translation.
引用
收藏
页数:22
相关论文
共 3 条
  • [1] Complexity of the Satisfiability Problem for a Class of Propositional Schemata
    Aravantinos, Vincent
    Caferra, Ricardo
    Peltier, Nicolas
    LANGUAGE AND AUTOMATA THEORY AND APPLICATIONS, 2010, 6031 : 58 - 69
  • [2] Type-Based Complexity Analysis of Probabilistic Functional Programs
    Avanzini, Martin
    Dal Lago, Ugo
    Ghyselen, Alexis
    2019 34TH ANNUAL ACM/IEEE SYMPOSIUM ON LOGIC IN COMPUTER SCIENCE (LICS), 2019,
  • [3] On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results
    Arenas, Marcelo
    Barcelo, Pablo
    Bertossi, Leopoldo
    Monet, Mikael
    JOURNAL OF MACHINE LEARNING RESEARCH, 2023, 24