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A NEW DECISION METHOD FOR INTUITIONISTIC LOGIC BY 3-VALUED NON-DETERMINISTIC TRUTH-TABLES

Published online by Cambridge University Press:  17 December 2025

RENATO R. LEME*
Affiliation:
INSTITUTE OF PHILOSOPHY AND HUMAN SCIENCES (IFCH) AND CENTRE FOR LOGIC, EPISTEMOLOGY AND THE HISTORY OF SCIENCE (CLE) STATE UNIVERSITY OF CAMPINAS (UNICAMP) BRAZIL E-mail: coniglio@unicamp.br
MARCELO E. CONIGLIO
Affiliation:
INSTITUTE OF PHILOSOPHY AND HUMAN SCIENCES (IFCH) AND CENTRE FOR LOGIC, EPISTEMOLOGY AND THE HISTORY OF SCIENCE (CLE) STATE UNIVERSITY OF CAMPINAS (UNICAMP) BRAZIL E-mail: coniglio@unicamp.br
BRUNO LOPES
Affiliation:
COMPUTING INSTITUTE (IC) FLUMINENSE FEDERAL UNIVERSITY (UFF) BRAZIL E-mail: bruno@ic.uff.br
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Abstract

Kurt Gödel proved that it is not possible to characterize intuitionistic propositional logic (${IPL}$) by means of finite and deterministic truth-tables. After extending the same result with respect to non-deterministic matrices (Nmatrices), we provide a semantical characterization of ${IPL}$ by means of a $3$-valued Nmatrix with a restricted set of valuations. This structure allows to define an algorithm to delete unsound rows from the non-deterministic truth-tables generated for each formula, which constitutes a new and very simple decision procedure for ${IPL}$. This method can be seen as truth-tables in a broader sense, and a way to overcome Gödel’s limiting result.

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Type
Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of The Association for Symbolic Logic
Figure 0

Figure 1 Tables for $\mathcal {M}_{Ke}$.

Figure 1

Figure 2 Assume that $Val^{nd}(\mathcal {M}_{Ke}) = \{ v_0, v_1, \ldots , v_m, v_n, v_k, \ldots \}$. We remove a valuation v in level $\mathcal {L}_{n+1}$ iff there is some $\alpha $ such that $v(\alpha ) = t$ and $w(\alpha ) \in \{T,t\}$ for every $w \in \mathcal {L}_n$.

Figure 2

Figure 3 Tables for $\mathcal {M}_{S4}$.

Figure 3

Figure 4 Tables for $\mathcal {M}^{\prime }_{S4}$.

Figure 4

Figure 5 Tables for $\mathcal {M}_{IPL}$.

Figure 5

Figure 6 Algorithm for obtaining the set $iPLV(\Lambda )$.

Figure 6

Figure 7 Example of executions of the refinement algorithm. Note that each partial valuation is identified by its ID, which is an index $n \in \mathbb {N}$. The dependency tables are obtained in two steps: first, find every v such that there is some $\alpha $ such that $v(\alpha ) = \mathbf {U}$. Then, for each of these partial valuations, find every valuation w such that $w(\alpha ) = \mathbf {F}$ and, for every $\beta $, $w(\beta ) = \mathbf {T}$ whenever $v(\beta ) = \mathbf {T}$.