Tableaux for Public Announcement Logic

2008 ◽  
Vol 20 (1) ◽  
pp. 55-76 ◽  
Author(s):  
P. Balbiani ◽  
H. van Ditmarsch ◽  
A. Herzig ◽  
T. de Lima
Keyword(s):  
Public Announcement ◽  
Public Announcement Logic

On axiomatizations of public announcement logic

Synthese ◽  
2013 ◽  
Vol 190 (S1) ◽  
pp. 103-134 ◽  
Author(s):  
Yanjing Wang ◽  
Qinxiang Cao
Keyword(s):  
Public Announcement ◽  
Public Announcement Logic

scholarly journals Linear Temporal Public Announcement Logic: a new perspective for reasoning the knowledge of multi-classifiers

2020 ◽  
Author(s):  
Amirhoshang Hoseinpour Dehkordi ◽  
Majid Alizadeh ◽  
Ali Movaghar
Keyword(s):  
Temporal Logic ◽  
Intelligent Systems ◽  
Time Series Data ◽  
Detection System ◽  
Transition System ◽  
Series Data ◽  
Performance Improvements ◽  
Public Announcement ◽  
Public Announcement Logic ◽  
New Perspective

Current applied intelligent systems have crucial shortcomings either in reasoning the gathered knowledge, or representation of comprehensive integrated information. To address these limitations, we develop a formal transition system which is applied to the common artificial intelligence (AI) systems, to reason about the findings. The developed model was created by combining the Public Announcement Logic (PAL) and the Linear Temporal Logic (LTL), which will be done to analyze both single-framed data and the following time-series data. To do this, first, the achieved knowledge by an AI-based system (i.e., classifiers) for an individual time-framed data, will be taken, and then, it would be modeled by a PAL. This leads to developing a unified representation of knowledge, and the smoothness in the integration of the gathered and external experiences. Therefore, the model could receive the classifier's predefined -or any external- knowledge, to assemble them in a unified manner. Alongside the PAL, all the timed knowledge changes will be modeled, using a temporal logic transition system. Later, following by the translation of natural language questions into the temporal formulas, the satisfaction leads the model to answer that question. This interpretation integrates the information of the recognized input data, rules, and knowledge. Finally, we suggest a mechanism to reduce the investigated paths for the performance improvements, which results in a partial correction for an object-detection system.

scholarly journals Future determination of entities in Talmudic public announcement logic

2013 ◽  
Vol 11 (1) ◽  
pp. 63-90 ◽  
Author(s):  
M. Abraham ◽  
I. Belfer ◽  
D.M. Gabbay ◽  
U. Schild
Keyword(s):  
Public Announcement ◽  
Public Announcement Logic

A cut-free labelled sequent calculus for dynamic epistemic logic

2020 ◽  
Vol 30 (1) ◽  
pp. 321-348
Author(s):  
Shoshin Nomura ◽  
Hiroakira Ono ◽  
Katsuhiko Sano
Keyword(s):  
Modal Logic ◽  
Epistemic Logic ◽  
Sequent Calculus ◽  
Dynamic Epistemic Logic ◽  
Kripke Semantics ◽  
Cut Rule ◽  
Public Announcement ◽  
Public Announcement Logic ◽  
Soundness And Completeness ◽  
Labelled Sequent Calculus

Abstract Dynamic epistemic logic is a logic that is aimed at formally expressing how a person’s knowledge changes. We provide a cut-free labelled sequent calculus ($\textbf{GDEL}$) on the background of existing studies of Hilbert-style axiomatization $\textbf{HDEL}$ of dynamic epistemic logic and labelled calculi for public announcement logic. We first show that the $cut$ rule is admissible in $\textbf{GDEL}$ and show that $\textbf{GDEL}$ is sound and complete for Kripke semantics. Moreover, we show that the basis of $\textbf{GDEL}$ is extended from modal logic K to other familiar modal logics including S5 with keeping the admissibility of cut, soundness and completeness.

scholarly journals DYNAMIC CONSEQUENCE AND PUBLIC ANNOUNCEMENT

2013 ◽  
Vol 6 (4) ◽  
pp. 659-679 ◽  
Author(s):  
ANDRÉS CORDÓN FRANCO ◽  
HANS VAN DITMARSCH ◽  
ANGEL NEPOMUCENO
Keyword(s):  
Common Knowledge ◽  
Consequence Relations ◽  
Multi Agent Systems ◽  
Consequence Relation ◽  
Agent Systems ◽  
Public Announcement ◽  
Public Announcement Logic ◽  
Multi Agent ◽  
Image Position ◽  
Open Question

AbstractIn van Benthem (2008), van Benthem proposes a dynamic consequence relation defined as ${\psi _1}, \ldots ,{\psi _n}{ \models ^d}\phi \,{\rm{iff}}{ \models ^{pa}}[{\psi _1}] \ldots [{\psi _n}]\phi ,$ where the latter denotes consequence in public announcement logic, a dynamic epistemic logic. In this paper we investigate the structural properties of a conditional dynamic consequence relation $\models _{\rm{\Gamma }}^d$ extending van Benthem’s proposal. It takes into account a set of background conditions Γ, inspired by Makinson (2003) wherein Makinson calls this reasoning ‘modulo’ a set Γ. In the presence of common knowledge, conditional dynamic consequence is definable from (unconditional) dynamic consequence. An open question is whether dynamic consequence is compact. We further investigate a dynamic consequence relation for soft instead of hard announcements. Surprisingly, it shares many properties with (hard) dynamic consequence. Dynamic consequence relations provide a novel perspective on reasoning about protocols in multi-agent systems.

scholarly journals Linear Temporal Public Announcement Logic: a new perspective for reasoning the knowledge of multi-classifiers

2020 ◽  
Author(s):  
Amirhoshang Hoseinpour Dehkordi ◽  
Majid Alizadeh ◽  
Ali Movaghar
Keyword(s):  
Temporal Logic ◽  
Intelligent Systems ◽  
Time Series Data ◽  
Detection System ◽  
Transition System ◽  
Series Data ◽  
Performance Improvements ◽  
Public Announcement ◽  
Public Announcement Logic ◽  
New Perspective

Current applied intelligent systems have crucial shortcomings either in reasoning the gathered knowledge, or representation of comprehensive integrated information. To address these limitations, we develop a formal transition system which is applied to the common artificial intelligence (AI) systems, to reason about the findings. The developed model was created by combining the Public Announcement Logic (PAL) and the Linear Temporal Logic (LTL), which will be done to analyze both single-framed data and the following time-series data. To do this, first, the achieved knowledge by an AI-based system (i.e., classifiers) for an individual time-framed data, will be taken, and then, it would be modeled by a PAL. This leads to developing a unified representation of knowledge, and the smoothness in the integration of the gathered and external experiences. Therefore, the model could receive the classifier's predefined -or any external- knowledge, to assemble them in a unified manner. Alongside the PAL, all the timed knowledge changes will be modeled, using a temporal logic transition system. Later, following by the translation of natural language questions into the temporal formulas, the satisfaction leads the model to answer that question. This interpretation integrates the information of the recognized input data, rules, and knowledge. Finally, we suggest a mechanism to reduce the investigated paths for the performance improvements, which results in a partial correction for an object-detection system.

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