Jair Minoro Abe

Annotated Semantics for Defeasible Deontic Reasoning

In this paper, we propose an annotated logic program called an EVALPSN (Extended Vector Annotated Logic Program with Strong Negation) to formulate the semantics for a defeasible deontic reasoning proposed by D.Nute. We propose a translation from defeasible deontic theory into EVALPSN and show that the stable model of EVALPSN provides an annotated semantics for D. Nutes defeasible deontic logic. The annotated semantics can provide a theoretical base for an automated defeasible deontic reasoning system.

A defeasible deontic reasoning system based on annotated logic programming

In this paper, we provide a theoretical framework for a defeasible deontic reasoning system based on annotated logic programming, we propose an annotated logic program called an EVALPSN (Extended Vector Annotated Logic Program with Strong Negation) to formulate a defeasible deontic reasoning proposed by D. Nute. We also propose a translation rule from defeasible deontic theory into EVALPSN and show that the derivability of defeasible deontic theory can be translated into the satisfiability of EVALPSN stable model. The combination of a translation system from defeasible deontic theories into EVALPSNs and a computing system of EVALPSN stable models can easily provide a theoretical framework for an automated defeasible deontic reasoning system.

Intelligent real-time traffic signal control based on a paraconsistent logic program EVALPSN

In this paper, we introduce an intelligent real-time traffic signal control system based on a paraconsistent logic program called an EVALPSN (Extended Vector Annotated Logic Program with Strong Negation), that can deal with contradiction and defeasible deontic reasoning. We show how the traffic signal control is implemented in EVALPSN with taking a simple intersection example in Japan. Simulation results for comparing EVALPSN traffic signal control to fixed-time traffic signal control are also provided.

Defeasible Deontic Robot Control Based on Extended Vector Annotated Logic Programming

We have already proposed an annotated logic program called an EVALPSN (Extended Vector Annotated Logic Program with Strong Negation) to deal with defeasible deontic reasoning. In this paper, we propose a defeasible deontic action control system for a virtual robot based on EVALPSN. We suppose a beetle robot who is traveling a maze with three kinds of obstacles and has some different kinds of sensors to detect the obstacles. If some sensor values are input to the robot control, the next action that the robot should do is computed by the EVALPSN programming system.

The paraconsistent process order control method

We have already developed some kinds of paraconsistent annotated logic programs. In this paper we propose the paraconsistent process order control method based on a paraconsistent annotated logic program called before–after extended vector annotated logic program with strong negation (bf-EVALPSN) with a small example of pipeline process order verification. Bf-EVALPSN can deal with before–after relations between two processes (time intervals) in its annotations, and its reasoning system consists of two kinds of inference rules called the basic bf-inference rule and the transitive bf-inference rule. We introduce how the bf-EVALPSN-based reasoning system can be applied to the safety verification for process order.

The development of paraconsistent annotated logic programs

We have developed paraconsistent annotated logic programs called Extended Vector Annotated Logic Program with Strong Negation (EVALPSN) and applied it to various intelligent control and safety verification. We have also developed EVALPSN to deal with before-after (bf) relation between time intervals and applied it to process order control. The developed EVALPSN is called bf-EVALPSN. In this paper, we review the process of the development of EVALPSN and bf EVALPSN and introduce the details of EVALPSN defeasible deontic reasoning.

Many-valued and annotated modal logics

Many-valued modal logics are of interest from theoretical and practical point of view. Unfortunately, there are no unified theoretical frameworks for many-valued modal logics. We sketch their foundations based on the so-called annotated logics. We give a Kripke semantics for annotated modal logics and prove the completeness theorem. We also discuss possible applications of annotated modal logics to AI.

ParaLog e: a paraconsistent evidential logic programming language

Inconsistency is a natural phenomenon arising from the description of the real world. This phenomenon may be encountered in several situations. Nevertheless, human beings are capable of reasoning adequately. The automation of such reasoning requires the development of formal theories. ParaLog (Paraconsistent Logic) was proposed by N.C.A. da Costa et al. (1995) to provide tools to reason about inconsistencies. This paper describes an extension of the ParaLog logic programming language, called ParaLog e, that allows direct handling of inconsistency. Languages such as ParaLog e, which are capable of merging classical logic programming concepts with those of inconsistency, widen the scope of logic programming applications in environments presenting conflicting beliefs and contradictory information.

Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study

EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis. Objectives To employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis. Methods Ten EEG records from patients with probable Alzheimer disease and ten controls were obtained during the awake state at rest. An EEG background between 8 Hz and 12 Hz was considered the normal pattern for patients, allowing a variance of 0.5 Hz. Results The PANN was capable of accurately recognizing waves belonging to Alpha band with favorable evidence of 0.30 and contrary evidence of 0.19, while for waves not belonging to the Alpha pattern, an average favorable evidence of 0.19 and contrary evidence of 0.32 was obtained, indicating that PANN was efficient in recognizing Alpha waves in 80% of the cases evaluated in this study. Artificial Neural Networks – ANN – are well suited to tackle problems such as prediction and pattern recognition. The aim of this work was to recognize predetermined EEG patterns by using a new class of ANN, namely the Paraconsistent Artificial Neural Network – PANN, which is capable of handling uncertain, inconsistent and paracomplete information. An architecture is presented to serve as an auxiliary method in diagnosing Alzheimer disease. Conclusions We believe the results show PANN to be a promising tool to handle EEG analysis, bearing in mind two considerations: the growing interest of experts in visual analysis of EEG, and the ability of PANN to deal directly with imprecise, inconsistent, and paracomplete data, thereby providing a valuable quantitative analysis.

Paraconsistent Artificial Neural Networks: An Introduction

In this work we sketch a new theory of artificial neural network, based on a paraconsistent annotated logic Eτ. Such theory, called Paraconsistent Artificial Neural Network – PANN – is built from the algorithm Para-analyzer and has as characteristics the capability of mainly manipulating uncertainty, inconsistent and paracomplete concepts. Some aspects such as capability of adaptation, velocity processing, and other useful characteristics make the PANN a promising theory.