Algorithms Based on Paraconsistent Annotated Logic for Applications in Expert Systems (original) (raw)

Treatment of Uncertainties with Algorithms of the Paraconsistent Annotated Logic

The method presented in this work is based on the fundamental concepts of Paraconsistent Annotated Logic with annotation of 2 values (PAL2v). The PAL2v is a non-classic Logics which admits contradiction and in this paper we perform a study using mathematical interpretation in its representative lattice. This studies result in algorithms and equations give an effective treatment on signals of information that represent situations found in uncertainty knowledge database. From the obtained equations, algorithms are elaborated to be utilized in computation models of the uncertainty treatment Systems. We presented some results that were obtained of analyses done with one of the algorithms that compose the paraconsistent analyzing system of logical signals with the PAL2v Logic. The paraconsistent reasoning system built according to the PAL2v methodology notions reveals itself to be more efficient than the traditional ones, because it gets to offer an appropriate treatment to contradictory information. Treatment of Uncertainties with Algorithms of the Paraconsistent Annotated Logic 145 P (, λ) : T = Inconsistent = P (1, 1) , F = False = P (0, 1) , t = True = P (1, 0) ,  = Indeterminate = P (0, 0) Figure 1. Four-vertex lattice.

A symbolic model for the representation of uncertain knowledge: theory and applications

[Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics

The purpose of this paper is twofold. In the first part, a symbolic model for the representation of uncertainty in a knowledge-based system, allowing the encoding of the expert's uncertain knowledge without recourse t o any quantification, is proposed. The application, in the second part, t o a problem of civil engineering leads to the design of an expert system for the statical diagnosis of ancient buildings.

Parameterized uncertain reasoning approach based on a lattice-valued logic

Symbolic and Quantitative …, 2011

This paper presents a parameterized reasoning approach with uncertainty based on a lattice-valued logic system. In this uncertain reasoning approach, some parameters are used to represent uncertainty arising from different sources, which is a common phenomenon in rule-based systems. In our system, reasoning with different parameter values means reasoning with different levels of belief and consistency. Some methods are presented for selecting appropriate parameter values during the uncertain reasoning process which allow us to find suitable parameter values to meet the diverse practical and theoretical requirements.

The development of paraconsistent annotated logic programs

International Journal of Reasoning-based Intelligent Systems, 2009

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.

Combining multiple-valued logics in modular expert systems

1991

The way experts manage uncertainty usually changes depending on the task they are performing. This fact has lead us to consider the problem of communicating modules (task implementations) in a large and structured knowledge based system when modules have different uncertainty calculi. In this paper, the analysis of the communication problem is made assuming that (i) each uncertainty calculus is an inference mechanism defining an entailment relation, and therefore the communication is considered to be inference-preserving, and (ii) we restrict ourselves to the case which the different uncertainty calculi are given by a class of truth functional Multiple-valued Logics.

RUM: A layered architecture for reasoning with uncertainty

Proceedings of the Tenth …, 1987

New reasoning techniques for dealing with uncertainty in expert systems have been embedded in RUM, a Reasoning with Uncertainty Module. RUM is an integrated software tool based on a frame system (KEE) that is implemented in an object oriented language. RUM's capabilities are subdivided into three layers: representation, inference, and control. The representation layer is based on frame-like data structures that capture the uncertainty information used in the inference layer and the uncertainty mete-information used in the control layer. Linguistic probabilities are used to describe the lower and upper bounds of the certainty measure attached to a well formed formula. The source and the conditions under which the information was obtained represent the non-numerical meta-information. The inference layer provides the uncertainty calculi with which to perform the intersection, detachment, union, and pooling of information. Five uncertainty calculi, based on their underlying Triangular norms, are used in this layer. The control layer uses the meta-information to select the appropriate calculus for each context and to resolve eventual ignorance or conflict in the information. This layer enables the programmer to declaratively express the local (context dependent) meta-knowledge that will substitute for the global assumptions traditionally used in uncertain reasoning. RUM has been tested in a sequence of experiments in both naval and aerial situation assessment, consisting of correlating reports and tracks, locating and classifying platforms, and identifying intents and threats.

A linguistic truth-valued uncertainty reasoning model based on Lattice-Valued Logic

Fuzzy Systems and Knowledge Discovery, 2005

The subject of this work is to establish a mathematical framework that provide the basis and tool for uncertainty reasoning based on linguistic information. This paper focuses on a flexible and realistic approach, i.e., the use of linguistic terms, specially, the symbolic approach acts by direct computation on linguistic terms. An algebra model with linguistic terms, which is based on a logical algebraic structure, i.e., lattice implication algebra, is applied to represent imprecise information and deals with both comparable and incomparable linguistic terms (i.e., non-ordered linguistic terms). Within this framework, some inferential rules are analyzed and extended to deal with these kinds of lattice-valued linguistic information.