Evidential reasoning is a body of techniques that supports automated reasoning from evidence. It is based upon the Dempster-Shafer theory of belief functions. Both the formal basis and a framework for the implementation of automated reasoning systems based upon these techniques are presented. The formal and practical approaches are divided into four parts (1) specifying a set of distinct propos...

In this paper we describe how an evidential-reasoner can be used as a component of risk assessment of engineering projects using a direct way of reasoning. Guan & Bell (1991) introduced this method by using the mass functions to express rule strengths. Mass functions are also used to express data strengths. The data and rule strengths are combined to get a mass distribution for each rule; i.e.,...

Fuzzy rule bases are built of linguistic, qualitative knowledge. By using fuzzy rules we are able to specify simple models of complex systems. But, we have to pay a price for this simpliication. In general, fuzzy knowledge is gradually incomplete and gradually inconsistent. This paper deals with the detection of such partial gaps of knowledge or local contradictions. In order to do so we introd...

This study presents a novel evidential reasoning (ER) prediction model called MAKER-RIMER to examine how different features embedded in Twitter posts (tweets) can predict the number of retweets achieved during an electoral campaign. The tweets posted by two most voted candidates official campaign for 2017 Ecuadorian Presidential election were used this research. For each tweet, five including t...

One of the most highly touted virtues of knowledge-based expert systems is their abil i ty to construct explanations of deduced lines of reasoning. However, there is a basic difficulty in generating explanations in expert systems that reason under uncertainty using numeric measures. In particular, systems based upon evidential reasoning using the theory of belief functions have lacked any facil...

As is well known, Dempster-Shafer theory offers an alternative approach to deal with uncertainty reasoning in expert systems and other fields. In this paper, we present two results that relate to the use of the theory. First, we present and analyze four methods to induce uncerioin rules fvlm a training instance set for Dempster-Shafer theory. This is the first attempt to do so, to the best of o...

This paper is on the formal modelling of reasoning about evidence. The main purpose is to advocate logical approaches as a worthwhile alternative to approaches rooted in probability theory. In particular, the use of logics for defeasible argumentation is investigated. Such logics model reasoning as the construction and comparison of arguments for and against a conclusion; this makes them very s...