Quality of Knowledge in Virtual Entities

Abstract

The work done by some authors in the fields of computer science, artificial intelligence, and multi-agent systems foresees an approximation of these disciplines and those of the social sciences, namely, in the areas of anthropology, sociology, and psychology. Much of this work has been done in terms of the humanization of the behavior of virtual entities by expressing human-like feelings and emotions. Some authors (e.g., Ortony, Clore & Collins, 1988; Picard, 1997) suggest lines of action considering ways to assign emotions to machines. Attitudes like cooperation, competition, socialization, and trust are explored in many different areas (Arthur, 1994; Challet & Zhang, 1998; Novais et al., 2004). Other authors (e.g., Bazzan et al., 2000; Castelfranchi, Rosis & Falcone, 1997) recognize the importance of modeling virtual entity mental states in an anthropopathic way. Indeed, an important motivation to the development of this project comes from the author’s work with artificial intelligence in the area of knowledge representation and reasoning, in terms of an extension to the language of logic programming, that is, the Extended Logic Programming (Alferes, Pereira & Przymusinski, 1998; Neves, 1984). On the other hand, the use of null values to deal with imperfect knowledge (Gelfond, 1994; Traylor & Gelfond, 1993) and the enforcement of exceptions to characterize the behavior of intelligent systems (Analide, 2004) is another justification for the adoption of these formalisms in this knowledge arena. Knowledge representation, as a way to describe the real world based on mechanical, logical, or other means, will always be a function of the systems ability to describe the existent knowledge and their associated reasoning mechanisms. Indeed, in the conception of a knowledge representation system, it must be taken into attention different instances of knowledge.

Formal Models in Web Based Contracting

“…This is supported by the developments in where the representation of incomplete information and the reasoning based on partial assumptions is studied, using the representation of null values to characterize abnormal or exceptional situations. The identification of null values emerges as a strategy for the enumeration of cases, for which one intends to distinguish between situations where the answers are known (true or false) or unknown [2]. It will be considered two types of null values: the former will allow the representation of unknown values, not necessarily taken from a given set of values, and the later will represent unknown values, taken from a given set of possible values.…”

“…In our approach, the learning procedure is based on evolution and is built on a quantification process of the quality of information that stems from a logic program or theory. This may be stated as follows: let i ( i ∈ {1,…,m}) denote the predicates whose extensions make an extended logic program or theory, and j (j ∈ {1,…,n}) the attributes for those predicates [2]. Let x j ∈ [min j , max j ] be a value for attribute j.…”

Ambient Intelligence and Simulation in Health Care Virtual Scenarios

“…High quality information and content may therefore be available to any user, anywhere, at any time, and on any device. Users are aware of their presence and context and digital environments are sensitive, adaptive, and responsive to needs, habits, gestures and emotions [10]. In virtual health care environments, they may not be separated from medical informatics, biomedical informatics or bio-informatics, aggregating electronic health records, decision support, telemedicine, knowledge representation and reasoning, knowledge discovery and computational biology.…”

“…The above program is now rewritten in terms of the function Lp (being p a predicate), in the form: It is therefore possible, through the evaluation of a simple area, to measure or quantifY the quality of the information that is carried out by the logic program referred to above, whose values are to be taken in the interval [0, I] [10]. Indeed, logic is broadly concerned with studying inference and expressive power of formal languages with well-defined semantics.…”

“…An objective measure of SA may be more sensitive and diagnostic than traditional performance measures. In order to model the system, it is considered an extension to the logic programming language, where a logic program presents two kinds of negation, classical negation "~,, and negation-by-failure "not" [10]. Intuitively, "not a" is true whenever there is no reason to believe "a", whereas "~a" requires a proof of the negated literal.…”