Conditional independence provides an essential framework to deal with knowledge and uncertainty in Artificial Intelligence, and is fundamental in probability and multivariate statistics. Its associated implication problem is paramount for building Bayesian networks. Unfortunately, the problem does not enjoy a finite ground axiomatization and is already coNP-complete to decide for restricted sub...

This paper proposes a new algorithm for recovery of belief network structure from data handling hidden variables. It consists essentially in an extension of the CI algorithm of Spirtes et al. by restricting the number of conditional dependencies checked up to k variables and in an extension of the original CI by additional steps transforming so called partial including path graph into a belief ...

Performance on the Wason selection task varies with content. This has been taken to demonstrate that there are different cognitive modules for dealing with different conceptual domains. This implication is only legitimate if our underlying cognitive architecture is formal. A non-formal system can explain content-sensitive inference without appeal to independent inferential modules.

During the past few decades, missing-data problems have been studied extensively, with a focus on ignorable missing case, where probability depends only observable quantities. By contrast, research into non-ignorable data is quite limited. The main difficulty in solving such that and regression likelihood function are tangled together presentation, model parameters may not be identifiable even ...

in this paper, we present a new path-following interior-point algorithm for -horizontal linear complementarity problems (hlcps). the algorithm uses only full-newton steps which has the advantage that no line searchs are needed. moreover, we obtain the currently best known iteration bound for the algorithm with small-update method, namely, , which is as good as the linear analogue.

Modus Ponens is a key property for fuzzy implication functions that are going to be used in fuzzy inference processes. In this paper it is investigated when fuzzy implication functions derived from uninorms via residuation, usually called RU implications, satisfy the modus ponens with respect to a continuous t-norm T , or equivalently, when they are T -conditionals. For RU -implications it is p...

The paper deals with devising the combination of fuzzy inference systems (FIS) and neural networks called the adaptive network fuzzy inference system (ANFIS) to determine the forming limit diagram (FLD). In this paper, FLDs are determined experimentally for two grades of low carbon steel sheets using out-of-plane (dome) formability test. The effect of different parameters such as work hardening...

This paper establishes that the same abstract process of inference which allows the extraction of certain kinds of structure in languages may also be applied to the extraction of structure in images. The implication for similar mechanisms operating in the Central Nervous System is discussed. The paper considers initially only very primitive structures in order to focus on fundamental issues.

This paper deals with the problem of looking for the set of defuzzification method features allowing us to generate the most accurate fuzzy models in combination with each fuzzy implication operator. Hence, we try to find fuzzy inference engines that are appropriate from the practical engineering point of view. To do so, the three basic properties for fuzzy implication operators presented in [C...

A family of unit resolution-based paraconsistent inference relations `Σ for propositional logic in the clausal case is introduced. Parameter Σ is any consistent set of clauses representing the beliefs which are intended to be exploited through full deduction, i.e., every classical consequence of Σ must be kept w.r.t. `Σ, whatever the belief base B. Contrariwise to many paraconsistent inference ...