A variety of methods exist for inductive learning of classification rules using crisp sets. In this paper we illustrate an inductive learner that uses fuzzy sets, where the membership functions of the linguistic terms are given in advance. We also show how the induction of conjunctive rules fit into a fuzzy set covering framework (FuzzyBexa) that we introduced before.

Is public opinion a beloved but unreal myth like the unicorn? Or a thing out there in the garden like an elephant, of which sight-impaired, competing methodologists measure different parts? Or a fuzzy set of probabilities like the electron, both wave and particle, perhaps of inherently uncertain location? Or does it include all of the above? ...If we want to measure public opinion, we need to d...

We introduce a model for voting under uncertainty where a group of voters have to decide on a joint action to take, but the individual voters are uncertain about the current state of the world and thus about the effect that the chosen action would have. Each voter has preferences about what state they would like to see reached once the action has been executed. That is, we need to integrate two...

Type-2 fuzzy set theory is one of the most powerful tools for dealing with the uncertainty and imperfection in dynamic and complex environments. The applications of type-2 fuzzy sets and soft computing methods are rapidly emerging in the ecological fields such as air pollution and weather prediction. The air pollution problem is a major public health problem in many cities of the world. Predict...

We present a probabilistic inductive logic programming framework which integrates non-monotonic reasoning, probabilistic inference and parameter learning. In contrast to traditional approaches to probabilistic Answer Set Programming (ASP), our framework imposes only comparatively little restrictions on probabilistic logic programs in particular, it allows for ASP as well as FOL syntax, and for ...

Inductive learning techniques can be utilised to build a set of IF-THEN rules from a given example data set. This paper presents a new technique for inductive learning called GAIL (Genetic Algorithm for Inductive Learning) which is based on Genetic Algorithms. Common algorithms for inductive learning are briefly reviewed. The GAIL algorithm is described and results are shown for two benchmark d...

Medical Decision Support Systems (MDSSs) are sophisticated, intelligent systems that can provide inference due to lack of information and uncertainty. In such systems, to model the uncertainty various soft computing methods such as Bayesian networks, rough sets, artificial neural networks, fuzzy logic, inductive logic programming and genetic algorithms and hybrid methods that formed from the co...

Abstract The quality of robot-assisted surgery can be improved and the use hospital resources optimized by enhancing autonomy reliability in robot’s operation. Logic programming is a good choice for task planning because it supports reliable reasoning with domain knowledge increases transparency decision making. However, prior typically incomplete, often needs to refined from executions surgica...