Knowledge discovery in databases and machine learning are important areas in the move to ÒintelligentÓ information systems. Logic programming is a powerful and flexible framework in this respect, as it allows background knowledge to be expressed and rules to be extracted. Inductive logic programming has had some notable successes. One shortcoming of logic programming is the lack of a convenient...

1: The Food, Water, Energy Nexus 2: The Politics of Nexus-Related Research 3: Origins, Aims and Questions 4: Multi-, Interand Transdisciplinarity 5: Methods, Methodologies and Functions 6: Context, Complexity and Uncertainty 7: Risk, Evidence and Sound Science 8: Objectivity, Subjectivity and Framing 9: Critical Reflection and Reflexivity 10: Inductive, Deductive and Abductive Methods in Nexus-...

Vagueness and uncertainty have attracted attention of philosophers and logicians for many years. Recently AI researchers contributed essentially to this area of research. Fuzzy set theory and the theory of evidence are seemingly the most appealing ones. In this note we present a new outlook on these problems, based on the rough set theory-which seems to be a very suitable mathematical tool to d...

Much of this is taken from Luce and Raiffa’s book and G. Owen’s book and this should be thought of as an extract from these references. Decision making is classified into four types based on who makes the decision (i)individual; (ii)group and on under what conditions it is made: (a)certainty;(b)risk; (c)uncertainty; or (d) combination of (b) and (c). Regardless of whether the decision is made b...

We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in terms of soft and hard constraints which determine ...

The main statistics used in rough set data analysis, the approximation quality, is of limited value when there is a choice of competing models for predicting a decision variable. In keeping within the rough set philosophy of non–invasive data analysis, we present three model selection criteria, using information theoretic entropy in the spirit of the minimum description length principle. Our ma...

A precision aspheric artefact is measured in 3D by a commercially available nano-measuring machine (NMM) integrated with a contact inductive sensor as the probe. The mathematics of 3D compensation of the error caused by the probe radius is derived. The influence of the probe radius measurement uncertainty on the compensation errors for the 3D measurements is discussed. If the calibration uncert...

Collective Strategy is the attempt of organizations to manage their interdependence by substituting contractual for competitive interconnectedness, creating a partially endogenous social environment. This paper discusses the concept of Collective Strategy and applies it to the collaboration of firms in an industry, which have the objective to reduce competition within the collective in order to...

Inductive learning is the learning that is based on induction. In inductive learningDecision tree algorithms are very famous. For the appropriate classification of the objects with the given attributes inductive methods use these algorithms basically. These algorithms are very important in the classification of the objects. That is why many of these algorithms are used in the intelligent system...

Inductive learning that creates decision trees from a set of existing cases is useful for automated knowledge acquisition. Most of the existing methods in literature are based on crisp concepts that are weak in handling marginal cases. In this paper, we present a fuzzy inductive learning method that integrates the fuzzy set theory into the regular inductive learning processes. The method conver...