This paper introduces a new approach to classification which combines pairwise decomposition techniques from machine learning with ideas and tools from fuzzy preference modeling. The approach, called fuzzy relational classification, effectively reduces the problem of classification to a problem of decision making based on a fuzzy preference relation. It will be shown that, by decomposing such a...

This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction prob...

The purpose of this paper is, to describe a work-in process for application of distributed agency methodology to multi-dimensional preference model into a complex social system. This paper shows a study case focused on a modeling system for decision-making on cognitive structure religious affiliation preference. A type-2 neurofuzzy approach is used to configure cognitive rules into an agent to ...

In a setting with repeated experiments, where evidence about the experiments is symmetric, a decision-maker ranks bets (or acts) over their outcomes. We describe a stark modeling trade-o¤ between symmetry of preference (indi¤erence to permutations), dynamic consistency and ambiguity. Then, assuming that experiments are ordered in time, we outline an axiomatic model of preference that exhibits d...

In this article, we describe an abductive representation of decision problems under uncertainty based on ATMS. Firstly, we extend the ATMS framework so as to include , in addition to the usual assumption symbols, preference and decision symbols. Then we show how this framework can be further extended, by allowing to assign multiple (real or qualitative) values to assumption and preference symbo...