Purpose This paper aims to develop a conceptual framework on how the representativeness heuristic operates in decision-making process. Specifically, authors unbundle into its building blocks: search rule, stopping rule and decision rule. Furthermore, focus is placed individual-level cognitive behavioral factors, namely experience, intuition overconfidence, affect functioning of this heuristic. ...

This thesis studies two different approaches to extracting information from collections of phylogenetic trees: supertrees and reduced consensus. Supertree methods combine the phylogenetic information from multiple partially-overlapping trees into a larger phylogenetic tree called a supertree. Several supertree construction methods have been proposed to date, but most of these are not designed w...

In this paper, we consider the iterative Lavrentiev regularization method for obtaining a stable approximate solution nonlinear ill-posed operator equation $F(x)=y$, where $ F:D(F) \subset X \rightarrow X$ is monotone on Hilbert spaces $X$. order to obtain using methods, it important use suitable stopping rule terminate iterations at appropriate step. Recently, Qinian Jin and Wei Wang (2018) ha...

We proposed earlier in [Dormoy & Raiman, 19881 a new way of reasoning about a device, we called “Assembling a Device”. Starting from a component description (namely confluences), the qualitative resolution rule provides task-oriented global relations which link the physical quantities involved in a device to some selected reference variables. This rule is complete: given any task to be performe...

In this paper, an orthogonal strip packing problem with rotation of items and without the guillotine packing constraint is considered. A fast heuristic algorithm for the large-scale problems is presented. This heuristic algorithm is mainly based on heuristic strategies inspired by the wall-building rule of bricklayers in daily life. The heuristics is simple and the setting of parameter is not r...

Heuristic inference rules with a measure of strength less than certainty have an unusual property: better individual rules do not necessarily lead to a better overall rule set. All less-than-certain rules contribute evidence towards erroneous conclusions for some problem instances, and the distribution of these erroneous conclusions over the instances is not necessarily related to individual ru...

Rule learning is typically used in solving classification and prediction tasks. However, learning of classification rules can be adapted also to subgroup discovery. This paper shows how this can be achieved by modifying the CN2 rule learning algorithm. Modifications include a new covering algorithm (weighted covering algorithm), a new search heuristic (weighted relative accuracy), probabilistic...

The DPLL (Davis-Putnam-Logemann-Loveland) procedure is one of the most effective methods for solving SAT problems. It is well known that its efficiency depends on the choice of the branching rule. Different branching rules are proposed in the literature. Unit propagation look-ahead (UPLA) branching rule was one of the main improvements in the DPLL procedure (e.g.,[10]). The UPLA branching rule ...

The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. Our basic approach is to learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set performance from training set characteristics. We investigate several variations of this basic scenario, inclu...