DeepAI
Log In Sign Up

A Fuzzy Model for Analogical Problem Solving

04/28/2012
by   Michael Gr. Voskoglou, et al.
0

In this paper we develop a fuzzy model for the description of the process of Analogical Reasoning by representing its main steps as fuzzy subsets of a set of linguistic labels characterizing the individuals' performance in each step and we use the Shannon- Wiener diversity index as a measure of the individuals' abilities in analogical problem solving. This model is compared with a stochastic model presented in author's earlier papers by introducing a finite Markov chain on the steps of the process of Analogical Reasoning. A classroom experiment is also presented to illustrate the use of our results in practice.

READ FULL TEXT

page 1

page 2

page 3

page 4

01/04/2014

A stochastic model for Case-Based Reasoning

Case-Bsed Reasoning (CBR) is a recent theory for problem-solving and lea...
07/02/2018

Shannon entropy for intuitionistic fuzzy information

The paper presents an extension of Shannon fuzzy entropy for intuitionis...
11/21/2013

Dealing with the Fuzziness of Human Reasoning

Reasoning, the most important human brain operation, is charactrized by ...
06/03/2011

Reasoning within Fuzzy Description Logics

Description Logics (DLs) are suitable, well-known, logics for managing s...
12/13/2017

Consideration on Example 2 of "An Algorithm of General Fuzzy InferenceWith The Reductive Property"

In this paper, we will show that (1) the results about the fuzzy reasoni...
04/29/2014

Assessing the players'performance in the game of bridge: A fuzzy logic approach

Contract bridge occupies nowadays a position of great prestige being, to...
05/21/2010

A Soft Computing Model for Physicians' Decision Process

In this paper the author presents a kind of Soft Computing Technique, ma...