Uncertainty Modelling In Knowledge Engineering And Decision Making - Proceedings Of The 12th International Flins Conference (Flins 2016)

Proceedings of the 12th International FLINS Conference (FLINS 2016)

Xianyi Zeng, Jie Lu;Etienne E Kerre;Luis Martinez;Ludovic Koehl

• Publisher: WSPC
• Year: 2016
• eBook ISBN: 9789813146983
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to include Computational Intelligence for applied research. The contributions to the 12th of FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, both from the foundations and the applications points-of-view.

PART 1

DECISION MAKING AND FUZZY TECHNIQUES

THE INTUITIVE CARTOGRAPHIC REPRESENTATION IN DECISION-MAKING

STANISLAV BELYAKOV, ALEXANDER BOZHENYUK

Department of Information Security Systems, Southern Federal University,
Nekrasovsky 44, Taganrog, 347928, Russia

IGOR ROZENBERG

Public corporation “Research and development institute of railway engineers”,
27/1, Nizhegorodskaya, Moscow, 109029, Russia

The problem of the accumulation of experience and the use of decision-making in the previously observed situations is researched. The main objective of the research is development of the data model that provides the upgrade of reliability of decision-making on the basis of experience. The concept of the image of the situation, which has not clearly defined center and a neighborhood, is introduced. The main thing is not trajectories in feature space but the admissible transformations of situations and solutions.

Keywords: Geographic information systems, case analysis, figurative representation of experience, decision-making.

1.Introduction

Geographic information systems (GIS) are widely used in the systems of decision-making as facilities of time-space data processing [1–4]. An important advantage of GIS is the rendering engine, which gives a special opportunity of real-world situations’ analysis.

An important feature of the presentation and usage of knowledge in the GIS is the ability of its visualization. Visualization allows to simulate creative thinking. Knowledge representation by iconic image-mapping models and the use of knowledge by its specific comparison can be considered as the implementation of the conceptual semantics principles [5]. The use of fuzzy sets to describe the decision-making process with visualization is very interesting. Images description formalism and use logic help to automate the generation of solutions. As a result, the facts and the experts’ intuitive generalizations will be available for reuse.

The features of the procedure for constructing intelligent GIS solutions using figurative representation of decision-making experience are analyzed in this paper.

2.The Case-Based Analysis of Images

The concept of decision-making experience representation can be selected in different ways, based on the mode of the intellectual system’s usage [2, 3]. The systems operate in mode of precedent analysis [3]. Case-based reasoning [6] is based on superficial knowledge, limited description of the characteristic features of objects or phenomena. The process of finding the solution is finding previously observed similar precedent in the knowledge base and the adaptation of decision to the concerned precedent. Using the analogy, as known, gives probable but no reliable conclusions [7]. In an effort to improve the accuracy, we can intellectualize as follows.

Supposing that D(s) there is a dependency that is used for generation solutions for a given set of environmental parameters s. If you have experience in decision-making d₀ = D(s₀) with the parameter values s₀, the spreading of this experience to the problematic situation with the parameters s_p means obtaining the decision d_p = D(s_p). The condition of the positive effect of usage of the solution d_p in a problem situation is the inequality:

where W is the criterion of solutions’ quality. It is obvious that the applicability of the solution d_p - only a hypothesis, the accuracy of which is higher, s_p and s₀ closer. This follows from the continuity property of the real world. If

inequality (1) becomes valid equality. It is obeyed in the surroundings s_p where

as it ensures no solutions’ quality loss.

As the dependence D(s) is not known, the equation (2) can be regarded as a conceptual basis for knowledge representation, which is the accumulation of knowledge about deviations (transformations) of situations’ parameters and solutions. The knowledge of variations is an opportunity to construct a fair solution D(s) in a neighborhood Δs of place s with the degree of membership

whereas the absence of such knowledge is

The condition (2) can be regarded as a formal basis for the concept of knowledge representation in the form of the transformation of situations. Reflection of the happened events and analysis of the decision-making are the experience of mental activity. This experience is valuable. The vag...

Table of contents

• Cover
• Half
• Series
• Title
• Copy
• Foreword
• Cont
• Invited Lectures
• Part 1: Decision Making and Fuzzy Techniques
• Part 2: Intelligent Systems and Data Analysis with Uncertainty
• Part 3: Intelligent Systems and Knowledge Engineering
• Part 4: Logic and Automated Reasoning
• Part 5: Decision Making Under Uncertainty in Health Care Systems
• Part 6: Advanced Computations for Sustainable Development
• Part 7: Intelligent Systems for Logistics, Supply Chain and Production Optimization
• Part 8: Multi-Criteria Evaluation Applications
• Part 9: Soft Computing in Fashion Design and Textile Production
• Part 10: Risk and Security Analysis
• Part 11: Image Analysis, Neural Networks and Dynamic Systems
• Author Index
• Acknowledgements