Neural network models for situation similarity assessment in hybrid-CBR
The case-based reasoning method has a high potential for solving tasks of intelligence decision-support. To implement it, it is necessary to solve the problem of comparing situations and selecting the one that is most similar to the current situation in the knowledge base. The problem arises in the...
Gespeichert in:
Veröffentlicht in: | Journal of intelligent & fuzzy systems 2023-05, Vol.44 (5), p.7669-7682 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | The case-based reasoning method has a high potential for solving tasks of intelligence decision-support. To implement it, it is necessary to solve the problem of comparing situations and selecting the one that is most similar to the current situation in the knowledge base. The problem arises in the case of heterogeneous objects and situations with many different types of parameters and their possible uncertainty. In this paper, an approach based on machine (deep) learning is investigated for this task. It is proposed to carry out the process of selecting situations and solutions from the knowledge base in two stages: recognition of the states of the elements of a complex object and the relationships between them, then the formation of a representation of the situation in the state space and its use for comparing situations using neural networks. An ensemble neural network model based on a multi-layer network is proposed. It successfully simulates the cognitive functions of a human (expert), correctly selects similar situations and ranks them according to the similarity parameter. Proposed neural network models provide the implementation of a hybrid-CBR approach for decision-making on complex objects. |
---|---|
ISSN: | 1064-1246 1875-8967 |
DOI: | 10.3233/JIFS-221335 |