AUTHOR(S):

Abdel-Badeeh M. Salem, Mohamed Gawish

TITLE

Study on Analogical Reasoning Methodologies For Developing Analogical Learning Systems

KEYWORDS

Analogical reasoning, analogical learning systems, structured production rules, fuzzy rules, cognitive scripts, cases, semantic networks

ABSTRACT

In the last years, various reasoning methodologies have been proposed by the researchers in order to develop intelligent learning systems. These systems are based on the concepts and theories of the artificial intelligence (AI) science and technology. Many types of learning systems are in existence today and are applies to different domains and tasks, e.g., health care, business, commerce, and education. From the AI point of view, the research in the reasoning paradigms cover a variety of approaches. Analogical reasoning techniques (ARTs) play in developing an efficient and intelligent Analogical Learning Systems (ALSs). A number of computational models of analogy have been employed in a wide variety of research ALSs in different fields, acquiring features of how human compare representations, retrieve source analogues from memory, and learn from the results. This paper investigates the main features of some ARTs (namely, structured production rules, fuzzy rules, cognitive scripts, cases, and semantic networks) of used for the development of ALSs from the AI perspective.

Cite this paper

Abdel-Badeeh M. Salem, Mohamed Gawish. (2016) Study on Analogical Reasoning Methodologies For Developing Analogical Learning Systems. International Journal of Circuits and Electronics, 1, 54-61