An Efficient XCS-based Algorithm for Learning Classifier Systems in Real Environments | ||
| Journal of AI and Data Mining | ||
| دوره 11، شماره 1، فروردین 2023، صفحه 13-27 اصل مقاله (2.09 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2022.12358.2384 | ||
| نویسندگان | ||
| Ali Yousefi1؛ Kambiz Badie* 2؛ Mohammad Mehdi Ebadzadeh3؛ Arash Sharifi4 | ||
| 1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. | ||
| 2Content & E-Services Research Group, IT Research Faculty, ICT Research Institute, Tehran, Iran. | ||
| 3Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran. | ||
| 4Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده | ||
| Recently, learning classifier systems are used to control physical robots, sensory robots, and intelligent rescue systems. The most important challenge in these systems, which are models of real environments, is its non-markov quality. Therefore, it is necessary to use memory to store system states in order to make decisions based on a chain of previous states. In this research, a memory-based XCS is proposed to help use more effective rules in classifier by identifying efficient rules. The proposed model was implemented on five important maze maps and led to a reduction in the number of steps to reach the goal and also an increase in the number of successes in reaching the goal in these maps. | ||
| کلیدواژهها | ||
| learning classifier systems (LCS)؛ XCS algorithm؛ identification of cycle and overlapping | ||
| مراجع | ||
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