| Title: | GIANT: General intelligent AgeNt trainer |
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| Authors: | ID Šmid, Marko (Author)
ID Ravber, Miha (Author) |
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| Files: |  1-s2.0-S2352711026001007-main.pdf (2,93 MB)
MD5: 7B4E2368E9C28DE2A250E43A8DE020A3
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.03 - Other scientific articles |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | Intelligent agent training is an active research area in artificial intelligence, primarily driven by reinforcement learning methods that utilize neural networks as decision-making systems. To broaden this field of research, we present the General Intelligent AgeNt Trainer, a flexible, modular, and scalable platform for training intelligent agents. The current implementation integrates evolutionary algorithms, with a particular focus on genetic pro gramming and behavior trees as interpretable decision-making systems. The platform’s architecture is designed for extensibility, allowing integration of additional machine learning techniques, optimization strategies, and decision-making systems. Furthermore, the platform aims to establish a benchmark for evaluating and comparing machine learning algorithms in both single-agent and multi-agent environments. |
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| Keywords: | machine learning, evolutionary algorithms, genetic programming, optimization, games, multi-agent systems |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 12.11.2025 |
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| Article acceptance date: | 11.03.2026 |
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| Publication date: | 18.03.2026 |
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| Publisher: | Elsevier B.V. |
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| Year of publishing: | 2026 |
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| Number of pages: | 9 str. |
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| Numbering: | Vol. 34, [article no.] 102607 |
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| PID: | 20.500.12556/DKUM-97592  |
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| UDC: | 004.8 |
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| ISSN on article: | 2352-7110 |
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| COBISS.SI-ID: | 272404483 |
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| DOI: | 10.1016/j.softx.2026.102607 |
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| Publication date in DKUM: | 23.03.2026 |
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| Views: | 333 |
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| Downloads: | 35 |
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| Metadata: |    |
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| Categories: | Misc.
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