Agential AI for Integrated Continual Learning, Deliberative Behavior, and Comprehensible Models
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915126402088960 |
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| author | Erden, Zeki Doruk Faltings, Boi |
| author_facet | Erden, Zeki Doruk Faltings, Boi |
| contents | Contemporary machine learning paradigm excels in statistical data analysis, solving problems that classical AI couldn't. However, it faces key limitations, such as a lack of integration with planning, incomprehensible internal structure, and inability to learn continually. We present the initial design for an AI system, Agential AI (AAI), in principle operating independently or on top of statistical methods, designed to overcome these issues. AAI's core is a learning method that models temporal dynamics with guarantees of completeness, minimality, and continual learning, using component-level variation and selection to learn the structure of the environment. It integrates this with a behavior algorithm that plans on a learned model and encapsulates high-level behavior patterns. Preliminary experiments on a simple environment show AAI's effectiveness and potential. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_16922 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agential AI for Integrated Continual Learning, Deliberative Behavior, and Comprehensible Models Erden, Zeki Doruk Faltings, Boi Artificial Intelligence Machine Learning Contemporary machine learning paradigm excels in statistical data analysis, solving problems that classical AI couldn't. However, it faces key limitations, such as a lack of integration with planning, incomprehensible internal structure, and inability to learn continually. We present the initial design for an AI system, Agential AI (AAI), in principle operating independently or on top of statistical methods, designed to overcome these issues. AAI's core is a learning method that models temporal dynamics with guarantees of completeness, minimality, and continual learning, using component-level variation and selection to learn the structure of the environment. It integrates this with a behavior algorithm that plans on a learned model and encapsulates high-level behavior patterns. Preliminary experiments on a simple environment show AAI's effectiveness and potential. |
| title | Agential AI for Integrated Continual Learning, Deliberative Behavior, and Comprehensible Models |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.16922 |