{"id":259,"date":"2025-11-07T14:52:12","date_gmt":"2025-11-07T14:52:12","guid":{"rendered":"https:\/\/www.ieeesmc.org\/cai-2026\/?page_id=259"},"modified":"2025-12-19T20:21:52","modified_gmt":"2025-12-19T20:21:52","slug":"w7-gpais","status":"publish","type":"page","link":"https:\/\/www.ieeesmc.org\/cai-2026\/w7-gpais\/","title":{"rendered":"Workshop W7-GPAIS: Workshop on General-Purpose Artificial Intelligent Systems (GPAIS)"},"content":{"rendered":"

Proposers<\/p>\n

Prof. Dr. Xingyu Wu<\/a>, Prof. Dr. Daniel Molina<\/a>, Prof. Dr. Isaac Triguero<\/a> and Prof. Dr. Javier Del Ser<\/a><\/p>\n

Workshop Code<\/p>\n

Please use the following code when submitting your paper to this Workshop: W7-GPAIS<\/strong><\/p>\n

Scope and Aims<\/p>\n

In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems designed to tackle a single task. In this context, a General-Purpose Artificial Intelligence System (GPAIS) refers to an advanced AI system capable of effectively performing a range of distinct tasks. Its degree of autonomy and ability is determined by several key characteristics, including the capacity to adapt or perform well on new tasks that arise at a future time, the demonstration of competence in domains for which it was not intentionally and specifically trained, the ability to learn from limited data, and the proactive acknowledgement of its own limitations in order to enhance its performance. The overarching design goal of a GPAIS is to design AI models with the ability not only to perform[1]. Several AI techniques have been identified as promising approaches to enhance GPAIS [2].<\/p>\n

This workshop is aligned and in cooperation with the IEEE CIS TaskForce LLMs and Computational Intelligence for General Purpose Artificial Intelligent Systems (GPAIS).<\/p>\n

[1] Triguero, I., Molina, D., Poyatos, J., Del Ser, J., & Herrera, F. (2024). General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance. Information Fusion, 103. Scopus. https:\/\/doi.org\/10.1016\/j.inffus.2023.102135<\/p>\n

[2] Molina, D., Poyatos, J., Ser, J. D., Garc\u00eda, S., Ishibuchi, H., Triguero, I., Xue, B., Yao, X., & Herrera, F. (2025). Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects. IEEE Transactions on Evolutionary Computation, 1-1. IEEE Transactions on Evolutionary Computation. https:\/\/doi.org\/10.1109\/TEVC.2025.3530096<\/p>\n

Content and Objectives<\/p>\n

In this Workshop, we encourage researchers to submit original contributions proposing new algorithmic approaches, improvements for GPAIS. Potential topics of interest for the workshop include, but are not limited to, the following:<\/p>\n

Using Computational Intelligence (CI) to Enhance GPAIS Performance and Expand GPAIS Application Boundaries<\/strong><\/p>\n