Workshop W7-GPAIS: Workshop on General-Purpose Artificial Intelligent Systems (GPAIS)

Proposers

Prof. Dr. Xingyu Wu, Prof. Dr. Daniel Molina, Prof. Dr. Isaac Triguero and Prof. Dr. Javier Del Ser

Workshop Code

Please use the following code when submitting your paper to this Workshop: W7-GPAIS

Scope and Aims

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].

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

[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

[2] Molina, D., Poyatos, J., Ser, J. D., García, 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

Content and Objectives

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:

Using Computational Intelligence (CI) to Enhance GPAIS Performance and Expand GPAIS Application Boundaries

  • Neuro-symbolic and CI-enhanced LLM architectures
  • CI-based preprocessing for GPAIS
  • Foundation models
  • Explainability and safety in GPAIS
  • Low-resource adaptation and efficiency for GPAIS
  • CI for robust multi-modal and multi-agent AI
  • Evolutionary fine-tuning and prompt optimization
  • Structural optimization of LLM for different objectives (alignment, XAI, efficiency)
  • Large scale transformers and distributed computing strategies to build GPAIS

Using GPAIS to Advance Intelligent, Explainable, and Semantic-Aware CI: Leverage GPAIS to select:

  • GPAIS-driven intelligent evolutionary algorithms
  • GPAIS-driven intelligent fuzzy systems
  • GPAIS for automated CI algorithm configuration and new CI algorithm design
  • Automated algorithm construction using LLM translated domain knowledge
  • GPAIS-driven explainable CI techniques
  • Design of more efficient CI techniques

Application scenarios for CI+GPAIS:

  • Benchmarks and validation frameworks for GPAIS
  • Robotics

Conversational agents

  • Bioinformatics
  • Healthcare diagnosis with CI-enhanced LLMs
  • Neuro-symbolic architectures in autonomous vehicle navigation systems