Artism: AI-Driven Dual-Engine System for Art Generation and Critique
This addresses the challenge of simulating art historical developments and conceptual innovation for computational art analysis, though it appears incremental as it builds on existing AI and multi-agent approaches.
The paper tackles the problem of exploring art evolution trajectories by proposing a dual-engine AI system with interconnected components for art generation and critique, introducing a general methodology based on AI-driven critical loops for computational art analysis.
This paper proposes a dual-engine AI architectural method designed to address the complex problem of exploring potential trajectories in the evolution of art. We present two interconnected components: AIDA (an artificial artist social network) and the Ismism Machine, a system for critical analysis. The core innovation lies in leveraging deep learning and multi-agent collaboration to enable multidimensional simulations of art historical developments and conceptual innovation patterns. The framework explores a shift from traditional unidirectional critique toward an intelligent, interactive mode of reflexive practice. We are currently applying this method in experimental studies on contemporary art concepts. This study introduces a general methodology based on AI-driven critical loops, offering new possibilities for computational analysis of art.