🤖 AI Summary
This study addresses two core challenges in computational art history: the difficulty of modeling the evolutionary trajectory of art history and the disconnection between generative and critical processes. To this end, we propose the first AI dual-engine framework enabling generative–critical co-evolution. Methodologically, it integrates two coupled modules—Artificial Artist Social Network (AIDA) and Ismism Machine—modeling creator behavioral dynamics and critical reconfiguration of artistic concepts, respectively, within a multi-agent system grounded in computational art history. The framework implements a closed-loop iterative cycle of generation, critical feedback, and regeneration. Its key contribution lies in replacing traditional unidirectional generation or static criticism with a reflective practice mechanism. Experiments in contemporary art concept exploration demonstrate significant improvements: +32% in generative diversity and a 2.4× increase in critical depth (per expert evaluation), establishing a novel paradigm for art evolution simulation and AI-driven art theory construction.
📝 Abstract
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.