Painting Effects Evaluation Based on Artificial Intelligence Technology using the Triangular OverNorm

The field of art, especially digital painting, has seen a radical change since the introduction of artificial intelligence (AI). As AI-generated art grows more popular, it is more important than ever to assess the usefulness and aesthetic appeal of these pieces. To assess painting effects using AI technology, this study offers a thorough framework that combines artistic sensibility with technical accuracy. To examine the effectiveness and impact of seven different AI-based painting techniques, eight assessment criteria are used. By providing insights into how machines understand, mimic, and possibly enhance human creativity in the visual arts, the study seeks to bridge the gap between computational developments and artistic interpretation. This study uses the multi-criteria decision-making approach (MCDM). The AROMAN method is used to rank the alternatives. The numerical example is provided in this study with eight criteria and seven alternatives. The sensitivity analysis is provided in this study to show the stability of the ranks. The results show the ranks of alternatives are stable in different cases.