With the advancement of technology represented by artificial intelligence, art creation is becoming increasingly rich, and content expression is intelligent, interactive, and data-driven, making the relationship between technology, art, and people increasingly close and bringing opportunities for the development of emerging interaction. Artificial intelligence technologies aim to perfectly replicate the human mind by enabling natural responses based on the surrounding environment, decoding emotions, and recognizing human traits within the energy range. Driven by AI technology, interactive art no longer focuses on a single audiovisual sensory experience but rather on integrated artistic expressions that are highly interactive, kinetic, and emotional, based on the study of natural human behavior and integrated senses, combined with intelligence. In this paper, we first sort out the intersection of AI technology development and interactive art expression streams on the timeline based on historical development and analyze the deconstructive relationship between the two from the macroperspective of the historical development of technology and art. First, based on the conceptual connotation, development history, technical application, and singularity outlook of AI, we identify the current characteristics and development trends of interactive art; second, based on exploring the advantages of AI technology, we propose the impact of AI on the creative thinking, creative mode, and artistic experience of interactive art and establish the paradigm of interactive art creation in the context of AI. It solves the problem that experts are unable to quickly locate the category of painters when facing different styles of unsigned digital Chinese painting images in the authenticity identification task.
Being popular in language evolution, cognitive science, and culture dynamics, the Naming Game has been widely used to analyze how agents reach global consensus via communications in multi-agent systems. Most prior work considered networks that are symmetric and homogeneous (e.g., vertex transitive). In this paper we consider asymmetric or heterogeneous settings that complement the current literature: 1) we show that increasing asymmetry in network topology can improve convergence rates. The star graph empirically converges faster than all previously studied graphs; 2) we consider graph topologies that are particularly challenging for naming game such as disjoint cliques or multi-level trees and ask how much extra homogeneity (random edges) is required to allow convergence or fast convergence. We provided theoretical analysis which was confirmed by simulations; 3) we analyze how consensus can be manipulated when stubborn nodes are introduced at different points of the process. Early introduction of stubborn nodes can easily influence the outcome in certain family of networks while late introduction of stubborn nodes has much less power.
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