| dc.description.abstract | With the widespread use of the internet, people have become accustomed to searching for information on search engines. With the rise of generative artificial intelligence, compared to traditional methods of browsing web pages, generative AI directly synthesizes search results, saving time and providing accurate responses. Using AI search engines has also become a popular choice. Given the long-term dominance of traditional search engines in the market, how to face the competition from AI search engines is an important issue. Using game theory, one can explore the strategies and equilibria of all participants in market competition. Therefore, this paper uses a game model to analyze the situation of product differentiation between two firms, discussing how different market environments affect the platform′s decisions. It explores how to set the pricing and the number of ads to maintain market competitiveness, formulate optimal strategies, and maximize profit. This paper will use the Nash Q-learning method to simulate real-world decision-making, leveraging high efficiency and simulations that are closer to real-world conditions. It observes potential decision-making trends in the real market, identifies the optimal equilibrium solution in the game model, and provides important reference points for search engine vendors, advertisers, and users when formulating business models and investment decisions. | en_US |