Feedback de Bandit é um conceito usado no campo de aprendizado de máquina and inteligência artificial, particularly within the context of decision-making problems. It derives its name from the ‘multi-armed bandit’ problem, a classic scenario in teoria da probabilidade e estatística.
Em um problema do multi-armed bandit, a gambler faces multiple slot machines (or ‘bandits’) and must decide which one to play to maximize their winnings over time. Each machine has an unknown probability distribution of rewards, and the gambler must balance the exploration of new machines against the exploitation of known ones. Similarly, in the context of AI, Bandit Feedback involves making decisions based on limited information, where the feedback received from users helps improve future actions.
Em aplicações práticas, Feedback de Bandit é frequentemente utilizado em sistemas de recomendação, online advertising, and A/B testing. For instance, if a user interacts with a recommendation system, the feedback—such as clicks, purchases, or ratings—serves as a signal to adjust the algorithm that determines which items to suggest next. This feedback loop allows the system to learn and adapt its recommendations based on user preferences.
Importantly, Bandit Feedback can be categorized into two types: stochastic and adversarial. Stochastic bandits assume that the reward probabilities are stationary and can be estimated over time, while adversarial bandits deal with scenarios where the rewards may be influenced by an opponent or adversarial strategy. This distinction plays a significant role in how algorithms são projetados e aplicados em problemas do mundo real.
No geral, Feedback de Bandit é um mecanismo crucial para desenvolver sistemas inteligentes que podem aprender e se adaptar ao comportamento do usuário em ambientes dinâmicos.