AI bandits are algorithms that are used to make decisions in situations where there is uncertainty about the outcomes of different actions or where a choice needs to be made between a number of different options, but does not have enough information to make a fully informed decision. Based on information and experience gathered in previous input rounds, the algorithms are expected to make decisions in favor of maximizing a certain reward or objective. AI bandits allow AI systems to learn and adapt to changing situations in real time, making them more effective at achieving their objectives.
