  The classic cake-cutting problem provides a model for addressing the fair and
efficient allocation of a divisible, heterogeneous resource among agents with
distinct preferences. Focusing on a standard formulation of cake cutting, in
which each agent must receive a contiguous piece of the cake in an offline
setting, this work instead focuses on online allocating non-contiguous pieces
of cake among agents and establishes algorithmic results for fairness measures.
In this regard, we made use of classic adversarial multi-armed bandits to
achieve sub-linear Fairness and Revenue Regret at the same time. Adversarial
bandits are powerful tools to model the adversarial reinforcement learning
environments, that provide strong upper-bounds for regret of learning with just
observing one action's reward in each step by applying smart trade-off between
exploration and exploitation. This work studies the power of the famous EXP_3
algorithm that is based on exponential wight{-}importance updating probability
distribution through time horizon.
