We discuss multi-task online learning when a decision maker has to deal
simultaneously with M tasks. The tasks are related, which is modeled by
imposing that the M-tuple of actions taken by the decision maker needs to
satisfy certain constraints. We give natural examples of such restrictions and
then discuss a general class of tractable constraints, for which we introduce
computationally efficient ways of selecting actions, essentially by reducing to
an on-line shortest path problem. We briefly discuss "tracking" and "bandit"
versions of the problem and extend the model in various ways, including
non-additive global losses and uncountably infinite sets of tasks
