Learning from experimentation allows a system to acquire planning domain knowledge by correcting its knowledge when an action execution fails. Experiments are designed and planned to bring the world to a state where a hypothesis (e.g., that an operator is missing a precondition) can be tested. When planning an experiment, the planner must take into account the interactions between the execution of the main plan and the execution of the experiment plans, since after the experiment itmust continue to carry on its main task. In order for planners to work in such environments where they can be given several tasks, they must take into account the interactions between them. A usual assumption in current planning systems is that they are given a single task (or set of goals to achieve). However, a plan that may seem adequate for a task in isolation may make other tasks harder (or even impossible) to achieve. Different tasks may compete for resources, execute irreversible actions that make other tasks unachievable, or set the world in undesirable states. This paper discusses what these interactions are and presents how the problem was adressed in EXPO, an implemented system that acquires domain knowledge for planning through experimentation.