In real world planning problems, it might not be possible for an automated agent to satisfy all the objectives assigned to it. When this situation arises, classical planning returns no plan. In partial satisfaction planning, it is possible to satisfy only a subset of the objectives. To solve this kind of problems, an agent can select a subset of objectives and return the plan that maximizes the net benefit, i.e. the sum of satisfied objectives utilities minus the sum of the cost of actions. This approach has been experimented for deterministic planning. This paper extends partial satisfaction planning for problems with uncertainty on time. For problems under uncertainty, the best subset of objectives can not be calculated at planning time. The effective duration of actions at execution time may dynamically influence the achievable subset of objectives. Our approach introduces special abort actions to explicitly abort objectives. These actions can have deadlines in order to control when objectives can be aborted.
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