An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Conditional, probabilistic planning: a unifying algorithm and effective search control mechanisms
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Risk sensitive reinforcement learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Probabilistic Planning in the Graphplan Framework
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
A model for projection and action
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Path planning under time-dependent uncertainty
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Agents often have preference models that are more complicated than minimizing the expected execution cost. In this paper, we study how they should act in the presence of uncertainty and immediate soft deadlines. Delivery robots, for example, are agents that are often confronted with immediate soft deadlines. We introduce the additive and multiplicative planning-task transformations, that are fast representation changes that transform planning tasks with convex exponential utility functions to planning tasks that can be solved with variants of standard deterministic or probabilistic artificial intelligence planners. Advantages of our representation changes include that they are context-insensitive, fast, scale well, allow for optimal and near-optimal planning, and are grounded in utility theory. Thus, while representation changes are often used to make planning more efficient, we use them to extend the functionality of existing planners, resulting in agents with more realistic preference models.