Abstract

Abstract: 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.

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