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Opportunistic Branched Plans to Maximise Utility in the Presence of Resource Uncertainty

机译:存在资源不确定性的机会性分支计划,以最大化效用

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In many applications, especially autonomous exploration, there is a trade-off between operational safety, forcing conservatism about resource usage; and maximising utility, requiring high resource utilisation. In this paper we consider a method of generating plans that maintain this conservatism whilst allowing exploitation of situations where resource usage is better than pessimistically estimated. We consider planning problems with soft goals, each with a violation cost. The challenge is to maximise utility (minimise the violation cost paid) whilst maintaining confidence that the plan will execute within the specified limits. We first show how forward search planning can be extended to generate such plans. Then we extend this to build branched plans: tree structures labelled with conditions on executing branches. Lower cost branches can be followed if their conditions are met. We demonstrate that the use of such plans can dramatically increase utility whilst still obeying strict safety constraints.
机译:在许多应用中,特别是在自主勘探中,在操作安全性与强制使用资源保守性之间需要权衡取舍。和最大化效用,需要高资源利用率。在本文中,我们考虑了一种生成计划的方法,该计划可以保持这种保守性,同时允许开发资源使用情况优于悲观估计的情况。我们考虑通过软目标计划问题,每个目标都有违规成本。挑战在于最大程度地发挥效用(最小化所支付的违规成本),同时又要确保计划将在指定的限制内执行。我们首先展示如何扩展前向搜索计划以生成此类计划。然后,我们将其扩展为构建分支计划:标记有执行分支条件的树结构。如果满足其条件,则可以遵循低成本分支机构。我们证明,使用此类计划可以显着提高实用性,同时仍然遵守严格的安全约束。

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