Optimal Policy Generation for Partially Satisfiable Co-Safe LTL Specifications
Optimal Policy Generation for Partially Satisfiable Co-Safe LTL Specifications
复制标题
部分满足共同安全零担规范的最优策略生成
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Nick Hawes
中科院分区：
文献类型：
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作者：
Bruno Lacerda;D. Parker;Nick Hawes
我们提出了一种方法，用于在随机系统的马尔可夫决策过程模型上计算协同安全线性时态逻辑中的任务规范的成本最优策略。我们的主要贡献是解决以概率一无法完成任务的场景。我们形式化了一个任务进展度量，并使用多目标概率模型检查，生成正式保证的策略，按优先级降序排列：最大化完成任务的概率；如果不可能的话，最大限度地提高完成进度；并最大限度地减少所需的预期时间或成本。我们在机器人任务规划场景中说明和评估我们的方法，其中任务是访问执行期间可能无法访问的一组房间。
We present a method to calculate cost-optimal policies for task specifications in co-safe linear temporal logic over a Markov decision process model of a stochastic system. Our key contribution is to address scenarios in which the task may not be achievable with probability one. We formalise a task progression metric and, using multi-objective probabilistic model checking, generate policies that are formally guaranteed to, in decreasing order of priority: maximise the probability of finishing the task; maximise progress towards completion, if this is not possible; and minimise the expected time or cost required. We illustrate and evaluate our approach in a robot task planning scenario, where the task is to visit a set of rooms that may be inaccessible during execution.