论文标题
紧凑的信念状态代表任务计划
Compact Belief State Representation for Task Planning
论文作者
论文摘要
概率信念状态域中的任务规划允许在受状态不确定性影响的那些领域中产生复杂而健壮的执行政策。任务计划者的表现依赖于信念状态的表示。但是，随着变量的数量和执行时间的增加，当前的信念状态表示很容易棘手。为了解决这个问题，我们根据信仰替代的笛卡尔产品和工会运营开发了一种新颖的信念状态代表。这两个操作和单个变量分配节点形成了信念状态（AOBS）的无环形图。我们展示了如何采用概率结果的行动，并衡量保持信仰状态的条件的可能性。我们在模拟的远期状态探索中评估了AOBS性能。我们将AOB的大小与以前用来表示信仰状态的二进制决策图（BDD）的大小进行了比较。我们表明，AOBS表示不仅比完整的信念状态要紧凑得多，而且在大多数情况下，它的扩展比BDD更好。
Task planning in a probabilistic belief state domains allows generating complex and robust execution policies in those domains affected by state uncertainty. The performance of a task planner relies on the belief state representation. However, current belief state representation becomes easily intractable as the number of variables and execution time grows. To address this problem, we developed a novel belief state representation based on cartesian product and union operations over belief substates. These two operations and single variable assignment nodes form And-Or directed acyclic graph of Belief State (AOBS). We show how to apply actions with probabilistic outcomes and measure the probability of conditions holding over belief state. We evaluated AOBS performance in simulated forward state space exploration. We compared the size of AOBS with the size of Binary Decision Diagrams (BDD) that were previously used to represent belief state. We show that AOBS representation is not only much more compact than a full belief state but it also scales better than BDD for most of the cases.
