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首页> 外文会议>International Conference on Advanced Information Systems Engineering;International on Business Process Modeling, Development and Support;International Conference on Evaluation and Modeling Methods for Systems Analysis and Development >Automated Planning for Supporting Knowledge-Intensive Processes
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Automated Planning for Supporting Knowledge-Intensive Processes

机译：自动规划以支持知识密集型流程

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Knowledge-intensive Processes (KiPs) are processes characterized by high levels of unpredictability and dynamism. Their process structure may not be known before their execution. One way to cope with this uncertainty is to defer decisions regarding the process structure until run time. In this paper, we consider the definition of the process structure as a planning problem. Our approach uses automated planning techniques to generate plans that define process models according to the current context. The generated plan model relies on a metamodel called METAKIP that represents the basic elements of KiPs. Our solution explores Markov Decision Processes (MDP) to generate plan models. This technique allows uncertainty representation by defining state transition probabilities, which gives us more flexibility than traditional approaches. We construct an MDP model and solve it with the help of the PRISM model-checker. The solution is evaluated by means of a proof of concept in the medical domain which reveals the feasibility of our approach.
机译：知识密集型流程（KiP）是具有高度不可预测性和动态性的流程。在执行之前可能不知道它们的过程结构。应对这种不确定性的一种方法是将有关流程结构的决定推迟到运行时为止。在本文中，我们将流程结构的定义视为计划问题。我们的方法使用自动计划技术来生成计划，这些计划根据当前上下文定义流程模型。生成的计划模型依赖于称为METAKIP的元模型，该元模型表示KiP的基本元素。我们的解决方案探索了马尔可夫决策过程（MDP）以生成计划模型。通过定义状态转移概率，该技术允许不确定性表示，这比传统方法具有更大的灵活性。我们构建了一个MDP模型，并在PRISM模型检查器的帮助下对其进行了解决。该解决方案通过医学领域的概念验证进行评估，这表明了我们方法的可行性。

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