Action Selection for Transparent Planning
Action Selection for Transparent Planning
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发表时间：
2018-07
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通讯作者：
Aleck M. MacNally;N. Lipovetzky;Miquel Ramírez;A. Pearce
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作者：
Aleck M. MacNally;N. Lipovetzky;Miquel Ramírez;A. Pearce
我们引入一种新颖的框架，通过适时恰当地选择并执行行动来形式化和解决透明规划任务。透明规划任务被定义为这样一种任务：智能体的目标是向观察者传达其真实目标，从而使其意图和行动选择透明化。我们将这些任务正式定义并建模为目标部分可观察马尔可夫决策过程（Goal POMDPs），其中状态空间是世界状态和一组给定假设目标的笛卡尔积。行动效果在问题的世界状态中是确定性的，但在观察者的信念中是概率性的。转移概率是通过调用一种基于模型的规划识别算法获得的，我们将其称为观察者刻板印象。我们通过在线规划提出一种行动选择策略，该策略寻求能够快速将所追求的目标传达给被假定符合给定刻板印象的观察者的行动。为了使运行时间可行，我们提出一种新颖的基于模型的规划识别算法，它对著名的概率规划识别方法进行了良好的近似。经过在多种不同领域和三种不同观察者刻板印象上进行评估，结果表明所得到的在线规划器比纯粹以目标为导向的规划器能更快地传达目标信息。
We introduce a novel framework to formalize and solve transparent planning tasks by executing actions selected in a suitable and timely fashion. A transparent planning task is defined as a task where the objective of the agent is to communicate its true goal to observers, thereby making its intentions and its action selection transparent. We formally define and model these tasks as Goal POMDPs where the state space is the Cartesian product of the states of the world and a given set of hypothetical goals. Action effects are deterministic in the world states of the problem but probabilistic in the observer's beliefs. Transition probabilities are obtained from making a call to a model-based plan recognition algorithm, which we refer to as an observer stereotype. We propose an action selection strategy via on-line planning that seeks actions to quickly convey the goal being pursued to an observer assumed to fit a given stereotype. In order to keep run-times feasible, we propose a novel model-based plan recognition algorithm that approximates well-known probabilistic plan recognition methods. The resulting on-line planner, after being evaluated over a diverse set of domains and three different observer stereotypes, is found to convey goal information faster than purely goal-directed planners.