Planning to Chronicle
Planning to Chronicle
复制标题
计划编年史
DOI：
10.1007/978-3-030-66723-8_17
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
O'Kane, Jason M.
中科院分区：
文献类型：
--
作者：
Rahmani, Hazhar;Shell, Dylan A.;O'Kane, Jason M.
一类重要的应用需要机器人监控，仔细检查或记录不确定的时间延长过程的演变。这种情况导致了一系列有趣的规划问题，其中机器人在它所看到的方面受到限制，因此必须选择关注什么。这种设置的显著特点是，机器人可以通过传感器影响它捕获的内容，但对进化过程没有因果关系。因此，机器人的目标是观察潜在的过程，并产生一个“编年史”的发生的事件，受到目标规范的各种事件序列，可能是感兴趣的。本文探讨了这些问题的变种时，机器人的目的是收集的意见，以满足其顺序结构丰富的规格。我们研究这一类的问题，通过一个变种的隐马尔可夫模型的随机过程建模，并指定感兴趣的事件序列作为一个正规的语言，开发一个词汇表的“变应子”，使复杂的要求来表达。在不同的假设下收集的信息的事件模型，我们制定和解决不同的规划问题。其核心思想是在事件模型和规范自动机之间构造一个产品。本文报告和比较性能指标，借鉴一些小的案例研究，深入分析模拟。
An important class of applications entails a robot monitoring, scrutinizing, or recording the evolution of an uncertain time-extended process. This sort of situation leads to an interesting family of planning problems in which the robot is limited in what it sees and must, thus, choose what to pay attention to. The distinguishing characteristic of this setting is that the robot has influence over what it captures via its sensors, but exercises no causal authority over the evolving process. As such, the robot’s objective is to observe the underlying process and to produce a ‘chronicle’ of occurrent events, subject to a goal specification of the sorts of event sequences that may be of interest. This paper examines variants of such problems when the robot aims to collect sets of observations to meet a rich specification of their sequential structure. We study this class of problems by modeling a stochastic process via a variant of a hidden Markov model, and specify the event sequences of interest as a regular language, developing a vocabulary of ‘mutators’ that enable sophisticated requirements to be expressed. Under different suppositions about the information gleaned about the event model, we formulate and solve different planning problems. The core underlying idea is the construction of a product between the event model and a specification automaton. The paper reports and compares performance metrics by drawing on some small case studies analyzed in depth in simulation.