Riegler Bente, Polani Daniel, Steuber Volker
Sepia Lab, Adaptive Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom.
Biocomputation Research Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom.
Front Robot AI. 2021 Apr 12;8:535158. doi: 10.3389/frobt.2021.535158. eCollection 2021.
The importance of embodiment for effective robot performance has been postulated for a long time. Despite this, only relatively recently concrete quantitative models were put forward to characterize the advantages provided by a well-chosen embodiment. We here use one of these models, based on the concept of relevant information, to identify in a minimalistic scenario how and when embodiment affects the decision density. Concretely, we study how embodiment affects information costs when, instead of atomic actions, scripts are introduced, that is, predefined action sequences. Their inclusion can be treated as a straightforward extension of the basic action space. We will demonstrate the effect on informational decision cost of utilizing scripts vs. basic actions using a simple navigation task. Importantly, we will also employ a world with "mislabeled" actions, which we will call a "twisted" world. This is a model which had been used in an earlier study of the influence of embodiment on decision costs. It will turn out that twisted scenarios, as opposed to well-labeled ("embodied") ones, are significantly more costly in terms of relevant information. This cost is further worsened when the agent is forced to lower the decision density by employing scripts (once a script is triggered, no decisions are taken until the script has run to its end). This adds to our understanding why well-embodied (interpreted in our model as well-labeled) agents should be preferable, in a quantifiable, objective sense.
长期以来，人们一直假定具身性对于机器人有效执行任务很重要。尽管如此，直到最近才提出具体的定量模型来描述精心选择的具身性所带来的优势。我们在此使用基于相关信息概念的其中一个模型，在一个极简场景中确定具身性如何以及何时影响决策密度。具体而言，我们研究当引入脚本（即预定义的动作序列）而非原子动作时，具身性如何影响信息成本。它们的纳入可被视为基本动作空间的直接扩展。我们将通过一个简单的导航任务来展示使用脚本与基本动作对信息决策成本的影响。重要的是，我们还将采用一个具有“错误标记”动作的世界，我们将其称为“扭曲”世界。这是一个在早期关于具身性对决策成本影响的研究中使用过的模型。结果将表明，与标记良好（“具身化”）的场景相比，扭曲场景在相关信息方面的成本要高得多。当智能体被迫通过使用脚本降低决策密度时（一旦触发脚本，在脚本运行结束之前不进行任何决策），这种成本会进一步恶化。这加深了我们对于为什么在可量化、客观的意义上，具身性良好（在我们的模型中解释为标记良好）的智能体更可取的理解。
