In this paper, we present foundations of the Socio-physical Model of Activities (SOMA). SOMA represents both the physical as well as the social context of everyday activities. Such tasks seem to be trivial for humans, however, they pose severe problems for artificial agents. For starters, a natural language command requesting something will leave many pieces of information necessary for performing the task unspecified. Humans can solve such problems fast as we reduce the search space by recourse to prior knowledge such as a connected collection of plans that describe how certain goals can be achieved at various levels of abstraction. Rather than enumerating fine-grained physical contexts SOMA sets out to include socially constructed knowledge about the functions of actions to achieve a variety of goals or the roles objects can play in a given situation. As the human cognition system is capable of generalizing experiences into abstract knowledge pieces applicable to novel situations, we argue that both physical and social context need be modeled to tackle these challenges in a general manner. This is represented by the link between the physical and social context in SOMA where relationships are established between occurrences and generalizations of them, which has been demonstrated in several use cases that validate SOMA.

中文翻译：

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自主机器人代理的社会物理活动模型（SOMA）的基础

在本文中，我们介绍了活动的社会物理模型（SOMA）的基础。SOMA代表日常活动的身体和社会背景。这样的任务对人类来说似乎是微不足道的，但是，它们给人工制剂带来了严重的问题。对于初学者来说，请求某项内容的自然语言命令会留下许多未指定的执行任务所需的信息。当我们通过求助于先验知识（例如描述计划如何在各种抽象级别上实现某些目标的相互关联的计划）来减少搜索空间时，人类可以快速解决此类问题。SOMA并未列举详细的物理环境，而是要包括关于为实现各种目标或特定对象在给定情况下可以扮演的角色的功能的社会建构知识。由于人类认知系统能够将经验概括为适用于新情况的抽象知识，因此，我们认为，必须对身体和社会环境进行建模，以一般方式应对这些挑战。这由SOMA的物理环境和社会环境之间的链接表示，其中在它们的出现和概括之间建立了关系，这在验证SOMA的几个用例中得到了证明。我们认为，无论是在自然环境还是在社会环境上，都需要以一种通用的方式来应对这些挑战。这由SOMA的物理环境和社会环境之间的链接表示，其中在它们的出现和概括之间建立了关系，这在验证SOMA的几个用例中得到了证明。我们认为，无论是在自然环境还是在社会环境上，都需要以一种通用的方式来应对这些挑战。这由SOMA的物理环境和社会环境之间的链接表示，其中在它们的出现和概括之间建立了关系，这在验证SOMA的几个用例中得到了证明。