自律ロボットエージェントのための社会・物理活動モデルSOMAの基礎
Foundations of the Socio-physical Model of Activities (SOMA) for Autonomous Robotic Agents
日常活動の物理的・社会的文脈を統合的に表現する知識モデルSOMAを提案し、曖昧な自然言語指示を抽象的な計画知識で補完してロボットがタスクを遂行できる枠組みを示した論文。
著者: Daniel Beßler, Robert Porzel, Mihai Pomarlan, Abhijit Vyas, Sebastian Höffner, Michael Beetz, Rainer Malaka, John Bateman
分類: cs.RO
原文アブストラクト
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.
