Foundations of the Socio-physical Model of Activities (SOMA) for Autonomous Robotic Agents

by   Daniel Beßler, et al.

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.


A Paradigm for Situated and Goal-Driven Language Learning

A distinguishing property of human intelligence is the ability to flexib...

Shaping embodied agent behavior with activity-context priors from egocentric video

Complex physical tasks entail a sequence of object interactions, each wi...

Help Me Explore: Minimal Social Interventions for Graph-Based Autotelic Agents

In the quest for autonomous agents learning open-ended repertoires of sk...

PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception

The ability to perceive and reason about social interactions in the cont...

Do Neural Language Representations Learn Physical Commonsense?

Humans understand language based on the rich background knowledge about ...

Coupling purposes with status-functions in artificial institutions

In multi-agent systems, the agents may have goals that depend on a socia...

NSense: A People-centric, non-intrusive Opportunistic Sensing Tool for Contextualizing Nearness

In the context of social well-being and context awareness several eHealt...