🤖 AI Summary
Existing knowledge representation approaches struggle to capture cooperative affordances in multi-agent social contexts—namely, the possibilities through which agents extend their action capabilities via collaboration. This work introduces, for the first time, an extension of the affordance concept to multi-agent cooperative scenarios by proposing a computable ontological framework that formally defines “cooperative affordances.” Building upon ontology engineering principles, the framework establishes composable and extensible basic representational patterns. By combining these patterns, the system effectively models collaborative interactions ranging from elementary to complex, demonstrating both expressive power and practical utility across diverse social settings.
📝 Abstract
In robotics, the capability of an artificial agent to represent the range of its action possibilities, i.e. affordances, is crucial to understand how it can act on its environment. While functional affordances, which refer to the use of tools and objects, have been broadly studied in knowledge representation, the implications of a social context and the presence of other agents have remained unexplored in this field. Consequently, in the field of social robotics, a multi-agent context enables the agents to engage in new actions that are potentially complementary to their individual capabilities, leading to the perspective of agentexploitation. This work focuses on the concept of cooperative affordance within the realm of social affordances. Cooperative affordances refer to situations where agents interact with each other to extend their action possibilities range. From this definition, this paper proposes a tractable ontological representation of this concept with the aim of making it usable by an artificial agent. Expanding on those elementary patterns, we illustrate the effectiveness of these representations by combining them to depict a diverse range of scenarios.