Affordance-Based Similarity Measurement for Entity Types

When interacting with the environment subjects tend to classify entities with respect to the functionalities they offer for solving specific tasks. The theory of affordances accounts for this agent-environment interaction, while similarity allows for measuring resemblances among entities and entity types. Most similarity measures separate the similarity estimations from the context—the agents, their tasks and environment—and focus on structural and static descriptions of the compared entities and types. This paper argues that an affordance-based representation of the context in which similarity is measured, makes the estimations situation-aware and therefore improves their quality. It also leads to a better understanding of how unfamiliar entities are grouped together to ad-hoc categories, which has not been explained in terms of similarity yet. We propose that types of entities are the more similar the more common functionalities their instances afford an agent. This paper presents a framework for representing affordances, which allows determining similarity between them. The approach is demonstrated through a planning task.