A polynomial time computable metric between point sets

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Learning Theory for Distribution Regression (2014, background): “There also exist other (semi)metric constructions on points sets (Eiter and Mannila, 1997; Ramon and Bruynooghe, 2001).”
Relational Instance Based Regression for Relational Reinforcement Learning (2003, methodology): “To calculate the distance between the two states, regard each state (with renamed blocks) as a set of stacks and calculate the distance between these two sets using the matching-distance between sets based on the distance between the stacks of blocks (Ramon & Bruynooghe, 2001).”
A survey on graph kernels (2019, methodology): “Such fingerprints are typically compared using similarity measures such as the Tanimoto coefficient , which are closely related to kernels (Ralaivola et al. 2005).”
Relational Reinforcement Learning An Overview (2004, background): “The distance used needs to be able to cope with relational representations of states and actions and can be either a general purpose rst order distance (Sebag, 19977 Ramon & Bruynooghe, 2001) or an application-speciic one, which can usually be computed more eeciently.”