Fingerprint multilateration for automatically classifying evolved Prisoner's Dilemma agents

We present a novel tool for automatically analyzing evolved Prisoner's Dilemma agents, based on combining two existing techniques: fingerprinting, which turns a strategy into a representation-independent functional summary of its behaviour, and multilateration, which finds the location of a point in space using measured distances to a known set of anchor points. We take as our anchor points the space of 2-state deterministic transducers; using this, we can emplace an arbitrary strategy into 7-dimensional real space by computing numerical integrals and solving a set of linear equations, which is sufficiently fast to be doable online. Several new aspects of evolutionary behaviour, such as the velocity of evolution and population diversity, can now be directly quantified.