Behaviour recognition of planning agents using Behaviour Trees

Research on AI is more and more focusing towards explainable technology that accounts for the outcomes of programs and products. One important aspect in this direction is the ability to recognize the behaviour patterns of our application in order to make sensible and informed decisions. In this work, we aim to uncover the behaviour of a planning agent by means of Behaviour Trees (BTs), a flexible and controllable mathematical model of plan execution used to describe flows between tasks in a modular fashion. We analyze the behaviour of a planning agent when solving a simple logistics problem by comparing the agent’s plan with the plans resulting from various BTs, each representing a different behaviour. We propose different distance metrics as a similarity measurement between plans and we evaluate their accuracy at identifying similar behaviours.