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Human beings use compositionality to generalise from past experiences tonovel experiences. We assume a separation of our experiences into fundamentalatomic components that can be recombined in novel ways to support our abilityto engage with novel experiences. We frame this as the ability to learn togeneralise compositionally, and we will refer to behaviours making use of thisability as compositional learning behaviours (CLBs). A central problem tolearning CLBs is the resolution of a binding problem (BP). While it is anotherfeat of intelligence that human beings perform with ease, it is not the casefor state-of-the-art artificial agents. Thus, in order to build artificialagents able to collaborate with human beings, we propose to develop a novelbenchmark to investigate agents' abilities to exhibit CLBs by solving adomain-agnostic version of the BP. We take inspiration from the languageemergence and grounding framework of referential games and propose ameta-learning extension of referential games, entitled Meta-Referential Games,and use this framework to build our benchmark, the Symbolic Behaviour Benchmark(S2B). We provide baseline results and error analysis showing that ourbenchmark is a compelling challenge that we hope will spur the researchcommunity towards developing more capable artificial agents.

Meta-Referential Games to Learn Compositional Learning Behaviours