RecBayes

Abstract

Hybrid intelligence scenarios - where artificial agents must collaborate with pre-existing human teams in the real world - require agents capable of joining those teams on-the-fly, without prior coordination or communication, and without being able to directly observe the internal states or intended actions of their human teammates. Ad hoc teamwork formalises precisely this challenge, yet current approaches remain constrained by requirements that are fundamentally incompatible with human-AI teaming: either they require access to privileged information such as the full environment state or the real-time actions of teammates [Barrett et al., 2017, Gu et al., 2021, Rahman et al., 2023], or they are limited to environments small enough to be tabularly modelled as partially observable Markov decision processes [Ribeiro et al., 2023a]. In this paper we introduce RecBayes, a recurrent Bayesian approach to ad hoc teamwork that lifts both constraints simultaneously. RecBayes identifies teammates and the tasks they are performing using only partial observations of the environment, never requiring access to teammate actions or environment states at any stage, and scales to domains with arbitrarily large state and observation spaces. We demonstrate its effectiveness in two benchmark multi-agent domains with up to 1M states and 2125 possible observations, showing that RecBayes correctly identifies teams and tasks from partial observations alone, and does so efficiently enough to assist those teams in completing their objectives. These properties make RecBayes a principled step toward ad hoc agents deployable in real hybrid human-AI teams.