ON THE CRITICAL ROLE OF CONVENTIONS IN ADAPTIVE HUMAN-AI COLLABORATION

Abstract

Humans can quickly adapt to new partners in collaborative tasks (e.g. playing basketball), because they understand which fundamental skills of the task (e.g. how to dribble, how to shoot) carry over across new partners. Humans can also quickly adapt to similar tasks with the same partners by carrying over conventions that they have developed (e.g. raising hand signals pass the ball), without learning to coordinate from scratch. To collaborate seamlessly with humans, AI agents should adapt quickly to new partners and new tasks as well. However, current approaches have not attempted to distinguish between the complexities intrinsic to a task and the conventions used by a partner, and more generally there has been little focus on leveraging conventions for adapting to new settings. In this work, we propose a learning framework that teases apart rule-dependent representation from convention-dependent representation in a principled way. We show that, under some assumptions, our rule-dependent representation is a sufficient statistic of the distribution over best-response strategies across partners. Using this separation of representations, our agents are able to adapt quickly to new partners, and to coordinate with old partners on new tasks in a zero-shot manner. We experimentally validate our approach on three collaborative tasks varying in complexity: a contextual multi-armed bandit, a block placing task, and the card game Hanabi.

1. INTRODUCTION

Humans collaborate well together in complex tasks by adapting to each other through repeated interactions. What emerges from these repeated interactions is shared knowledge about the interaction history. We intuitively refer to this shared knowledge as conventions. Convention formation helps explain why teammates collaborate better than groups of strangers, and why friends develop lingo incomprehensible to outsiders. The notion of conventions has been studied in language (Clark & Wilkes-Gibbs, 1986; Clark, 1996; Hawkins et al., 2017; Khani et al., 2018) and also alluded to in more general multiagent collaborative tasks (Boutilier, 1996; Stone et al., 2010; Foerster et al., 2019; Carroll et al., 2019; Lerer & Peysakhovich, 2019; Hu et al., 2020) . For example, Foerster et al. ( 2019) trained agents to play the card game Hanabi, and noted the emergent convention that "hinting for red or yellow indicates that the newest card of the other player is playable". One established characterization of a convention that is commonly used (Boutilier, 1996; Hawkins et al., 2017; Lerer & Peysakhovich, 2019 ) is an arbitrary solution to a recurring coordination problem (Lewis, 1969) . A convention is thus one of many possible solutions that a group of partners happens to converge to. This is in contrast to problem solutions that are enforced by the rule constraints, and would have arisen no matter how the partners collaborated and what behavior they converged to. Success in a collaborative task typically involves learning both types of knowledge, which we will refer to as convention-dependent and rule-dependent behavior. The distinction between these two types of behavior has been studied extensively in the linguistic literature (Franke et al.; Brochhagen, 2020) . In this work, we focus on the less-explored setting of implicit communication, and provide a concrete approach to learn representations for these two different types of behavior. In the context of multi-agent or human-AI collaboration, we would like our AI agents to adapt quickly to new partners. AI agents should be able to flexibly adjust their partner-specific convention-1

