Several recent works have found the emergence of grounded com-positional language in the communication protocols developed bymostly cooperative multi-agent systems when learned end-to-endto maximize performance on a downstream task. However, humanpopulations learn to solve complex tasks involving communicativebehaviors not only in fully cooperative settings but also in scenar-ios where competition acts as an additional external pressure forimprovement. In this work, we investigate whether competitionfor performance from an external, similar agent team could actas a social influence that encourages multi-agent populations todevelop better communication protocols for improved performance,compositionality, and convergence speed. We start fromTask &Talk, a previously proposed referential game between two coopera-tive agents as our testbed and extend it intoTask, Talk & Compete,a game involving two competitive teams each consisting of twoaforementioned cooperative agents. Using this new setting, we pro-vide an empirical study demonstrating the impact of competitiveinfluence on multi-agent teams. Our results show that an externalcompetitive influence leads to improved accuracy and generaliza-tion, as well as faster emergence of communicative languages thatare more informative and compositional.
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Towards Situated Communication in Multi-Step Interactions: Time is a Key Pressure in Communication Emergence
Enabling efficient communication in artificial agents brings us closer to machines that can cooperate with each other and with human partners. Hand-engineered approaches have substantial limitations, leading to increased interest in methods for communication to emerge autonomously between artificial agents. Most of the research in the field explores unsituated communication in one-step referential tasks. The tasks are not temporally interactive and lack time pressures typically present in natural communication and language learning. In these settings, agents can successfully learn what to communicate but not when or whether to communicate. Here, we extend the literature by assessing emergence of communication between reinforcement learning agents in a temporally interactive, cooperative task of navigating a gridworld environment. We show that, through multi-step interactions, agents develop just-in-time messaging protocols that enable them to successfully solve the task. With memory—which provides flexibility around message timing—agent pairs converge to a look-ahead communication protocol, finding an optimal solution to the task more quickly than without memory. Lastly, we explore situated communication, enabling the acting agent to choose when and whether to communicate. With the opportunity cost of forgoing an action to communicate, the acting agent learns to solicit information sparingly, in line with the Gricean Maxim of quantity. Our results point towards the importance of studying language emergence through situated communication in multi-step interactions.
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- Award ID(s):
- 1837515
- NSF-PAR ID:
- 10471793
- Publisher / Repository:
- Cognitive Science Society, https://escholarship.org/uc/item/61k7486v
- Date Published:
- Journal Name:
- Annual Meeting of the Cognitive Science Society
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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