Learning in public goods games: the effects of uncertainty and communication on cooperation.

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Neural Computing & Applications Pub Date : 2025-01-01 Epub Date: 2025-01-30 DOI:10.1007/s00521-024-10530-6
Nicole Orzan, Erman Acar, Davide Grossi, Roxana Rădulescu
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引用次数: 0

Abstract

Communication is a widely used mechanism to promote cooperation in multi-agent systems. In the field of emergent communication, agents are typically trained in specific environments: cooperative, competitive or mixed-motive. Motivated by the idea that real-world settings are characterized by incomplete information and that humans face daily interactions under a wide spectrum of incentives, we aim to explore the role of emergent communication when simultaneously exploited across all these contexts. In this work, we pursue this line of research by focusing on social dilemmas. To do this, we developed an extended version of the Public Goods Game, which allows us to train independent reinforcement learning agents simultaneously in different scenarios where incentives are (mis)aligned to various extents. Additionally, agents experience uncertainty in terms of the alignment of their incentives with those of others. We equip agents with the ability to learn a communication policy and study the impact of emergent communication in the face of uncertainty among agents. Our findings show that in settings where all agents have the same level of uncertainty, communication can enhance the cooperation of the whole group. However, in cases of asymmetric uncertainty, the agents that do not face uncertainty learn to use communication to deceive and exploit their uncertain peers.

公共物品博弈中的学习:不确定性和沟通对合作的影响。
通信是多智能体系统中广泛使用的促进合作的机制。在紧急通信领域,智能体通常在特定的环境中训练:合作、竞争或混合动机。考虑到现实环境的特点是信息不完整,以及人类在各种激励下面临日常互动,我们的目标是探索在所有这些环境中同时利用紧急沟通的作用。在这项工作中,我们通过关注社会困境来追求这一研究方向。为此,我们开发了一个扩展版本的公共物品博弈,它允许我们在不同的激励(错误)对齐到不同程度的情况下同时训练独立的强化学习代理。此外,代理体会到他们的激励与他人的激励的一致性方面的不确定性。我们赋予agent学习沟通策略的能力,并研究agent之间面对不确定性时紧急沟通的影响。我们的研究结果表明,在所有代理人都具有相同程度的不确定性的情况下,沟通可以增强整个群体的合作。然而,在不对称不确定性的情况下,不面临不确定性的代理学习使用通信来欺骗和利用他们不确定的同伴。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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