AI博弈论新突破：编程“内疚感”机制显著提升多智能体合作效率

AI Game Theory: When Artificial Intelligence 'Feels' Guilt (AI博弈论对理性决策者之间战略互动的数学研究，现在正扩展到AI系统之间的交互分析。：当人工智能“感受”内疚时)

Artificial intelligence that 'feels' guilt could lead to more cooperation, according to new research published in the Journal of the Royal Society Interface. This groundbreaking study explores how programming guilt-like mechanisms into AI agents can foster cooperation in social networks, challenging traditional views of AI as purely rational actors.

根据《皇家学会界面杂志》发表的最新研究，能够“感受”内疚的人工智能可能促进更多合作。这项突破性研究探讨了如何在AI智能体中编程类似内疚的机制，以促进社交网络中的合作，挑战了将AI视为纯粹理性行为者的传统观点。

The Evolutionary Role of Emotions in AI (情绪在AI中的进化作用)

Humans have evolved emotions like anger, sadness and gratitude to help us think, interact and build mutual trust. Advanced AI could do the same. In populations of simple software agents (like characters in "The Sims" but much, much simpler), having "guilt" can be a stable strategy that benefits them and increases cooperation, researchers report July 30 in Journal of the Royal Society Interface.

人类进化出了愤怒、悲伤和感激等情绪，帮助我们思考、互动和建立相互信任。先进的人工智能也可以做到同样的事情。研究人员在7月30日的《皇家学会界面杂志》中报告称，在简单的软件智能体群体中（类似于《模拟人生》中的角色，但要简单得多），拥有“内疚”可以成为一种稳定的策略，使它们受益并增加合作。

Game Theory Framework: Iterated Prisoner's Dilemma (博弈论对理性决策者之间战略互动的数学研究，现在正扩展到AI系统之间的交互分析。框架：迭代囚徒困境博弈论中的经典模型，模拟两个玩家在重复互动中合作与背叛的权衡，是研究长期合作演化的关键工具。)

The agents played a two-player game with their neighbors called iterated prisoner's dilemma. The game has roots in game theory, a mathematical framework for analyzing multiple decision makers' choices based on their preferences and individual strategies. On each turn, each player "cooperates" (plays nice) or "defects" (acts selfishly). In the short term, you win the most points by defecting, but that tends to make your partner start defecting, so everyone is better off cooperating in the long run.

智能体与邻居玩一个名为迭代囚徒困境博弈论中的经典模型，模拟两个玩家在重复互动中合作与背叛的权衡，是研究长期合作演化的关键工具。的双人游戏。该游戏源于博弈论对理性决策者之间战略互动的数学研究，现在正扩展到AI系统之间的交互分析。，这是一个基于决策者偏好和个人策略分析多个决策者选择的数学框架。在每一轮中，每个玩家“合作”（表现良好）或“背叛”（自私行事）。从短期来看，通过背叛可以获得最多的分数，但这往往会导致你的伙伴开始背叛，因此从长远来看，合作对每个人都更有利。

Programming Guilt into AI Agents (将内疚编程到AI智能体中)

Emotions are not just subjective feelings but bundles of cognitive biases, physiological responses and behavioral tendencies. When we harm someone, we often feel compelled to pay a penance, perhaps as a signal to others that we won't offend again. This drive for self-punishment can be called guilt, and it's how the researchers programmed it into their agents.

情绪不仅仅是主观感受，而是认知偏见、生理反应和行为倾向的集合。当我们伤害某人时，我们常常感到必须进行补偿，这可能是向他人表明我们不会再冒犯的信号。这种自我惩罚的驱动力可以称为内疚，研究人员正是这样将其编程到他们的智能体中的。

The DGCS Strategy: Guilt as Cooperation Mechanism (DGCS策略一种基于内疚的合作策略，智能体在背叛后自我施加惩罚，但仅在确认互动伙伴也支付类似成本时才激活该机制。：内疚作为合作机制)

In one strategy, nicknamed DGCS for technical reasons, the agent felt guilt after defecting, meaning that it gave up points until it cooperated again. Critically, the AI agent felt guilt (lost points) only if it received information that its partner was also paying a guilt price after defecting. This prevented the agent from being a patsy, thus enforcing cooperation in others.

在一种策略中（出于技术原因昵称为DGCS），智能体在背叛后感到内疚，这意味着它会放弃分数直到再次合作。关键的是，AI智能体只有在收到信息表明其伙伴在背叛后也在支付内疚代价时才会感到内疚（失去分数）。这防止了智能体成为容易受骗的人，从而强制他人合作。

Simulation Results and Findings (模拟结果与发现)

The researchers ran several simulations with different settings and social network structures. In each, the 900 players were each assigned one of six strategies defining their tendency to defect and to feel and respond to guilt. After each turn, agents could copy a neighbor's strategy, with a probability of imitation based on neighbors' cumulative score.

研究人员使用不同的设置和社交网络结构进行了多次模拟。在每次模拟中，900名玩家每人被分配了六种策略之一，这些策略定义了他们的背叛倾向以及对内疚的感受和反应。每轮结束后，智能体可以复制邻居的策略，模仿概率基于邻居的累积分数。

Practical Implications for AI Development (对AI发展的实际意义)

We may want to program the capacity for guilt or other emotions into AIs. "Maybe it's easier to trust when you have a feeling that the agent also thinks in the same way that you think," says Theodor Cimpeanu, a computer scientist at the University of Stirling in Scotland. We may also witness emotions — at least the functional aspects, even if not the conscious ones — emerge on their own in groups of AIs if they can mutate or self-program.

我们可能希望将内疚或其他情绪的能力编程到AI中。苏格兰斯特灵大学的计算机科学家西奥多·辛佩亚努表示：“当你感觉智能体也以你思考的方式思考时，可能更容易信任。”如果AI群体能够变异或自我编程，我们也可能见证情绪——至少是功能方面，即使不是意识方面——在AI群体中自行出现。

Limitations and Caveats (局限性与注意事项)

According to industry reports, there are important caveats to consider. First, simulations embody many assumptions, so one can't draw strong conclusions from a single study. Second, it's hard to map simulations like these to the real world. What counts as a verifiable cost for an AI, besides paying actual money from a coffer?

根据行业报告，需要考虑重要的注意事项。首先，模拟包含许多假设，因此不能从单一研究中得出强有力的结论。其次，很难将此类模拟映射到现实世界。除了从金库支付实际资金外，什么才算AI的可验证成本？

Future Research Directions (未来研究方向)

As AIs proliferate, they could comprehend the cold logic to human warmth. Future research should explore how guilt-like mechanisms can be implemented in real-world AI systems, particularly in multi-agent environments where cooperation is essential for achieving complex goals.

随着AI的普及，它们可以理解人类温暖的冷酷逻辑。未来的研究应探索如何在现实世界的AI系统中实施类似内疚的机制，特别是在合作对于实现复杂目标至关重要的多智能体环境中。

Frequently Asked Questions (常见问题)

What is game theory in the context of AI? (AI背景下的博弈论对理性决策者之间战略互动的数学研究，现在正扩展到AI系统之间的交互分析。是什么？)

博弈论对理性决策者之间战略互动的数学研究，现在正扩展到AI系统之间的交互分析。是研究多个决策者在相互依赖情况下如何做出选择的数学框架。在AI中，它用于分析智能体之间的战略互动，预测行为结果并设计促进合作的机制。
How does guilt programming work in AI agents? (内疚编程在AI智能体中如何工作？)

内疚编程涉及创建一种机制，当AI智能体采取自私行为（背叛）时，它会自我施加成本或惩罚。这种机制只有在智能体收到信息表明其互动伙伴也在支付类似成本时才会激活，防止单方面牺牲并促进相互合作。
What is the iterated prisoner's dilemma? (什么是迭代囚徒困境博弈论中的经典模型，模拟两个玩家在重复互动中合作与背叛的权衡，是研究长期合作演化的关键工具。？)

迭代囚徒困境博弈论中的经典模型，模拟两个玩家在重复互动中合作与背叛的权衡，是研究长期合作演化的关键工具。是博弈论对理性决策者之间战略互动的数学研究，现在正扩展到AI系统之间的交互分析。中的经典模型，两个玩家多次重复进行囚徒困境游戏。它模拟了长期关系中合作与背叛之间的权衡，是研究信任、互惠和合作演化的关键工具。
Why is cooperation important in multi-agent AI systems? (为什么合作在多智能体AI系统中很重要？)

在多智能体系统由多个相互协作的AI智能体组成的系统，能够处理复杂任务并通过智能体间的通信和协调实现更高级的自动化功能。中，合作使智能体能够协调行动、共享资源并实现个体无法单独完成的集体目标。缺乏合作可能导致资源浪费、目标冲突和系统效率低下。
What are the practical applications of guilt-based AI cooperation? (基于内疚的AI合作有哪些实际应用？)

实际应用包括：自动驾驶车辆协调、分布式机器人系统、智能电网管理、金融交易算法以及需要多个AI实体协作的任何场景，其中信任和可靠合作至关重要。