记忆即通信：记忆与信号之间的边界
Memory Is Communication: The Frontier Between Remembering and Signaling
- Alberta Machine Intelligence Institute, University of Alberta（阿尔伯塔大学阿尔伯塔机器智能研究所）
- Network for Applied Technology（应用技术网络）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究探讨受资源限制的智能体如何分配记忆与通信预算以优化决策，提出记忆-信号边界概念，通过参考博弈实验验证了从历史中获更大损失降幅可减少同伴通信的假设。
AI中文摘要：
一个受资源限制的智能体可从自身过往、同伴或两者获取决策所需信息。保留任务相关的历史可减少后续通信，而同伴消息可补充记忆缺失的内容。在两种资源均受限的情况下，智能体应如何分配信息预算？给定固定任务和决策规则，在使用历史与同伴观测的特定规则下，达到性能阈值的记忆与消息率对构成可达区域，我们将其有效边界称为记忆-信号边界。在历史允许任务损失最大降幅相同的条件下，我们假设受资源限制的智能体从历史中获得的损失降幅越大，所需的同伴通信就越少。在初步的参考博弈中，目标重复对应更短的成功消息，而隐藏循环规则带来的可预测性并未缩短消息长度。通过改变记忆与消息率的实验可估算该边界，并在合作任务中验证这一预测。
英文摘要：
A bounded agent may obtain information for a decision from its own past, from peers, or from both sources. Retaining task-relevant history can reduce later communication, while a peer message can supply what memory lacks. Under limits on both resources, how should an agent allocate its information budget? Given a fixed task and decision rule, the memory and message rate pairs attaining a performance threshold form an achievable region under specified rules for using history and peer observations. We call its efficient boundary the remembering--signaling frontier. Across conditions where history permits the same maximum reduction in task loss, we hypothesize that a bounded agent will need less peer communication when it obtains a larger loss reduction from history. In preliminary referential games, target repetition coincided with shorter successful messages, while predictability from a hidden cyclic rule did not shorten them. Experiments varying memory and message rates can estimate the frontier and test this prediction across cooperative tasks.
