面向目标的有效通信的推拉集成更新模型
Integrated Push-and-Pull Update Model for Goal-Oriented Effective Communication
AI总结：
该研究面向目标有效通信决策，集成推送与拉取模式构建推拉更新模型，采用GoE指标衡量有效性，推导双侧效应感知策略，实验证明该模型及策略在效率和有效性上均优于传统单一模式及无效应感知策略。
摘要：
本文研究面向目标的有效通信决策问题。我们考虑一个端到端状态更新系统：感知智能体（SA）观测信源，生成更新并发送给执行智能体（AA），而AA在终端采取行动以完成目标。我们将基于推送和基于拉取的更新通信模型相集成，得到推拉模型，该模型允许SA侧的传输控制器决定向AA推送更新，同时AA侧的查询控制器可在特定时刻发起查询以拉取更新。为衡量通信有效性，我们采用有效性等级（GoE）指标，该指标将更新的新鲜度、有用性以及行动的及时性作为定性属性纳入考量。随后我们推导了效应感知策略，在产生的成本约束下，最大化更新有效性的期望折扣和。SA侧的效应感知策略会考虑通信更新在终端的潜在有效性，而AA侧的效应感知策略则会考量信源的概率演化以及所生成更新的重要性。我们的结果表明，所提出的推拉模型在效率和有效性两方面均优于仅基于推送或仅基于拉取的更新模型。此外，与在单侧或双侧智能体上采用周期性和/或概率性的无效应感知策略相比，在双侧智能体上均使用效应感知策略能提升有效性。
英文摘要：
This paper studies decision-making for goal-oriented effective communication. We consider an end-to-end status update system where a sensing agent (SA) observes a source, generates and transmits updates to an actuation agent (AA), while the AA takes actions to accomplish a goal at the endpoint. We integrate the push- and pull-based update communication models to obtain a push-and-pull model, which allows the transmission controller at the SA to decide to push an update to the AA and the query controller at the AA to pull updates by raising queries at specific time instances. To gauge effectiveness, we utilize a grade of effectiveness (GoE) metric incorporating updates' freshness, usefulness, and timeliness of actions as qualitative attributes. We then derive effect-aware policies to maximize the expected discounted sum of updates' effectiveness subject to induced costs. The effect-aware policy at the SA considers the potential effectiveness of communicated updates at the endpoint, while at the AA, it accounts for the probabilistic evolution of the source and importance of generated updates. Our results show the proposed push-and-pull model outperforms models solely based on push- or pull-based updates both in terms of efficiency and effectiveness. Additionally, using effect-aware policies at both agents enhances effectiveness compared to periodic and/or probabilistic effect-agnostic policies at either or both agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
