多代理系统学术速递[1.10]
Update！H5支持摘要折叠，体验更佳！点击阅读原文访问arxivdaily.com，涵盖CS|物理|数学|经济|统计|金融|生物|电气领域，更有搜索、收藏等功能！
cs.MA多代理系统，共计3篇
【1】 Elephant-Human Conflict Mitigation: An Autonomous UAV Approach
标题：缓解大象与人类冲突：一种自主无人机方法
链接：https://arxiv.org/abs/2201.02584
备注：None
摘要：Elephant-human conflict (EHC) is one of the major problems in most African
and Asian countries. As humans overutilize natural resources for their
development, elephants' living area continues to decrease; this leads elephants
to invade the human living area and raid crops more frequently, costing
millions of dollars annually. To mitigate EHC, in this paper, we propose an
original solution that comprises of three parts: a compact custom low-power GPS
tag that is installed on the elephants, a receiver stationed in the human
living area that detects the elephants' presence near a farm, and an autonomous
unmanned aerial vehicle (UAV) system that tracks and herds the elephants away
from the farms. By utilizing proportional-integral-derivative controller and
machine learning algorithms, we obtain accurate tracking trajectories at a
real-time processing speed of 32 FPS. Our proposed autonomous system can save
over 68 % cost compared with human-controlled UAVs in mitigating EHC.
【2】 Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation
标题：多议题双边谈判的深度学习策略模板
链接：https://arxiv.org/abs/2201.02455
备注：arXiv admin note: text overlap with arXiv:2009.08302
摘要：We study how to exploit the notion of strategy templates to learn strategies
for multi-issue bilateral negotiation. Each strategy template consists of a set
of interpretable parameterized tactics that are used to decide an optimal
action at any time. We use deep reinforcement learning throughout an
actor-critic architecture to estimate the tactic parameter values for a
threshold utility, when to accept an offer and how to generate a new bid. This
contrasts with existing work that only estimates the threshold utility for
those tactics. We pre-train the strategy by supervision from the dataset
collected using "teacher strategies", thereby decreasing the exploration time
required for learning during negotiation. As a result, we build automated
agents for multi-issue negotiations that can adapt to different negotiation
domains without the need to be pre-programmed. We empirically show that our
work outperforms the state-of-the-art in terms of the individual as well as
social efficiency.
【3】 Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement
标题：基于拍卖的带声誉和贡献度的横向联合学习支付后激励机制设计
链接：https://arxiv.org/abs/2201.02410
摘要：Federated learning trains models across devices with distributed data, while
protecting the privacy and obtaining a model similar to that of centralized ML.
A large number of workers with data and computing power are the foundation of
federal learning. However, the inevitable costs prevent self-interested workers
from serving for free. Moreover, due to data isolation, task publishers lack
effective methods to select, evaluate and pay reliable workers with
high-quality data. Therefore, we design an auction-based incentive mechanism
for horizontal federated learning with reputation and contribution measurement.
By designing a reasonable method of measuring contribution, we establish the
reputation of workers, which is easy to decline and difficult to improve.
Through reverse auctions, workers bid for tasks, and the task publisher selects
workers combining reputation and bid price. With the budget constraint, winning
workers are paid based on performance. We proved that our mechanism satisfies
the individual rationality of the honest worker, budget feasibility,
truthfulness, and computational efficiency.
机器翻译，仅供参考
点击“阅读原文”获取带摘要的学术速递