arXiv - CS - Multiagent Systems最新文献_第6页

Multi-Agent Based Simulation for Decentralized Electric Vehicle Charging Strategies and their Impacts 基于多代理的分散式电动汽车充电策略及其影响模拟

arXiv - CS - Multiagent Systems Pub Date : 2024-08-20 DOI: arxiv-2408.10790

Kristoffer Christensen, Bo Nørregaard Jørgensen, Zheng Grace Ma

引用次数: 0

Athena: Safe Autonomous Agents with Verbal Contrastive Learning 雅典娜：具有语言对比学习能力的安全自主机器人

arXiv - CS - Multiagent Systems Pub Date : 2024-08-20 DOI: arxiv-2408.11021

Tanmana Sadhu, Ali Pesaranghader, Yanan Chen, Dong Hoon Yi

{"title":"Athena: Safe Autonomous Agents with Verbal Contrastive Learning","authors":"Tanmana Sadhu, Ali Pesaranghader, Yanan Chen, Dong Hoon Yi","doi":"arxiv-2408.11021","DOIUrl":"https://doi.org/arxiv-2408.11021","url":null,"abstract":"Due to emergent capabilities, large language models (LLMs) have been utilized\u0000as language-based agents to perform a variety of tasks and make decisions with\u0000an increasing degree of autonomy. These autonomous agents can understand\u0000high-level instructions, interact with their environments, and execute complex\u0000tasks using a selection of tools available to them. As the capabilities of the\u0000agents expand, ensuring their safety and trustworthiness becomes more\u0000imperative. In this study, we introduce the Athena framework which leverages\u0000the concept of verbal contrastive learning where past safe and unsafe\u0000trajectories are used as in-context (contrastive) examples to guide the agent\u0000towards safety while fulfilling a given task. The framework also incorporates a\u0000critiquing mechanism to guide the agent to prevent risky actions at every step.\u0000Furthermore, due to the lack of existing benchmarks on the safety reasoning\u0000ability of LLM-based agents, we curate a set of 80 toolkits across 8 categories\u0000with 180 scenarios to provide a safety evaluation benchmark. Our experimental\u0000evaluation, with both closed- and open-source LLMs, indicates verbal\u0000contrastive learning and interaction-level critiquing improve the safety rate\u0000significantly.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Agent Based Simulation for Investigating Centralized Charging Strategies and their Impact on Electric Vehicle Home Charging Ecosystem 基于多代理的模拟研究集中充电策略及其对电动汽车家庭充电生态系统的影响

arXiv - CS - Multiagent Systems Pub Date : 2024-08-20 DOI: arxiv-2408.10773

Kristoffer Christensen, Bo Nørregaard Jørgensen, Zheng Grace Ma

{"title":"Multi-Agent Based Simulation for Investigating Centralized Charging Strategies and their Impact on Electric Vehicle Home Charging Ecosystem","authors":"Kristoffer Christensen, Bo Nørregaard Jørgensen, Zheng Grace Ma","doi":"arxiv-2408.10773","DOIUrl":"https://doi.org/arxiv-2408.10773","url":null,"abstract":"This paper addresses the critical integration of electric vehicles (EVs) into\u0000the electricity grid, which is essential for achieving carbon neutrality by\u00002050. The rapid increase in EV adoption poses significant challenges to the\u0000existing grid infrastructure, particularly in managing the increasing\u0000electricity demand and mitigating the risk of grid overloads. Centralized EV\u0000charging strategies are investigated due to their potential to optimize grid\u0000stability and efficiency, compared to decentralized approaches that may\u0000exacerbate grid stress. Utilizing a multi-agent based simulation model, the\u0000study provides a realistic representation of the electric vehicle home charging\u0000ecosystem in a case study of Strib, Denmark. The findings show that the\u0000Earliest-deadline-first and Round Robin perform best with 100% EV adoption in\u0000terms of EV user satisfaction. The simulation considers a realistic adoption\u0000curve, EV charging strategies, EV models, and driving patterns to capture the\u0000full ecosystem dynamics over a long-term period with high resolution (hourly).\u0000Additionally, the study offers detailed load profiles for future distribution\u0000grids, demonstrating how centralized charging strategies can efficiently manage\u0000grid loads and prevent overloads.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DBHP: Trajectory Imputation in Multi-Agent Sports Using Derivative-Based Hybrid Prediction DBHP：利用基于派生的混合预测在多代理运动中进行轨迹推断

arXiv - CS - Multiagent Systems Pub Date : 2024-08-20 DOI: arxiv-2408.10878

Hanjun Choi, Hyunsung Kim, Minho Lee, Chang-Jo Kim, Jinsung Yoon, Sang-Ki Ko

{"title":"DBHP: Trajectory Imputation in Multi-Agent Sports Using Derivative-Based Hybrid Prediction","authors":"Hanjun Choi, Hyunsung Kim, Minho Lee, Chang-Jo Kim, Jinsung Yoon, Sang-Ki Ko","doi":"arxiv-2408.10878","DOIUrl":"https://doi.org/arxiv-2408.10878","url":null,"abstract":"Many spatiotemporal domains handle multi-agent trajectory data, but in\u0000real-world scenarios, collected trajectory data are often partially missing due\u0000to various reasons. While existing approaches demonstrate good performance in\u0000trajectory imputation, they face challenges in capturing the complex dynamics\u0000and interactions between agents due to a lack of physical constraints that\u0000govern realistic trajectories, leading to suboptimal results. To address this\u0000issue, the paper proposes a Derivative-Based Hybrid Prediction (DBHP) framework\u0000that can effectively impute multiple agents' missing trajectories. First, a\u0000neural network equipped with Set Transformers produces a naive prediction of\u0000missing trajectories while satisfying the permutation-equivariance in terms of\u0000the order of input agents. Then, the framework makes alternative predictions\u0000leveraging velocity and acceleration information and combines all the\u0000predictions with properly determined weights to provide final imputed\u0000trajectories. In this way, our proposed framework not only accurately predicts\u0000position, velocity, and acceleration values but also enforces the physical\u0000relationship between them, eventually improving both the accuracy and\u0000naturalness of the predicted trajectories. Accordingly, the experiment results\u0000about imputing player trajectories in team sports show that our framework\u0000significantly outperforms existing imputation baselines.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-agent based modeling for investigating excess heat utilization from electrolyzer production to district heating network 基于多代理的建模，用于调查从电解槽生产到区域供热网络的多余热量利用情况

arXiv - CS - Multiagent Systems Pub Date : 2024-08-20 DOI: arxiv-2408.10783

Kristoffer Christensen, Bo Nørregaard Jørgensen, Zheng Grace Ma

引用次数: 0

Analyzing the Impact of Electric Vehicles on Local Energy Systems using Digital Twins 利用数字孪生分析电动汽车对当地能源系统的影响

arXiv - CS - Multiagent Systems Pub Date : 2024-08-20 DOI: arxiv-2408.10763

Daniel René Bayer, Marco Pruckner

引用次数: 0

MegaAgent: A Practical Framework for Autonomous Cooperation in Large-Scale LLM Agent Systems MegaAgent：大型 LLM 代理系统中自主合作的实用框架

arXiv - CS - Multiagent Systems Pub Date : 2024-08-19 DOI: arxiv-2408.09955

Qian Wang, Tianyu Wang, Qinbin Li, Jingsheng Liang, Bingsheng He

{"title":"MegaAgent: A Practical Framework for Autonomous Cooperation in Large-Scale LLM Agent Systems","authors":"Qian Wang, Tianyu Wang, Qinbin Li, Jingsheng Liang, Bingsheng He","doi":"arxiv-2408.09955","DOIUrl":"https://doi.org/arxiv-2408.09955","url":null,"abstract":"With the emergence of large language models (LLMs), LLM-powered multi-agent\u0000systems (LLM-MA systems) have been proposed to tackle real-world tasks.\u0000However, their agents mostly follow predefined Standard Operating Procedures\u0000(SOPs) that remain unchanged across the whole interaction, lacking autonomy and\u0000scalability. Additionally, current solutions often overlook the necessity for\u0000effective agent cooperation. To address the above limitations, we propose\u0000MegaAgent, a practical framework designed for autonomous cooperation in\u0000large-scale LLM Agent systems. MegaAgent leverages the autonomy of agents to\u0000dynamically generate agents based on task requirements, incorporating features\u0000such as automatically dividing tasks, systematic planning and monitoring of\u0000agent activities, and managing concurrent operations. In addition, MegaAgent is\u0000designed with a hierarchical structure and employs system-level parallelism to\u0000enhance performance and boost communication. We demonstrate the effectiveness\u0000of MegaAgent through Gobang game development, showing that it outperforms\u0000popular LLM-MA systems; and national policy simulation, demonstrating its high\u0000autonomy and potential to rapidly scale up to 590 agents while ensuring\u0000effective cooperation among them. Our results indicate that MegaAgent is the\u0000first autonomous large-scale LLM-MA system with no pre-defined SOPs, high\u0000effectiveness and scalability, paving the way for further research in this\u0000field. Our code is at https://anonymous.4open.science/r/MegaAgent-81F3.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthesis of Reward Machines for Multi-Agent Equilibrium Design (Full Version) 多代理平衡设计奖励机制的合成（完整版）

arXiv - CS - Multiagent Systems Pub Date : 2024-08-19 DOI: arxiv-2408.10074

Muhammad Najib, Giuseppe Perelli

{"title":"Synthesis of Reward Machines for Multi-Agent Equilibrium Design (Full Version)","authors":"Muhammad Najib, Giuseppe Perelli","doi":"arxiv-2408.10074","DOIUrl":"https://doi.org/arxiv-2408.10074","url":null,"abstract":"Mechanism design is a well-established game-theoretic paradigm for designing\u0000games to achieve desired outcomes. This paper addresses a closely related but\u0000distinct concept, equilibrium design. Unlike mechanism design, the designer's\u0000authority in equilibrium design is more constrained; she can only modify the\u0000incentive structures in a given game to achieve certain outcomes without the\u0000ability to create the game from scratch. We study the problem of equilibrium\u0000design using dynamic incentive structures, known as reward machines. We use\u0000weighted concurrent game structures for the game model, with goals (for the\u0000players and the designer) defined as mean-payoff objectives. We show how reward\u0000machines can be used to represent dynamic incentives that allocate rewards in a\u0000manner that optimises the designer's goal. We also introduce the main decision\u0000problem within our framework, the payoff improvement problem. This problem\u0000essentially asks whether there exists a dynamic incentive (represented by some\u0000reward machine) that can improve the designer's payoff by more than a given\u0000threshold value. We present two variants of the problem: strong and weak. We\u0000demonstrate that both can be solved in polynomial time using a Turing machine\u0000equipped with an NP oracle. Furthermore, we also establish that these variants\u0000are either NP-hard or coNP-hard. Finally, we show how to synthesise the\u0000corresponding reward machine if it exists.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Algorithmic Contract Design with Reinforcement Learning Agents 使用强化学习代理的算法合同设计

arXiv - CS - Multiagent Systems Pub Date : 2024-08-19 DOI: arxiv-2408.09686

David Molina Concha, Kyeonghyeon Park, Hyun-Rok Lee, Taesik Lee, Chi-Guhn Lee

{"title":"Algorithmic Contract Design with Reinforcement Learning Agents","authors":"David Molina Concha, Kyeonghyeon Park, Hyun-Rok Lee, Taesik Lee, Chi-Guhn Lee","doi":"arxiv-2408.09686","DOIUrl":"https://doi.org/arxiv-2408.09686","url":null,"abstract":"We introduce a novel problem setting for algorithmic contract design, named\u0000the principal-MARL contract design problem. This setting extends traditional\u0000contract design to account for dynamic and stochastic environments using Markov\u0000Games and Multi-Agent Reinforcement Learning. To tackle this problem, we\u0000propose a Multi-Objective Bayesian Optimization (MOBO) framework named\u0000Constrained Pareto Maximum Entropy Search (cPMES). Our approach integrates MOBO\u0000and MARL to explore the highly constrained contract design space, identifying\u0000promising incentive and recruitment decisions. cPMES transforms the\u0000principal-MARL contract design problem into an unconstrained multi-objective\u0000problem, leveraging the probability of feasibility as part of the objectives\u0000and ensuring promising designs predicted on the feasibility border are included\u0000in the Pareto front. By focusing the entropy prediction on designs within the\u0000Pareto set, cPMES mitigates the risk of the search strategy being overwhelmed\u0000by entropy from constraints. We demonstrate the effectiveness of cPMES through\u0000extensive benchmark studies in synthetic and simulated environments, showing\u0000its ability to find feasible contract designs that maximize the principal's\u0000objectives. Additionally, we provide theoretical support with a sub-linear\u0000regret bound concerning the number of iterations.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Auctioning Escape Permits for Multiple Correlated Pollutants Using CMRA 利用 CMRA 拍卖多种相关污染物的逃逸许可证

arXiv - CS - Multiagent Systems Pub Date : 2024-08-19 DOI: arxiv-2408.10148

Keshav Goyal, Sooraj Sathish, Shrisha Rao

引用次数: 0