arXiv - CS - Multiagent Systems最新文献_第7页

Value-Enriched Population Synthesis: Integrating a Motivational Layer 价值丰富的人口合成：整合动机层

arXiv - CS - Multiagent Systems Pub Date : 2024-08-18 DOI: arxiv-2408.09407

Alba Aguilera, Miquel Albertí, Nardine Osman, Georgina Curto

{"title":"Value-Enriched Population Synthesis: Integrating a Motivational Layer","authors":"Alba Aguilera, Miquel Albertí, Nardine Osman, Georgina Curto","doi":"arxiv-2408.09407","DOIUrl":"https://doi.org/arxiv-2408.09407","url":null,"abstract":"In recent years, computational improvements have allowed for more nuanced,\u0000data-driven and geographically explicit agent-based simulations. So far,\u0000simulations have struggled to adequately represent the attributes that motivate\u0000the actions of the agents. In fact, existing population synthesis frameworks\u0000generate agent profiles limited to socio-demographic attributes. In this paper,\u0000we introduce a novel value-enriched population synthesis framework that\u0000integrates a motivational layer with the traditional individual and household\u0000socio-demographic layers. Our research highlights the significance of extending\u0000the profile of agents in synthetic populations by incorporating data on values,\u0000ideologies, opinions and vital priorities, which motivate the agents'\u0000behaviour. This motivational layer can help us develop a more nuanced\u0000decision-making mechanism for the agents in social simulation settings. Our\u0000methodology integrates microdata and macrodata within different Bayesian\u0000network structures. This contribution allows to generate synthetic populations\u0000with integrated value systems that preserve the inherent socio-demographic\u0000distributions of the real population in any specific region.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190421","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

Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning 超越局部观点：利用扩散模型进行全局状态推断，实现多代理合作强化学习

arXiv - CS - Multiagent Systems Pub Date : 2024-08-18 DOI: arxiv-2408.09501

Zhiwei Xu, Hangyu Mao, Nianmin Zhang, Xin Xin, Pengjie Ren, Dapeng Li, Bin Zhang, Guoliang Fan, Zhumin Chen, Changwei Wang, Jiangjin Yin

{"title":"Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning","authors":"Zhiwei Xu, Hangyu Mao, Nianmin Zhang, Xin Xin, Pengjie Ren, Dapeng Li, Bin Zhang, Guoliang Fan, Zhumin Chen, Changwei Wang, Jiangjin Yin","doi":"arxiv-2408.09501","DOIUrl":"https://doi.org/arxiv-2408.09501","url":null,"abstract":"In partially observable multi-agent systems, agents typically only have\u0000access to local observations. This severely hinders their ability to make\u0000precise decisions, particularly during decentralized execution. To alleviate\u0000this problem and inspired by image outpainting, we propose State Inference with\u0000Diffusion Models (SIDIFF), which uses diffusion models to reconstruct the\u0000original global state based solely on local observations. SIDIFF consists of a\u0000state generator and a state extractor, which allow agents to choose suitable\u0000actions by considering both the reconstructed global state and local\u0000observations. In addition, SIDIFF can be effortlessly incorporated into current\u0000multi-agent reinforcement learning algorithms to improve their performance.\u0000Finally, we evaluated SIDIFF on different experimental platforms, including\u0000Multi-Agent Battle City (MABC), a novel and flexible multi-agent reinforcement\u0000learning environment we developed. SIDIFF achieved desirable results and\u0000outperformed other popular algorithms.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"113 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190420","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

Joint-perturbation simultaneous pseudo-gradient 联合扰动同步伪梯度

arXiv - CS - Multiagent Systems Pub Date : 2024-08-17 DOI: arxiv-2408.09306

Carlos Martin, Tuomas Sandholm

{"title":"Joint-perturbation simultaneous pseudo-gradient","authors":"Carlos Martin, Tuomas Sandholm","doi":"arxiv-2408.09306","DOIUrl":"https://doi.org/arxiv-2408.09306","url":null,"abstract":"We study the problem of computing an approximate Nash equilibrium of a game\u0000whose strategy space is continuous without access to gradients of the utility\u0000function. Such games arise, for example, when players' strategies are\u0000represented by the parameters of a neural network. Lack of access to gradients\u0000is common in reinforcement learning settings, where the environment is treated\u0000as a black box, as well as equilibrium finding in mechanisms such as auctions,\u0000where the mechanism's payoffs are discontinuous in the players' actions. To\u0000tackle this problem, we turn to zeroth-order optimization techniques that\u0000combine pseudo-gradients with equilibrium-finding dynamics. Specifically, we\u0000introduce a new technique that requires a number of utility function\u0000evaluations per iteration that is constant rather than linear in the number of\u0000players. It achieves this by performing a single joint perturbation on all\u0000players' strategies, rather than perturbing each one individually. This yields\u0000a dramatic improvement for many-player games, especially when the utility\u0000function is expensive to compute in terms of wall time, memory, money, or other\u0000resources. We evaluate our approach on various games, including auctions, which\u0000have important real-world applications. Our approach yields a significant\u0000reduction in the run time required to reach an approximate Nash equilibrium.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190424","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

Multilevel Graph Reinforcement Learning for Consistent Cognitive Decision-making in Heterogeneous Mixed Autonomy 多层次图强化学习促进异构混合自主中的一致认知决策

arXiv - CS - Multiagent Systems Pub Date : 2024-08-16 DOI: arxiv-2408.08516

Xin Gao, Zhaoyang Ma, Xueyuan Li, Xiaoqiang Meng, Zirui Li

{"title":"Multilevel Graph Reinforcement Learning for Consistent Cognitive Decision-making in Heterogeneous Mixed Autonomy","authors":"Xin Gao, Zhaoyang Ma, Xueyuan Li, Xiaoqiang Meng, Zirui Li","doi":"arxiv-2408.08516","DOIUrl":"https://doi.org/arxiv-2408.08516","url":null,"abstract":"In the realm of heterogeneous mixed autonomy, vehicles experience dynamic\u0000spatial correlations and nonlinear temporal interactions in a complex,\u0000non-Euclidean space. These complexities pose significant challenges to\u0000traditional decision-making frameworks. Addressing this, we propose a\u0000hierarchical reinforcement learning framework integrated with multilevel graph\u0000representations, which effectively comprehends and models the spatiotemporal\u0000interactions among vehicles navigating through uncertain traffic conditions\u0000with varying decision-making systems. Rooted in multilevel graph representation\u0000theory, our approach encapsulates spatiotemporal relationships inherent in\u0000non-Euclidean spaces. A weighted graph represents spatiotemporal features\u0000between nodes, addressing the degree imbalance inherent in dynamic graphs. We\u0000integrate asynchronous parallel hierarchical reinforcement learning with a\u0000multilevel graph representation and a multi-head attention mechanism, which\u0000enables connected autonomous vehicles (CAVs) to exhibit capabilities akin to\u0000human cognition, facilitating consistent decision-making across various\u0000critical dimensions. The proposed decision-making strategy is validated in\u0000challenging environments characterized by high density, randomness, and\u0000dynamism on highway roads. We assess the performance of our framework through\u0000ablation studies, comparative analyses, and spatiotemporal trajectory\u0000evaluations. This study presents a quantitative analysis of decision-making\u0000mechanisms mirroring human cognitive functions in the realm of heterogeneous\u0000mixed autonomy, promoting the development of multi-dimensional decision-making\u0000strategies and a sophisticated distribution of attentional resources.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190426","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

ASGM-KG: Unveiling Alluvial Gold Mining Through Knowledge Graphs ASGM-KG：通过知识图谱揭开砂金开采的神秘面纱

arXiv - CS - Multiagent Systems Pub Date : 2024-08-16 DOI: arxiv-2408.08972

Debashis Gupta, Aditi Golder, Luis Fernendez, Miles Silman, Greg Lersen, Fan Yang, Bob Plemmons, Sarra Alqahtani, Paul Victor Pauca

{"title":"ASGM-KG: Unveiling Alluvial Gold Mining Through Knowledge Graphs","authors":"Debashis Gupta, Aditi Golder, Luis Fernendez, Miles Silman, Greg Lersen, Fan Yang, Bob Plemmons, Sarra Alqahtani, Paul Victor Pauca","doi":"arxiv-2408.08972","DOIUrl":"https://doi.org/arxiv-2408.08972","url":null,"abstract":"Artisanal and Small-Scale Gold Mining (ASGM) is a low-cost yet highly\u0000destructive mining practice, leading to environmental disasters across the\u0000world's tropical watersheds. The topic of ASGM spans multiple domains of\u0000research and information, including natural and social systems, and knowledge\u0000is often atomized across a diversity of media and documents. We therefore\u0000introduce a knowledge graph (ASGM-KG) that consolidates and provides crucial\u0000information about ASGM practices and their environmental effects. The current\u0000version of ASGM-KG consists of 1,899 triples extracted using a large language\u0000model (LLM) from documents and reports published by both non-governmental and\u0000governmental organizations. These documents were carefully selected by a group\u0000of tropical ecologists with expertise in ASGM. This knowledge graph was\u0000validated using two methods. First, a small team of ASGM experts reviewed and\u0000labeled triples as factual or non-factual. Second, we devised and applied an\u0000automated factual reduction framework that relies on a search engine and an LLM\u0000for labeling triples. Our framework performs as well as five baselines on a\u0000publicly available knowledge graph and achieves over 90 accuracy on our ASGM-KG\u0000validated by domain experts. ASGM-KG demonstrates an advancement in knowledge\u0000aggregation and representation for complex, interdisciplinary environmental\u0000crises such as ASGM.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190425","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

AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation AgentSimulator：基于代理的数据驱动型业务流程模拟方法

arXiv - CS - Multiagent Systems Pub Date : 2024-08-16 DOI: arxiv-2408.08571

Lukas Kirchdorfer, Robert Blümel, Timotheus Kampik, Han van der Aa, Heiner Stuckenschmidt

{"title":"AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation","authors":"Lukas Kirchdorfer, Robert Blümel, Timotheus Kampik, Han van der Aa, Heiner Stuckenschmidt","doi":"arxiv-2408.08571","DOIUrl":"https://doi.org/arxiv-2408.08571","url":null,"abstract":"Business process simulation (BPS) is a versatile technique for estimating\u0000process performance across various scenarios. Traditionally, BPS approaches\u0000employ a control-flow-first perspective by enriching a process model with\u0000simulation parameters. Although such approaches can mimic the behavior of\u0000centrally orchestrated processes, such as those supported by workflow systems,\u0000current control-flow-first approaches cannot faithfully capture the dynamics of\u0000real-world processes that involve distinct resource behavior and decentralized\u0000decision-making. Recognizing this issue, this paper introduces AgentSimulator,\u0000a resource-first BPS approach that discovers a multi-agent system from an event\u0000log, modeling distinct resource behaviors and interaction patterns to simulate\u0000the underlying process. Our experiments show that AgentSimulator achieves\u0000state-of-the-art simulation accuracy with significantly lower computation times\u0000than existing approaches while providing high interpretability and adaptability\u0000to different types of process-execution scenarios.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190427","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

Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players 马尔可夫势能博弈的独立策略镜像后裔：扩展到大量玩家

arXiv - CS - Multiagent Systems Pub Date : 2024-08-15 DOI: arxiv-2408.08075

Pragnya Alatur, Anas Barakat, Niao He

{"title":"Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players","authors":"Pragnya Alatur, Anas Barakat, Niao He","doi":"arxiv-2408.08075","DOIUrl":"https://doi.org/arxiv-2408.08075","url":null,"abstract":"Markov Potential Games (MPGs) form an important sub-class of Markov games,\u0000which are a common framework to model multi-agent reinforcement learning\u0000problems. In particular, MPGs include as a special case the identical-interest\u0000setting where all the agents share the same reward function. Scaling the\u0000performance of Nash equilibrium learning algorithms to a large number of agents\u0000is crucial for multi-agent systems. To address this important challenge, we\u0000focus on the independent learning setting where agents can only have access to\u0000their local information to update their own policy. In prior work on MPGs, the\u0000iteration complexity for obtaining $epsilon$-Nash regret scales linearly with\u0000the number of agents $N$. In this work, we investigate the iteration complexity\u0000of an independent policy mirror descent (PMD) algorithm for MPGs. We show that\u0000PMD with KL regularization, also known as natural policy gradient, enjoys a\u0000better $sqrt{N}$ dependence on the number of agents, improving over PMD with\u0000Euclidean regularization and prior work. Furthermore, the iteration complexity\u0000is also independent of the sizes of the agents' action spaces.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190429","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

EmBARDiment: an Embodied AI Agent for Productivity in XR EmBARDiment：用于提高 XR 生产率的嵌入式人工智能代理

arXiv - CS - Multiagent Systems Pub Date : 2024-08-15 DOI: arxiv-2408.08158

Riccardo Bovo, Steven Abreu, Karan Ahuja, Eric J Gonzalez, Li-Te Cheng, Mar Gonzalez-Franco

引用次数: 0

Bridging Training and Execution via Dynamic Directed Graph-Based Communication in Cooperative Multi-Agent Systems 在多代理合作系统中通过基于动态有向图的通信架起训练与执行的桥梁

arXiv - CS - Multiagent Systems Pub Date : 2024-08-14 DOI: arxiv-2408.07397

Zhuohui Zhang, Bin He, Bin Cheng, Gang Li

引用次数: 0

A Nested Graph Reinforcement Learning-based Decision-making Strategy for Eco-platooning 基于嵌套图强化学习的生态排队决策策略

arXiv - CS - Multiagent Systems Pub Date : 2024-08-14 DOI: arxiv-2408.07578

Xin Gao, Xueyuan Li, Hao Liu, Ao Li, Zhaoyang Ma, Zirui Li

{"title":"A Nested Graph Reinforcement Learning-based Decision-making Strategy for Eco-platooning","authors":"Xin Gao, Xueyuan Li, Hao Liu, Ao Li, Zhaoyang Ma, Zirui Li","doi":"arxiv-2408.07578","DOIUrl":"https://doi.org/arxiv-2408.07578","url":null,"abstract":"Platooning technology is renowned for its precise vehicle control, traffic\u0000flow optimization, and energy efficiency enhancement. However, in large-scale\u0000mixed platoons, vehicle heterogeneity and unpredictable traffic conditions lead\u0000to virtual bottlenecks. These bottlenecks result in reduced traffic throughput\u0000and increased energy consumption within the platoon. To address these\u0000challenges, we introduce a decision-making strategy based on nested graph\u0000reinforcement learning. This strategy improves collaborative decision-making,\u0000ensuring energy efficiency and alleviating congestion. We propose a theory of\u0000nested traffic graph representation that maps dynamic interactions between\u0000vehicles and platoons in non-Euclidean spaces. By incorporating spatio-temporal\u0000weighted graph into a multi-head attention mechanism, we further enhance the\u0000model's capacity to process both local and global data. Additionally, we have\u0000developed a nested graph reinforcement learning framework to enhance the\u0000self-iterative learning capabilities of platooning. Using the I-24 dataset, we\u0000designed and conducted comparative algorithm experiments, generalizability\u0000testing, and permeability ablation experiments, thereby validating the proposed\u0000strategy's effectiveness. Compared to the baseline, our strategy increases\u0000throughput by 10% and decreases energy use by 9%. Specifically, increasing the\u0000penetration rate of CAVs significantly enhances traffic throughput, though it\u0000also increases energy consumption.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190431","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