arXiv - CS - Multiagent Systems最新文献_第3页

Responsible Blockchain: STEADI Principles and the Actor-Network Theory-based Development Methodology (ANT-RDM) 负责任的区块链：STEADI 原则和基于行为网络理论的开发方法（ANT-RDM）

arXiv - CS - Multiagent Systems Pub Date : 2024-09-10 DOI: arxiv-2409.06179

Yibai Li, Ahmed Gomaa, Xiaobing Li

引用次数: 0

Foragax: An Agent Based Modelling framework based on JAX Foragax：基于 JAX 的代理建模框架

arXiv - CS - Multiagent Systems Pub Date : 2024-09-10 DOI: arxiv-2409.06345

Siddharth Chaturvedi, Ahmed El-Gazzar, Marcel van Gerven

{"title":"Foragax: An Agent Based Modelling framework based on JAX","authors":"Siddharth Chaturvedi, Ahmed El-Gazzar, Marcel van Gerven","doi":"arxiv-2409.06345","DOIUrl":"https://doi.org/arxiv-2409.06345","url":null,"abstract":"Foraging for resources is a ubiquitous activity conducted by living organisms\u0000in a shared environment to maintain their homeostasis. Modelling multi-agent\u0000foraging in-silico allows us to study both individual and collective emergent\u0000behaviour in a tractable manner. Agent-based modelling has proven to be\u0000effective in simulating such tasks, though scaling the simulations to\u0000accommodate large numbers of agents with complex dynamics remains challenging.\u0000In this work, we present Foragax, a general-purpose, scalable,\u0000hardware-accelerated, multi-agent foraging toolkit. Leveraging the JAX library,\u0000our toolkit can simulate thousands of agents foraging in a common environment,\u0000in an end-to-end vectorized and differentiable manner. The toolkit provides\u0000agent-based modelling tools to model various foraging tasks, including options\u0000to design custom spatial and temporal agent dynamics, control policies, sensor\u0000models, and boundary conditions. Further, the number of agents during such\u0000simulations can be increased or decreased based on custom rules. The toolkit\u0000can also be used to potentially model more general multi-agent scenarios.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"113 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190500","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

Enhancing the Performance of Multi-Vehicle Navigation in Unstructured Environments using Hard Sample Mining 利用硬样本挖掘提高非结构化环境中的多车导航性能

arXiv - CS - Multiagent Systems Pub Date : 2024-09-08 DOI: arxiv-2409.05119

Yining Ma, Ang Li, Qadeer Khan, Daniel Cremers

{"title":"Enhancing the Performance of Multi-Vehicle Navigation in Unstructured Environments using Hard Sample Mining","authors":"Yining Ma, Ang Li, Qadeer Khan, Daniel Cremers","doi":"arxiv-2409.05119","DOIUrl":"https://doi.org/arxiv-2409.05119","url":null,"abstract":"Contemporary research in autonomous driving has demonstrated tremendous\u0000potential in emulating the traits of human driving. However, they primarily\u0000cater to areas with well built road infrastructure and appropriate traffic\u0000management systems. Therefore, in the absence of traffic signals or in\u0000unstructured environments, these self-driving algorithms are expected to fail.\u0000This paper proposes a strategy for autonomously navigating multiple vehicles in\u0000close proximity to their desired destinations without traffic rules in\u0000unstructured environments. Graphical Neural Networks (GNNs) have demonstrated good utility for this task\u0000of multi-vehicle control. Among the different alternatives of training GNNs,\u0000supervised methods have proven to be most data-efficient, albeit require ground\u0000truth labels. However, these labels may not always be available, particularly\u0000in unstructured environments without traffic regulations. Therefore, a tedious\u0000optimization process may be required to determine them while ensuring that the\u0000vehicles reach their desired destination and do not collide with each other or\u0000any obstacles. Therefore, in order to expedite the training process, it is\u0000essential to reduce the optimization time and select only those samples for\u0000labeling that add most value to the training. In this paper, we propose a warm\u0000start method that first uses a pre-trained model trained on a simpler subset of\u0000data. Inference is then done on more complicated scenarios, to determine the\u0000hard samples wherein the model faces the greatest predicament. This is measured\u0000by the difficulty vehicles encounter in reaching their desired destination\u0000without collision. Experimental results demonstrate that mining for hard\u0000samples in this manner reduces the requirement for supervised training data by\u000010 fold. Videos and code can be found here:\u0000url{https://yininghase.github.io/multiagent-collision-mining/}.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190502","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

Towards Multi-agent Policy-based Directed Hypergraph Learning for Traffic Signal Control 为交通信号控制实现基于多代理策略的有向超图学习

arXiv - CS - Multiagent Systems Pub Date : 2024-09-08 DOI: arxiv-2409.05037

Kang Wang, Zhishu Shen, Zhenwei Wang, Tiehua Zhang

引用次数: 0

Adaptation Procedure in Misinformation Games 误导游戏中的适应程序

arXiv - CS - Multiagent Systems Pub Date : 2024-09-07 DOI: arxiv-2409.04854

Konstantinos Varsos, Merkouris Papamichail, Giorgos Flouris, Marina Bitsaki

引用次数: 0

PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization PARCO：学习并行自回归政策，实现高效的多代理组合优化

arXiv - CS - Multiagent Systems Pub Date : 2024-09-05 DOI: arxiv-2409.03811

Federico Berto, Chuanbo Hua, Laurin Luttmann, Jiwoo Son, Junyoung Park, Kyuree Ahn, Changhyun Kwon, Lin Xie, Jinkyoo Park

{"title":"PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization","authors":"Federico Berto, Chuanbo Hua, Laurin Luttmann, Jiwoo Son, Junyoung Park, Kyuree Ahn, Changhyun Kwon, Lin Xie, Jinkyoo Park","doi":"arxiv-2409.03811","DOIUrl":"https://doi.org/arxiv-2409.03811","url":null,"abstract":"Multi-agent combinatorial optimization problems such as routing and\u0000scheduling have great practical relevance but present challenges due to their\u0000NP-hard combinatorial nature, hard constraints on the number of possible\u0000agents, and hard-to-optimize objective functions. This paper introduces PARCO\u0000(Parallel AutoRegressive Combinatorial Optimization), a novel approach that\u0000learns fast surrogate solvers for multi-agent combinatorial problems with\u0000reinforcement learning by employing parallel autoregressive decoding. We\u0000propose a model with a Multiple Pointer Mechanism to efficiently decode\u0000multiple decisions simultaneously by different agents, enhanced by a\u0000Priority-based Conflict Handling scheme. Moreover, we design specialized\u0000Communication Layers that enable effective agent collaboration, thus enriching\u0000decision-making. We evaluate PARCO in representative multi-agent combinatorial\u0000problems in routing and scheduling and demonstrate that our learned solvers\u0000offer competitive results against both classical and neural baselines in terms\u0000of both solution quality and speed. We make our code openly available at\u0000https://github.com/ai4co/parco.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190505","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

A Survey on Emergent Language 新兴语言调查

arXiv - CS - Multiagent Systems Pub Date : 2024-09-04 DOI: arxiv-2409.02645

Jannik Peters, Constantin Waubert de Puiseau, Hasan Tercan, Arya Gopikrishnan, Gustavo Adolpho Lucas De Carvalho, Christian Bitter, Tobias Meisen

{"title":"A Survey on Emergent Language","authors":"Jannik Peters, Constantin Waubert de Puiseau, Hasan Tercan, Arya Gopikrishnan, Gustavo Adolpho Lucas De Carvalho, Christian Bitter, Tobias Meisen","doi":"arxiv-2409.02645","DOIUrl":"https://doi.org/arxiv-2409.02645","url":null,"abstract":"The field of emergent language represents a novel area of research within the\u0000domain of artificial intelligence, particularly within the context of\u0000multi-agent reinforcement learning. Although the concept of studying language\u0000emergence is not new, early approaches were primarily concerned with explaining\u0000human language formation, with little consideration given to its potential\u0000utility for artificial agents. In contrast, studies based on reinforcement\u0000learning aim to develop communicative capabilities in agents that are\u0000comparable to or even superior to human language. Thus, they extend beyond the\u0000learned statistical representations that are common in natural language\u0000processing research. This gives rise to a number of fundamental questions, from\u0000the prerequisites for language emergence to the criteria for measuring its\u0000success. This paper addresses these questions by providing a comprehensive\u0000review of 181 scientific publications on emergent language in artificial\u0000intelligence. Its objective is to serve as a reference for researchers\u0000interested in or proficient in the field. Consequently, the main contributions\u0000are the definition and overview of the prevailing terminology, the analysis of\u0000existing evaluation methods and metrics, and the description of the identified\u0000research gaps.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190508","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

An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning 多代理合作强化学习中分散执行的集中训练简介

arXiv - CS - Multiagent Systems Pub Date : 2024-09-04 DOI: arxiv-2409.03052

Christopher Amato

{"title":"An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning","authors":"Christopher Amato","doi":"arxiv-2409.03052","DOIUrl":"https://doi.org/arxiv-2409.03052","url":null,"abstract":"Multi-agent reinforcement learning (MARL) has exploded in popularity in\u0000recent years. Many approaches have been developed but they can be divided into\u0000three main types: centralized training and execution (CTE), centralized\u0000training for decentralized execution (CTDE), and Decentralized training and\u0000execution (DTE). CTDE methods are the most common as they can use centralized information\u0000during training but execute in a decentralized manner -- using only information\u0000available to that agent during execution. CTDE is the only paradigm that\u0000requires a separate training phase where any available information (e.g., other\u0000agent policies, underlying states) can be used. As a result, they can be more\u0000scalable than CTE methods, do not require communication during execution, and\u0000can often perform well. CTDE fits most naturally with the cooperative case, but\u0000can be potentially applied in competitive or mixed settings depending on what\u0000information is assumed to be observed. This text is an introduction to CTDE in cooperative MARL. It is meant to\u0000explain the setting, basic concepts, and common methods. It does not cover all\u0000work in CTDE MARL as the subarea is quite extensive. I have included work that\u0000I believe is important for understanding the main concepts in the subarea and\u0000apologize to those that I have omitted.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190507","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

Context-Aware Agent-based Model for Smart Long Distance Transport System 基于情境感知的智能长途运输系统代理模型

arXiv - CS - Multiagent Systems Pub Date : 2024-09-04 DOI: arxiv-2409.02434

Muhammad Raees, Afzal Ahmed

引用次数: 0

From Grounding to Planning: Benchmarking Bottlenecks in Web Agents 从接地到规划：网络代理瓶颈的基准测试

arXiv - CS - Multiagent Systems Pub Date : 2024-09-03 DOI: arxiv-2409.01927

Segev Shlomov, Ben wiesel, Aviad Sela, Ido Levy, Liane Galanti, Roy Abitbol

{"title":"From Grounding to Planning: Benchmarking Bottlenecks in Web Agents","authors":"Segev Shlomov, Ben wiesel, Aviad Sela, Ido Levy, Liane Galanti, Roy Abitbol","doi":"arxiv-2409.01927","DOIUrl":"https://doi.org/arxiv-2409.01927","url":null,"abstract":"General web-based agents are increasingly essential for interacting with\u0000complex web environments, yet their performance in real-world web applications\u0000remains poor, yielding extremely low accuracy even with state-of-the-art\u0000frontier models. We observe that these agents can be decomposed into two\u0000primary components: Planning and Grounding. Yet, most existing research treats\u0000these agents as black boxes, focusing on end-to-end evaluations which hinder\u0000meaningful improvements. We sharpen the distinction between the planning and\u0000grounding components and conduct a novel analysis by refining experiments on\u0000the Mind2Web dataset. Our work proposes a new benchmark for each of the\u0000components separately, identifying the bottlenecks and pain points that limit\u0000agent performance. Contrary to prevalent assumptions, our findings suggest that\u0000grounding is not a significant bottleneck and can be effectively addressed with\u0000current techniques. Instead, the primary challenge lies in the planning\u0000component, which is the main source of performance degradation. Through this\u0000analysis, we offer new insights and demonstrate practical suggestions for\u0000improving the capabilities of web agents, paving the way for more reliable\u0000agents.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190511","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