Autonomous Agents and Multi-Agent Systems最新文献

Hypertension and total-order forward decomposition optimizations 高血压和全阶正分解优化

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-04-25 DOI: 10.1007/s10458-025-09705-9

Maurício Cecílio Magnaguagno, Felipe Meneguzzi, Lavindra de Silva

引用次数: 0

Improving execution concurrency in partial-order plans via block-substitution 通过块替换提高部分顺序计划的执行并发性

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-04-23 DOI: 10.1007/s10458-025-09706-8

Sabah Binte Noor, Fazlul Hasan Siddiqui

{"title":"Improving execution concurrency in partial-order plans via block-substitution","authors":"Sabah Binte Noor, Fazlul Hasan Siddiqui","doi":"10.1007/s10458-025-09706-8","DOIUrl":"10.1007/s10458-025-09706-8","url":null,"abstract":"<div><p>Partial-order plans in AI planning facilitate execution flexibility and several other tasks, such as plan reuse, modification, and decomposition, due to their less constrained nature. A Partial-Order Plan (POP) specifies partial-order over actions, providing the flexibility of executing unordered actions in different sequences. This flexibility can be further extended by enabling parallel execution of actions in the POP to reduce its overall execution time. While extensive studies exist on improving the flexibility of a POP by optimizing its action orderings through plan deordering and reordering, there has been limited focus on the flexibility of executing actions concurrently in a plan. Flexibility of executing actions concurrently, referred to as concurrency, in a POP can be achieved by incorporating action non-concurrency constraints, specifying which actions can not be executed in parallel. This work establishes the necessary and sufficient conditions for non-concurrency constraints between two actions or two subplans with respect to a planning task. We also introduce an algorithm to improve a plan’s concurrency by optimizing resource utilization through substitutions of the plan’s subplans with respect to the corresponding planning task. Our algorithm employs block deordering that eliminates orderings in a POP by encapsulating coherent actions in blocks, and then exploits blocks as candidate subplans for substitutions. Experiments over the benchmark problems from International Planning Competitions (IPC) exhibit considerable improvement in plan concurrency.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptation Procedure in misinformation games 错误信息游戏中的适应程序

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-03-21 DOI: 10.1007/s10458-025-09704-w

Konstantinos Varsos, Merkouris Papamichail, Giorgos Flouris, Marina Bitsaki

引用次数: 0

On fair and efficient solutions for budget apportionment 关于公平有效的预算分配办法

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-03-20 DOI: 10.1007/s10458-025-09694-9

Pierre Cardi, Laurent Gourvès, Julien Lesca

{"title":"On fair and efficient solutions for budget apportionment","authors":"Pierre Cardi, Laurent Gourvès, Julien Lesca","doi":"10.1007/s10458-025-09694-9","DOIUrl":"10.1007/s10458-025-09694-9","url":null,"abstract":"<div><p>This article deals with an apportionment problem involving <i>n</i> agents and a common budget <i>B</i>. Each agent submits some demands which are indivisible portions of the budget, and a central authority has to decide which demands to accept. The utility of an agent corresponds to the total amount of her accepted demands. In this context, it is desirable to be fair among the agents and efficient by not wasting the budget. An ideal solution would be to spend exactly <i>B</i>/<i>n</i> for every agent but this is rarely possible because of the indivisibility of the demands. Since combining fairness with efficiency is highly desirable but often impossible, we explore relaxed notions of fairness and efficiency, in order to determine if they go together. Our approach is also constructive because polynomial algorithms that build fair and efficient solutions are also given. The fairness criteria under consideration are the maximization of the minimum agent utility (max–min), proportionality, a customized notion of envy-freeness called jealousy-freeness, and the relaxations up to one or any demand of the previous two concepts. Efficiency in this work is either the maximization of the utilitarian social welfare or Pareto optimality. First we consider fairness and efficiency separately. The existence and computation of solutions that are either fair or efficient are studied. A complete picture of the relations that connect the fairness and efficiency concepts is provided. Second, we determine when fairness and efficiency can be combined for every possible instance. We prove that Pareto optimality is compatible with two notions of fairness, namely max–min and proportionality up to any demand. In contrast, none of the fairness concepts under consideration can be paired with the maximization of utilitarian social welfare. Therefore, we finally conduct a thorough analysis of the price of fairness which bounds the loss of efficiency caused by imposing fairness or one of its relaxations.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

“Provably fair” algorithms may perpetuate racial and gender bias: a study of salary dispute resolution “可证明公平”的算法可能使种族和性别偏见永久化：一项关于薪资纠纷解决的研究

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-03-12 DOI: 10.1007/s10458-025-09703-x

James Hale, Peter H. Kim, Jonathan Gratch

引用次数: 0

Investigating the impact of direct punishment on the emergence of cooperation in multi-agent reinforcement learning systems 研究直接惩罚对多智能体强化学习系统中合作出现的影响

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-03-11 DOI: 10.1007/s10458-025-09698-5

Nayana Dasgupta, Mirco Musolesi

{"title":"Investigating the impact of direct punishment on the emergence of cooperation in multi-agent reinforcement learning systems","authors":"Nayana Dasgupta, Mirco Musolesi","doi":"10.1007/s10458-025-09698-5","DOIUrl":"10.1007/s10458-025-09698-5","url":null,"abstract":"<div><p>Solving the problem of cooperation is fundamentally important for the creation and maintenance of functional societies. Problems of cooperation are omnipresent within human society, with examples ranging from navigating busy road junctions to negotiating treaties. As the use of AI becomes more pervasive throughout society, the need for socially intelligent agents capable of navigating these complex cooperative dilemmas is becoming increasingly evident. Direct punishment is a ubiquitous social mechanism that has been shown to foster the emergence of cooperation in both humans and non-humans. In the natural world, direct punishment is often strongly coupled with partner selection and reputation and used in conjunction with third-party punishment. The interactions between these mechanisms could potentially enhance the emergence of cooperation within populations. However, no previous work has evaluated the learning dynamics and outcomes emerging from multi-agent reinforcement learning populations that combine these mechanisms. This paper addresses this gap. It presents a comprehensive analysis and evaluation of the behaviors and learning dynamics associated with direct punishment, third-party punishment, partner selection, and reputation. Finally, we discuss the implications of using these mechanisms on the design of cooperative AI systems.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-025-09698-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emergent language: a survey and taxonomy 新兴语言：调查与分类

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-03-07 DOI: 10.1007/s10458-025-09691-y

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

{"title":"Emergent language: a survey and taxonomy","authors":"Jannik Peters, Constantin Waubert de Puiseau, Hasan Tercan, Arya Gopikrishnan, Gustavo Adolpho Lucas de Carvalho, Christian Bitter, Tobias Meisen","doi":"10.1007/s10458-025-09691-y","DOIUrl":"10.1007/s10458-025-09691-y","url":null,"abstract":"<div><p>The field of emergent language represents a novel area of research within the domain of artificial intelligence, particularly within the context of multi-agent reinforcement learning. Although the concept of studying language emergence is not new, early approaches were primarily concerned with explaining human language formation, with little consideration given to its potential utility for artificial agents. In contrast, studies based on reinforcement learning aim to develop communicative capabilities in agents that are comparable to or even superior to human language. Thus, they extend beyond the learned statistical representations that are common in natural language processing research. This gives rise to a number of fundamental questions, from the prerequisites for language emergence to the criteria for measuring its success. This paper addresses these questions by providing a comprehensive review of relevant scientific publications on emergent language in artificial intelligence. Its objective is to serve as a reference for researchers interested in or proficient in the field. Consequently, the main contributions are the definition and overview of the prevailing terminology, the analysis of existing evaluation methods and metrics, and the description of the identified research gaps.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-025-09691-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Relationship design for socially-aware behavior in static games 静态游戏中社交意识行为的关系设计

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-03-05 DOI: 10.1007/s10458-025-09699-4

Shenghui Chen, Yigit E. Bayiz, David Fridovich-Keil, Ufuk Topcu

{"title":"Relationship design for socially-aware behavior in static games","authors":"Shenghui Chen, Yigit E. Bayiz, David Fridovich-Keil, Ufuk Topcu","doi":"10.1007/s10458-025-09699-4","DOIUrl":"10.1007/s10458-025-09699-4","url":null,"abstract":"<div><p>Autonomous agents can adopt socially-aware behaviors to reduce social costs, mimicking the way animals interact in nature and humans in society. We present a new approach to model socially-aware decision-making that includes two key elements: bounded rationality and inter-agent relationships. We capture the inter-agent relationships by introducing a novel model called a relationship game and encode agents’ bounded rationality using quantal response equilibria. For each relationship game, we define a social cost function and formulate a mechanism design problem to optimize weights for relationships that minimize social cost at the equilibrium. We address the multiplicity of equilibria by presenting the problem in two forms: Min-Max and Min-Min, aimed respectively at minimization of the highest and lowest social costs in the equilibria. We compute the quantal response equilibrium by solving a least-squares problem defined with its Karush-Kuhn-Tucker conditions, and propose two projected gradient descent algorithms to solve the mechanism design problems. Numerical results, including two-lane congestion and congestion with an ambulance, confirm that these algorithms consistently reach the equilibrium with the intended social costs.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal matchings with one-sided preferences: fixed and cost-based quotas 具有单边偏好的最优匹配：固定和基于成本的配额

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-03-05 DOI: 10.1007/s10458-025-09693-w

K. A. Santhini, Govind S. Sankar, Meghana Nasre

{"title":"Optimal matchings with one-sided preferences: fixed and cost-based quotas","authors":"K. A. Santhini, Govind S. Sankar, Meghana Nasre","doi":"10.1007/s10458-025-09693-w","DOIUrl":"10.1007/s10458-025-09693-w","url":null,"abstract":"<div><p>We consider the well-studied many-to-one bipartite matching problem of assigning applicants <span>({varvec{mathcal {A}}})</span> to posts <span>({varvec{mathcal {P}}})</span> where applicants rank posts in the order of preference. This setting models many important real-world allocation problems like assigning students to courses, applicants to jobs, amongst many others. In such scenarios, it is natural to ask for an allocation that satisfies guarantees of the form “match at least 80% of applicants to one of their top three choices” or “it is unacceptable to leave more than 10% of applicants unassigned”. The well-studied notions of rank-maximality and fairness fail to capture such requirements due to their property of optimizing extreme ends of the <i>signature</i> of a matching. We, therefore, propose a novel optimality criterion, which we call the “weak dominance ” of ranks.</p><p>We investigate the computational complexity of the new notion of optimality in the setting where posts have associated <i>fixed</i> quotas. We prove that under the fixed quota setting, the problem turns out to be NP-hard under natural restrictions. We provide randomized algorithms in the fixed quota setting when the number of ranks is constant. We also study the problem under a <i>cost-based quota</i> setting and show that a matching that weakly dominates the input signature and has minimum total cost can be computed efficiently. Apart from circumventing the hardness, the cost-based quota setting is motivated by real-world applications like course allocation or school choice where the capacities or quotas need not be rigid. We also show that when the objective is to minimize the maximum cost, the problem under the cost-based quota setting turns out to be NP-hard. To complement the hardness, we provide a randomized algorithm when the number of ranks is constant. We also provide an approximation algorithm which is an asymptotic faster alternative to the randomized algorithm.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Double mixing networks based monotonic value function decomposition algorithm for swarm intelligence in UAVs 基于双混合网络的无人机群智能单调值函数分解算法

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-03-05 DOI: 10.1007/s10458-025-09700-0

Pingping Qu, Chenglong He, Xiaotong Wu, Ershen Wang, Song Xu, Huan Liu, Xinhui Sun

{"title":"Double mixing networks based monotonic value function decomposition algorithm for swarm intelligence in UAVs","authors":"Pingping Qu, Chenglong He, Xiaotong Wu, Ershen Wang, Song Xu, Huan Liu, Xinhui Sun","doi":"10.1007/s10458-025-09700-0","DOIUrl":"10.1007/s10458-025-09700-0","url":null,"abstract":"<div><p>In multi-agent systems, particularly when facing challenges of partial observability, reinforcement learning demonstrates significant autonomous decision-making capabilities. Aiming at addressing resource allocation and collaboration issues in drone swarms operating in dynamic and unknown environments, we propose a novel deep reinforcement learning algorithm, DQMIX. We employ a framework of centralized training with decentralized execution and incorporate a partially observable Markov game model to describe the complex game environment of drone swarms. The core innovation of the DQMIX algorithm lies in its dual-mixing network structure and soft-switching mechanism. Two independent mixing networks handle local Q-values and synthesize them into a global Q-value. This structure enhances decision accuracy and system adaptability under different scenarios and data conditions. The soft-switching module allows the system to transition smoothly between the two networks, selecting the output of the network with smaller TD-errors to enhance decision stability and coherence. Simultaneously, we introduce Hindsight Experience Replay to learn from failed experiences. Experimental results using JSBSim demonstrate that DQMIX provides an effective solution for drone swarm game problems, especially in resource allocation and adversarial environments.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0